Puneet Singhal

AI Engineer & Solutions Architect

AI DeveloperVibe CoderCloud & DevOpsBackend Architect

AI Engineer · AI Solutions Architect · Vibe Coder

15+ years building AI-powered products — from LLM pipelines and multi-agent systems to cloud infrastructure automation and enterprise backends. I help businesses ship production-grade AI solutions faster as an AI Developer, AI Solutions Architect, and Vibe Coder.

Top Rated on Upwork
Puneet Singhal - Senior AI Engineer and Full-Stack Developer specializing in AI, Machine Learning, and LLM integration

Interested in my services or have an employment offer?

puneetsinghal.11@gmail.com

About

I'm a Senior AI Engineer, AI Solutions Architect, and Vibe Coder passionate about building AI-powered products that solve real business problems. I specialize in designing and shipping agentic AI systems, LLM-powered applications, cloud infrastructure automation, and enterprise backends — end-to-end, fast, and at scale.

🧠

AI Engineer & LLM Developer

Building production-grade AI applications with OpenAI GPT, Anthropic Claude, Google Gemini, and AWS Bedrock. Specialized in agentic AI systems, multi-agent orchestration (LangGraph), RAG pipelines, conversational AI, and LLM fine-tuning.

Vibe Coder & AI Solutions Architect

Designing and shipping AI-first products end-to-end — from idea to production — using the latest LLMs, cloud services, and modern toolchains. A Vibe Coder who combines deep AI expertise with rapid engineering to deliver AI solutions that actually work at scale.

☁️

Cloud, DevOps & Backend Architect

Expert in AWS, Oracle Cloud (OCI), Terraform, Docker, and Kubernetes. Designing scalable microservices, event-driven systems with Apache Kafka, CI/CD pipelines, and cloud infrastructure automation with Infrastructure as Code.

🗄️

Data Pipelines & Vector Search

Designing robust ETL pipelines, vector databases (FAISS, Pinecone, ChromaDB), and semantic search systems. Expert in AI-powered document processing, OCR automation, and data engineering with PostgreSQL, MongoDB, and DynamoDB.

Professional Highlights

14+

Years Experience

50+

Projects Delivered

10+

AI/LLM Projects

40+

Technologies

Core Competencies

15+ years of expertise across AI engineering, LLM development, cloud architecture, DevOps, and enterprise backend systems — as an AI Developer, AI Solutions Architect, and Vibe Coder

Languages & Frameworks

Overall Proficiency95%
Java95%
Spring Boot95%
Python95%
FastAPI95%
Kotlin80%
Click to expand

Databases

Overall Proficiency95%
PostgreSQL95%
MySQL95%
MongoDB80%
DynamoDB80%
Redis80%
Click to expand

Message Streaming

Overall Proficiency95%
Apache Kafka95%
Event-Driven Architecture95%
AWS SQS90%
AWS SNS90%
RabbitMQ80%
Click to expand

Cloud & DevOps

Overall Proficiency95%
AWS Services95%
Docker95%
Kubernetes95%
CI/CD95%
Jenkins80%
Click to expand

AI, LLM & Vibe Coding

Overall Proficiency92%
LangGraph / Agentic AI92%
LangChain92%
OpenAI APIs90%
Anthropic Claude90%
Vibe Coding95%
Click to expand

Monitoring & Logging

Overall Proficiency80%
Prometheus80%
Grafana80%
ELK Stack80%
Splunk85%
CloudWatch85%
Click to expand

Soft Skills

Overall Proficiency95%
Communication95%
Problem-Solving95%
Leadership95%
Agile/Scrum92%
Team Collaboration95%
Click to expand

Industry Experience

Overall Proficiency92%
FinTech90%
Healthcare95%
Legal Tech88%
Employee Benefits95%
E-commerce85%
Click to expand
14+ Years of Professional Experience

Professional Journey

Over the past 15+ years I’ve evolved from building Java microservices to architecting agentic AI systems at scale. Here’s a snapshot of the roles, impact, and platforms I’ve shaped along the way.

