Puneet Singhal
AI Engineer & Solutions 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 UpworkCore 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
Databases
Message Streaming
Cloud & DevOps
AI, LLM & Vibe Coding
Monitoring & Logging
Soft Skills
Industry 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
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
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
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
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
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
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
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
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
Featured Projects
Real AI products that save time, cut costs, and unlock growth — built for FinTech, Healthcare, Legal, Education, Analytics, and Enterprise SaaS
AI Cloud Infrastructure Agent
Apr 2025 - PresentSetting 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
Cloud Setup: Days → Minutes
AI Invoice Processing & Automation Platform
2025 - PresentYour 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
Manual Invoice Entry: Eliminated
CAP-AI: Conversational Business Intelligence Platform
Jan 2025 - PresentYour 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
Any Business Question → Live Chart
Multi-Agent AI Tutoring System
Mar 2025 - PresentScaling 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
1-on-1 AI Coaching, 24/7 at Scale
AI Business Classification & Data Enrichment Engine
Jan 2025 - Mar 2025Manually 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
Weeks of Manual Work → Milliseconds
FinTech AI Financial Assistant
1 YearUsers 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
Personal Finance Guidance, Instant
Enterprise Notifications Platform — Workiva
Nov 2021 - Apr 2025When 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
10,000+ Users · Zero Missed Alerts
Enterprise Workflow Scheduling Engine — Workiva
Nov 2021 - Apr 2025Enterprise 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
Every Workflow Runs On Time, Always
HIPAA-Compliant Healthcare Data Exchange System
Mar 2018 - Oct 2021Healthcare 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
HIPAA Data Exchange, Fully Automated
Legal Case Management & Automation Platform
2 YearsLegal 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
Legal Admin Time Cut Significantly
Mobile App Security Testing Platform
1 YearMost 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
Security Risks Caught Before Launch
High-Scale API Gateway & Traffic Management
6 MonthsAs 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
Millions of API Calls · Always Online
Employee Benefits Administration Platform
Mar 2018 - Oct 2021Managing 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
HR Benefits Admin on Autopilot
AI Document Processing & Data Extraction System
6 MonthsEvery 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
Paper Docs → Structured Data, Instantly
Education & Certifications
Academic foundation and professional certifications
Academic Degrees
Master of Business Administrator
IT & Finance
Rajasthan Technical University
Bachelor of Technology
Computer Science
Rajasthan University
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 & EngagementAre 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?
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 & EngagementWhat industries have you built AI, backend, and cloud solutions for?
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 DevelopmentWhat 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?
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 DevelopmentWhat 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?
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 EngineeringHow do you build conversational analytics and Text-to-SQL AI systems?
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 EngineeringHow do you design production-grade agentic AI systems with LangGraph?
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 EngineeringHow do you orchestrate multi-agent systems for complex enterprise workflows?
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 EngineeringWhich LLM should you choose for different production use cases?
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 & DevOpsHow do you automate cloud infrastructure provisioning using AI?
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 & DevOpsHow do you architect scalable microservices for high-throughput enterprise systems?
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 & DevOpsWhat are the critical production concerns for RAG systems in 2025?
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 & DevOpsHow do you manage LLM costs and observability in production AI systems?
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.