Career Evolution

Backend Development

Enterprise Java, Spring Boot, RESTful APIs, and backend systems architecture

Microservices Architecture

Distributed systems design with microservices, scalability, and integration

Event-Driven Systems

Apache Kafka, event streaming, message queues, and asynchronous processing

Cloud Architecture

AWS services, Kubernetes orchestration, Docker, and CI/CD automation

AI Integration

LLM integration, NLP models, and AI-driven intelligent systems

Agentic AI Systems

LangGraph workflows, multi-agent orchestration, and advanced LLM integration

AI + Cloud Automation

Vibe Coding AI agents that provision and manage cloud infrastructure via natural language

1
Backend Development

Software Development Company | Backend Developer

2011 - 2014

  • Developed enterprise-level backend systems using Java and Spring Framework
  • Implemented RESTful APIs and microservices for scalable applications
  • Designed robust backend architectures and database integration
  • Collaborated with cross-functional teams to deliver high-quality solutions
Java 8Spring MVCHibernateREST APIsOracle DBJUnitGitTomcat
2
Backend Systems

Enterprise Solutions Provider | Sr. Backend Engineer

2014 - 2018

  • Architected microservices-based backend systems using Spring Boot
  • Implemented RESTful APIs and integrated third-party services
  • Optimized application performance and database query efficiency
  • Mentored junior developers and established coding best practices
Java 8+Spring BootMicroservicesKafkaMySQLHibernateDockerJenkinsAWS EC2OAuth2/JWT
3
Distributed Systems

Healthcare - Employee Benefits | Solutions Architect

Mar 2018 - Oct 2021

  • Designed scalable microservices architecture using Java Spring Boot for benefits administration
  • Integrated Apache Kafka for real-time data streaming and event-driven architecture
  • Implemented Cassandra for distributed, fault-tolerant data models supporting high-availability systems
  • Built RESTful APIs optimized for performance, security, and scalability
  • Orchestrated containerized microservices using Docker and Kubernetes across cloud environments
JavaSpring BootCassandraKafkaAWS LambdaAWS S3AWS RDSAWS API GatewayAWS CognitoAWS CloudWatchAWS ElastiCacheDockerKubernetesOAuth2/JWTJenkins
4
Cloud Architecture

Workiva - SP Team | Solutions Architect

Nov 2021 - Apr 2025

  • Designed and implemented high-scale microservices for notifications, scheduling, and EDI file processing
  • Architected event-driven messaging using Apache Kafka for reliable, high-throughput data streaming
  • Engineered Kubernetes orchestration on AWS EKS with Docker containerization for production deployments
  • Implemented CI/CD pipelines using Jenkins and AWS CodePipeline for automated build, test, and deployment
  • Established comprehensive monitoring using Splunk, Prometheus, and Grafana for real-time observability
JavaSpring BootKotlinKafkaAWS EKSAWS CloudWatchDockerKubernetesJenkinsGitHub ActionsTerraformPrometheusGrafanaOpenAPI
5
Text-to-SQL & AI Dashboards

CAP-AI — Conversational Analytics Platform | Sr. AI Engineer

Jan 2025 - Apr 2025

  • Architected JARVIS — a LangGraph-powered backend that converts natural language questions into SQL queries and auto-generates interactive ECharts visualizations for Apache Druid analytics data
  • Built a multi-tenant RAG system using Qdrant vector database for context-aware chatbot responses across different client organizations
  • Designed 10+ analytics intent types (time-series, KPIs, maps, tables, publisher overviews) with intelligent time bucket selection and automatic chart metadata generation
  • Integrated MongoDB and Elasticsearch for article search, and built a user feedback reinforcement learning loop to continuously improve query accuracy
  • Delivered CAP-UI frontend (Next.js 14 + TypeScript + ECharts) with drag-and-drop dashboard builder, thread management, and saved insights
LangGraphFastAPIOpenAI GPTApache DruidQdrantEChartsMongoDBElasticsearchPostgreSQLRedisNext.js 14Docker
6
Machine Learning

AI-Based Industry Classification System | Sr. AI Engineer

Jan 2025 - Mar 2025

  • Developed custom NLP models using Claude Sonnet 3.5 v2 for automated business classification into NAICS, SIC, and ISIC codes
  • Implemented transfer learning with pre-trained language models for improved contextual understanding and accuracy
  • Designed RESTful API supporting thousands of classification requests per second using AWS Lambda and DynamoDB
  • Built confidence-scoring mechanism with multi-model classification approach for enhanced accuracy
Claude Sonnet 3.5PythonFastAPIAWS LambdaAWS DynamoDBAWS SQSAWS CloudWatchTF-IDFNERVector EmbeddingsREST APIsGitHub Actions
7
Agentic AI Systems

Multi-agent Conversational AI Application | Sr. AI Engineer

Mar 2025 - Apr 2025

  • Architected LangGraph-based workflow system with 4 specialized nodes for intelligent routing and context management
  • Integrated OpenAI GPT-4 and GPT-5-mini models with sophisticated prompt engineering for 6 specialized TVET assistants
  • Designed microservices architecture with Docker Compose orchestration and PostgreSQL for conversation persistence
  • Implemented JWT-based authentication with role-based access control and comprehensive error handling
LangGraphLangChainOpenAI GPT-4GeminiFastAPIWebSocketsConversationBufferMemoryPostgreSQLMongoDBRedisDockerLangFuseAirflowPrometheus
8
AI + Cloud Automation

AI-Powered Cloud Infrastructure Agent (OCI Terraform) | Sr. AI Engineer & Vibe Coder

Apr 2025 - Present

  • Built a conversational AI agent that provisions, modifies, and destroys Oracle Cloud Infrastructure (OCI) resources through natural language — no manual Terraform required
  • Designed a LangGraph multi-step agentic workflow with 9+ intent types including infra creation, resource queries, Docker image push to OCIR, and Terraform editing
  • Implemented human-in-the-loop confirmation with AI-generated cost estimation before any infrastructure is deployed
  • Engineered secure OCI credential management with Fernet (AES-128) encryption, stored and resolved from a PostgreSQL-backed credential store
  • Delivered full-stack Vibe Coded product — FastAPI + WebSockets backend, React/TypeScript frontend, deployed with Docker Compose
LangGraphPythonFastAPIWebSocketsOCI SDKTerraformPostgreSQLFernet EncryptionDockerReactTypeScriptGitHub Actions

Featured Projects

Real AI products that save time, cut costs, and unlock growth — built for FinTech, Healthcare, Legal, Education, Analytics, and Enterprise SaaS

Agentic AI + Cloud

AI Cloud Infrastructure Agent

Apr 2025 - Present

Setting up cloud servers used to take your dev team days and required expensive Terraform specialists. This AI agent changes that — your team simply describes what they need in plain English ('set up 3 servers with a load balancer in US East'), and the AI plans it, shows a cost estimate, waits for your approval, then deploys to Oracle Cloud automatically. No Terraform expertise needed. No surprise bills. No accidental deployments. Built for tech startups and SaaS teams who need to move fast without DevOps overhead. Industry: Tech Startups / SaaS / Cloud Teams. Outcome: Cloud infrastructure that used to take days now takes minutes — with full cost visibility and human approval before anything is deployed.

Technologies

LangGraphPythonFastAPI

Cloud Setup: Days → Minutes

AI & Automation

AI Invoice Processing & Automation Platform

2025 - Present

Your team is spending hours every week manually entering supplier invoices — and still missing errors. This AI platform eliminates that entirely. It automatically collects invoices from email or cloud storage, reads every line using AI-powered document recognition, extracts supplier names, amounts, and line items into your database, and sends your team a daily Slack report showing what was processed, what failed, and your supplier cost breakdown. Built for a multi-location restaurant group managing dozens of suppliers monthly. Industry: Hospitality / Food & Beverage / SME Finance. Outcome: Manual invoice entry eliminated — finance teams reclaim hours every week and get full daily visibility into supplier costs, with errors flagged before they become problems.

Technologies

PythonAWS S3Tesseract OCR

Manual Invoice Entry: Eliminated

Conversational AI & Analytics

CAP-AI: Conversational Business Intelligence Platform

Jan 2025 - Present

Your business data sits locked in databases — accessible only to analysts, unavailable to the people who actually need it. CAP-AI fixes this. Any team member types a plain-English question — 'What were our top 5 products by revenue last month?' — and instantly sees a live, interactive chart. No SQL. No waiting for a report. No analyst bottleneck. The AI understands 10+ types of business questions, picks the right chart automatically, and also includes a drag-and-drop dashboard builder for ongoing reporting. Includes an AI chatbot for general business questions backed by your own knowledge base. Industry: Media / Analytics SaaS / Publisher Platforms / Any Data-Driven SME. Outcome: Business teams get data-backed answers in seconds — decisions move faster and analysts focus on higher-value work instead of running routine reports.

Technologies

LangGraphFastAPIOpenAI GPT

Any Business Question → Live Chart

LLM & AI

Multi-Agent AI Tutoring System

Mar 2025 - Present

Scaling personalised learning is impossible when every learner needs 1-on-1 human coaching — it just doesn't grow. This AI platform deploys 6 specialist AI tutors, each expert in a different dimension of vocational teaching: pedagogy, TVET practice, reflective teaching, worldview alignment, and curriculum design. Every learner gets expert, context-aware guidance 24/7 — without waiting for a human tutor to be available. An AI orchestration layer automatically routes each conversation to the right specialist based on what the learner is asking. Industry: Vocational Education / EdTech / Corporate Training Providers. Outcome: Personalised coaching that scales to any number of learners without scaling headcount — learner support that doesn't grow linearly with your team.

Technologies

LangGraphOpenAI GPT-4GPT-4o-mini

1-on-1 AI Coaching, 24/7 at Scale

LLM & Automation

AI Business Classification & Data Enrichment Engine

Jan 2025 - Mar 2025

Manually categorising thousands of companies by industry is slow, inconsistent, and doesn't scale — especially when your analysts have to do it from scratch for each new dataset. This AI engine automatically assigns any business to the correct industry category (NAICS, SIC, and ISIC codes) in milliseconds, with a confidence score so you always know when to trust the result and when to flag it for human review. Processes thousands of records per second via a real-time API, and includes a self-improving feedback loop that increases accuracy over time the more it is used. Industry: Market Research / Financial Services / Insurance / Data Enrichment Companies. Outcome: A manual enrichment process that took analyst teams weeks now runs automatically at any scale — consistent, auditable, and continuously improving.

Technologies

Claude Sonnet 3.5PythonAWS Lambda

Weeks of Manual Work → Milliseconds

LLM & AI

FinTech AI Financial Assistant

1 Year

Users open a financial app and still can't find answers to simple questions about their own money — so they call support, or worse, they churn. This AI assistant is embedded directly into the mobile app, giving users instant, plain-English answers: 'How much did I spend on food last month?', 'Am I on track for my savings goal?', 'What were my biggest transactions this week?' The AI pulls live account data, transaction history, and personal financial goals to give personalised, accurate responses — with response times under 100ms so it feels truly instant. Industry: FinTech / Mobile Banking / Personal Finance / Wealth Management. Outcome: Users who actually understand their financial picture — reducing support contact rates, improving in-app engagement, and increasing product stickiness.

Technologies

PythonFastAPILangChain

Personal Finance Guidance, Instant

Enterprise Backend

Enterprise Notifications Platform — Workiva

Nov 2021 - Apr 2025

When your platform sends thousands of critical alerts simultaneously — reports ready, deadlines missed, approvals needed — every single one has to arrive. Missing or delayed notifications in an enterprise SaaS product damages trust fast. This notifications platform delivers bulk alerts across Email, Slack, and Microsoft Teams simultaneously, with event-driven architecture ensuring no message is ever dropped even during traffic spikes. Fully load-tested and validated for 10,000+ concurrent users before every release, with built-in delivery tracking and automatic retry logic for failed sends. Deployed as part of the Workiva global platform serving finance and compliance teams. Industry: Enterprise SaaS / Finance / Compliance. Outcome: Critical alerts reach the right people on the right channel, every time — with the zero-failure reliability that enterprise customers expect.

Technologies

JavaSpring BootApache Kafka

10,000+ Users · Zero Missed Alerts

Enterprise Backend

Enterprise Workflow Scheduling Engine — Workiva

Nov 2021 - Apr 2025

Enterprise businesses run on time-sensitive automated tasks — financial reports generated on schedule, deadline reminders sent automatically, data syncs triggered at midnight. When these jobs fail silently or run twice, it creates real compliance and business problems. This scheduling engine guarantees that every automated workflow runs exactly once at exactly the right time — whether it's a one-off job, a daily recurring report, or a complex conditional workflow triggered by business events. Handles thousands of scheduled jobs per day for the Workiva global platform, with full job history, monitoring, and failure alerting built in. Industry: Enterprise SaaS / Finance / Compliance / Workflow Automation. Outcome: Business-critical workflows run without manual oversight, on time, every time — eliminating the risk of missed deadlines or duplicate processing.

Technologies

KotlinOpenAPIConfluent Kafka

Every Workflow Runs On Time, Always

Healthcare Tech

HIPAA-Compliant Healthcare Data Exchange System

Mar 2018 - Oct 2021

Healthcare employers and insurance carriers exchange sensitive benefits data through strict regulatory formats — a process that is typically slow, error-prone, and manually managed, with serious compliance consequences when it goes wrong. This automated system generates HIPAA-compliant EDI data files from your benefits platform and delivers them directly to insurance carriers on schedule via FTP, SFTP, or Email — with full validation before transmission and complete audit trails for compliance reporting. Supports custom carrier profiles and field-level configuration so it works with any carrier's specific requirements. Industry: Healthcare / Employee Benefits / Health Insurance Brokers. Outcome: Benefits data exchange that previously required manual file preparation now runs automatically — reducing compliance risk, cutting transmission time from days to hours, and giving compliance teams full audit visibility.

Technologies

JavaSpring BootMySQL

HIPAA Data Exchange, Fully Automated

Legal Tech

Legal Case Management & Automation Platform

2 Years

Legal teams waste too much time on administration — manually drafting standard documents, tracking case deadlines in spreadsheets, chasing invoices that slip through the cracks. This case management platform brings everything into one place: cases, clients, documents, timelines, and billing. Standard contracts and letters generate from templates in seconds. Deadlines trigger automatic reminders. Time logs convert to invoices automatically at billing time. Built for scalability so it works equally well for a solo practitioner and a large multi-office firm. Industry: Legal Tech / Law Firms / Corporate Legal Departments / In-house Counsel. Outcome: Legal professionals spend less time on admin and more time on actual legal work — document turnaround is faster, nothing falls through the cracks, and billing is captured accurately every time.

Technologies

JavaSpring BootPostgreSQL

Legal Admin Time Cut Significantly

Security & DevOps

Mobile App Security Testing Platform

1 Year

Most mobile app security breaches happen because vulnerabilities weren't caught during development — and by the time they're discovered after launch, the reputational and financial damage is already done. This automated security platform continuously scans iOS, Android, and hybrid mobile apps for vulnerabilities using both static analysis (reviewing the source code) and dynamic analysis (testing the live running app). It plugs directly into your CI/CD pipeline so every build is automatically scanned, findings are ranked by severity with clear remediation steps, and your team gets a fix-ready report — not just a list of problems. Industry: FinTech / Healthcare Apps / E-commerce / Any App Handling Sensitive User Data. Outcome: Security vulnerabilities are found and fixed before release — protecting your users, your brand, and avoiding the costly aftermath of a post-launch security incident.

Technologies

PythonFastAPIDocker

Security Risks Caught Before Launch

Infrastructure

High-Scale API Gateway & Traffic Management

6 Months

As your product grows, your APIs become a target — for abuse, for scraping, and for traffic spikes that can take your service offline for paying customers. This API gateway sits in front of your services and intelligently manages every request: rate limiting per user and IP, OAuth 2.0 authentication, intelligent routing, and a circuit breaker that automatically isolates a failing service before it cascades to bring everything else down. Handles millions of API requests per day with response times under 1ms for legitimate users — so growth never translates to downtime. Industry: SaaS Products / Marketplaces / API-first Businesses / Developer Platforms. Outcome: Your APIs stay fast, protected, and available at any scale — abuse is blocked before it reaches your servers, and legitimate users never feel the impact of traffic spikes.

Technologies

GoRedisNginx

Millions of API Calls · Always Online

Enterprise Solutions

Employee Benefits Administration Platform

Mar 2018 - Oct 2021

Managing health benefits for hundreds of employees involves endless paperwork, eligibility changes, open enrolment chaos, and constant back-and-forth with insurance carriers — it grows in complexity every time you hire. This self-service benefits platform lets HR teams configure benefit plans, manage employee eligibility, run open enrolment, and automatically exchange data with insurance carriers — all without manual file preparation or spreadsheet tracking. Supports multiple employer groups and locations, role-based access for HR managers and employees alike, and automated reminders for enrolment deadlines and eligibility changes. Industry: HR Tech / Employee Benefits / Insurance Brokers / Mid-size Employers. Outcome: HR teams manage benefits for thousands of employees without growing the HR headcount — enrolment, eligibility, and carrier data exchange all run on autopilot.

Technologies

JavaSpring BootCassandra

HR Benefits Admin on Autopilot

AI & Automation

AI Document Processing & Data Extraction System

6 Months

Every business receives documents that need to be manually read and entered into systems — contracts, invoices, medical forms, insurance claims, delivery notes. It's slow, error-prone, and scales badly. This AI document processing system automatically reads PDFs, scanned images, and photos, extracts the structured data you need (names, dates, amounts, tables, line items), validates it for accuracy, and outputs it directly into your database or downstream system — in seconds. Handles multiple document types and layouts without needing a custom template for every format. Confidence scores flag uncertain extractions for human review rather than silently passing wrong data through. Industry: Insurance / Healthcare / Legal / Finance / Logistics / Any Document-Heavy Business. Outcome: Manual document data entry eliminated — documents processed in seconds instead of hours, with built-in quality control so your data stays clean.

Technologies

PythonTesseract OCROpenCV

Paper Docs → Structured Data, Instantly

Education & Certifications

Academic foundation and professional certifications

Academic Degrees

Master of Business Administrator

IT & Finance

Rajasthan Technical University

2009 - 2011

Bachelor of Technology

Computer Science

Rajasthan University

2005 - 2009

Professional Certifications

AWS Cloud Solutions Architect Associate

Amazon Web Services

Zend Certified Engineer

Zend Technologies

Frequently Asked Questions

Real answers to questions asked by recruiters, clients, and engineers — covering AI development, Vibe Coding, AI Solutions Architecture, backend systems, cloud infrastructure, and LLM engineering.

Availability & EngagementVibe Coding & AI DevelopmentAI & LLM EngineeringBackend, Cloud & DevOps
Availability & Engagement

Are you available for freelance AI projects, consulting engagements, or full-time roles?

Are you available for freelance AI projects, consulting engagements, or full-time roles?

Yes — I'm actively open to all three. I take on freelance and consulting projects through Upwork (Top Rated) and direct contracts, typically for AI product builds, LLM integrations, and cloud architecture work. I'm also open to full-time or contract-to-hire roles globally — remote-first, with availability across India, US, Canada, Australia, Ireland, Singapore, and Malaysia time zones. Reach out via email or the contact form and I'll respond within 24 hours.

Availability & Engagement

What industries have you built AI, backend, and cloud solutions for?

What industries have you built AI, backend, and cloud solutions for?

Over 15+ years I've shipped production systems across Healthcare (HIPAA-compliant EDI, benefits administration), FinTech (conversational AI, real-time transaction analysis), Education/TVET (multi-agent tutoring platforms), Media & Analytics (conversational dashboards, Text-to-SQL, publisher insights), Legal Tech (document automation, case management), E-commerce, and Enterprise SaaS (Workiva — notifications, scheduling, and EDI at 10K+ concurrent users). Each domain has shaped how I design AI systems that are both technically sound and business-aware.

Vibe Coding & AI Development

What is Vibe Coding and how does it help ship AI products faster?

What is Vibe Coding and how does it help ship AI products faster?

Vibe Coding is an AI-first development style where you collaborate deeply with LLMs — Claude, GPT-4, Gemini — not just to write code, but to architect systems, generate boilerplate, validate logic, and debug at speed. As a Vibe Coder with 15+ years of engineering depth, I combine AI-assisted development with production-grade judgement. The result is enterprise-quality AI solutions shipped 3–5x faster than traditional methods — without sacrificing security, scalability, or code quality.

Vibe Coding & AI Development

What does an AI Solutions Architect do, and what can you build for my business?

What does an AI Solutions Architect do, and what can you build for my business?

An AI Solutions Architect designs the full technical strategy for how AI fits into your product or organisation — choosing the right LLMs, designing RAG pipelines, defining multi-agent workflows, setting up observability, and ensuring the system scales under real load. I've built conversational analytics platforms (Text-to-SQL + live charts), multi-agent educational assistants, AI-powered invoice processing pipelines, cloud infrastructure agents that provision OCI resources via chat, and LLM-driven classification systems. If you have a business problem and want to solve it with AI, I can design and build the solution end-to-end.

AI & LLM Engineering

How do you build conversational analytics and Text-to-SQL AI systems?

How do you build conversational analytics and Text-to-SQL AI systems?

The core is a LangGraph state machine that classifies the user's intent (time-series, KPI, ranking, etc.), retrieves the relevant schema context, generates a parameterized SQL query, executes it against the analytics store (Apache Druid, BigQuery, PostgreSQL), and returns structured chart metadata alongside the raw data. I pair this with a Redis cache layer for repeated query patterns, a RAG fallback (Qdrant vector DB) for general questions, and an ECharts/Recharts frontend that auto-selects the right chart type. I've built this end-to-end for a media analytics SaaS platform with 10+ intent types and reinforcement learning from user feedback.

AI & LLM Engineering

How do you design production-grade agentic AI systems with LangGraph?

How do you design production-grade agentic AI systems with LangGraph?

Start with a clear state schema — every field the agent needs to make decisions. Model each action as a node (requirement gathering, planning, execution, confirmation) and use conditional edges for routing logic. Add interrupt points for human-in-the-loop approval gates. Implement checkpointing so long-running workflows survive restarts. Use sub-graphs for modular agent teams and streaming for real-time UI feedback. I've used this architecture to build a cloud infrastructure agent (OCI Terraform provisioning via chat) and a 6-specialist TVET educational platform — both with phase-aware context tracking so follow-up messages always land at the right node.

AI & LLM Engineering

How do you orchestrate multi-agent systems for complex enterprise workflows?

How do you orchestrate multi-agent systems for complex enterprise workflows?

Use a supervisor-plus-specialist pattern: a routing agent classifies intent and delegates to domain-specific agents (each with its own prompt, tools, and memory scope). Shared state via LangGraph's graph context or a vector store keeps context consistent across handoffs. Add circuit breakers so one failing agent doesn't cascade. Use semantic routing (embedding similarity) instead of rigid conditionals for more robust intent classification. I monitor token usage per agent, log full trace chains via LangFuse or LangSmith, and implement fallback responses when agents hit confidence thresholds.

AI & LLM Engineering

Which LLM should you choose for different production use cases?

Which LLM should you choose for different production use cases?

GPT-4o for general reasoning, tool use, and speed at scale. Claude Sonnet 3.5/3.7 for long-context tasks, code generation, and document processing. Gemini 2.0 Flash for multimodal inputs and cost-sensitive, low-latency pipelines. Llama 3.x / Mistral for on-premises or compliance-restricted deployments. For routing and classification within agent workflows, always use smaller, cheaper models (GPT-4o-mini, Claude Haiku) — never waste frontier model budget on intent classification. The right answer always comes from benchmarking on your own domain data, not general leaderboard scores.

Backend, Cloud & DevOps

How do you automate cloud infrastructure provisioning using AI?

How do you automate cloud infrastructure provisioning using AI?

I built an end-to-end AI agent (OCI Terraform Agent) where users describe what they need in plain English — 'I need 3 compute instances in the US East region with a load balancer' — and the agent gathers requirements via clarifying questions, generates a full Terraform plan with cost estimates, shows it for human approval, then executes terraform init/plan/apply live. Built on LangGraph with OCI SDK integration, Fernet-encrypted credential management, FastAPI + WebSocket backend, and a React frontend. The same pattern applies to AWS, GCP, or Azure — it's infrastructure as conversation.

Backend, Cloud & DevOps

How do you architect scalable microservices for high-throughput enterprise systems?

How do you architect scalable microservices for high-throughput enterprise systems?

I use event-driven architecture as the backbone: Apache Kafka for reliable, high-throughput data streaming between services, with each microservice owning its own database (PostgreSQL, Cassandra, or DynamoDB depending on access patterns). Services are containerised with Docker and orchestrated on Kubernetes (AWS EKS), with CI/CD via GitHub Actions or Jenkins. For observability I layer Prometheus + Grafana for metrics, Splunk or ELK Stack for logs, and distributed tracing with trace IDs on every request. I've validated systems at 10,000+ concurrent users with Locust load testing before every production release.

Backend, Cloud & DevOps

What are the critical production concerns for RAG systems in 2025?

What are the critical production concerns for RAG systems in 2025?

Retrieval quality is everything — implement hybrid search (dense + sparse BM25) and a reranker (Cohere, cross-encoder) to surface genuinely relevant chunks, not just semantically similar ones. Use metadata filtering aggressively to narrow the search space before embedding similarity kicks in. Cache embeddings for repeated queries (cosine threshold 0.95+). For multi-tenant systems, isolate vector namespaces per client. Monitor chunk relevance scores, track retrieval precision against a golden eval dataset, and build a user feedback loop so failed answers become training signal. I use Qdrant for production RAG — it handles multi-tenancy, filtering, and payload storage cleanly.

Backend, Cloud & DevOps

How do you manage LLM costs and observability in production AI systems?

How do you manage LLM costs and observability in production AI systems?

Cost control starts at the routing layer — use the smallest model that achieves acceptable quality for each task class (classification, summarisation, generation get different model tiers). Implement semantic caching so repeated queries hit Redis instead of the LLM. Set per-request token budgets and alert when P95 cost spikes. For observability, I use LangFuse or LangSmith for full trace logging — every prompt version, model response, latency P95/P99, and tool call chain is logged with a trace ID. Prompt versions are managed like code: versioned, A/B tested, and rolled back on quality regressions. Hallucination monitoring uses an eval dataset with automated scoring on every deployment.

Let's Connect

Looking to hire an AI Engineer, AI Solutions Architect, or Vibe Coder? Let's talk about your AI project, backend system, or cloud automation challenge.

Core Expertise

AI EngineerAI Solutions ArchitectVibe CoderAI DeveloperLLM IntegrationAgentic AIBackend SystemsCloud & DevOpsMicroservicesApache Kafka

Connect With Me

🤖AI & LLM

LangChainLangGraphOpenAI GPT-4GPT-5-miniClaude SonnetGeminiVertex AIAssistants APICustom ML ModelsNLPSemantic Search

💻Backend

PythonJavaKotlinFastAPIFlaskSpring BootHibernateJPAREST APIsGraphQLOpenAPIWebSocketsJWTOAuth 2.0

☁️Cloud & Infra

AWSLambdaDynamoDBS3EKSECREC2SQSSNSSESCloudWatchIAMDockerKubernetesCodePipelineCodeDeployTerraformCloudFormation

🗄️Databases

PostgreSQLMySQLCassandraMongoDBRedisDynamoDBElastiCacheVector DBsFAISSPineconeChromaDB

📊Observability

KafkaRabbitMQSQS/SNSPrometheusGrafanaSplunkELK StackCloudWatchLangFuseDataDog

Portfolio

AI Solutions Architect with 15+ years of experience building LLM-powered systems, intelligent automation, and scalable enterprise AI applications using OpenAI, AWS, and Python.

Expertise

  • LLM Integration & Customization
  • Agentic AI Systems
  • Microservices Architecture
  • Cloud Infrastructure
  • Event-Driven Systems

© 2026 Puneet Singhal - Senior AI Engineer. All rights reserved.