Full-Stack AI Engineer
I build AI applications end to end — from product experience to backend workflows, RAG, evals, and deployment.
I combine full-stack software engineering with practical AI system design: frontend UX, backend APIs, retrieval, agent workflows, evaluation, observability, and production delivery.
6 years 4 months building production systems across cloud, data, and AI-adjacent platforms — now focused on shipping useful AI products and teaching others how to build them.
Full-stack AI system map
The layers I design together
AI Product UX
Copilot screens, review queues, dashboards, feedback, and trust signals.
Frontend State + Events
Streaming responses, loading states, user corrections, and workflow progress.
Backend Workflow Layer
APIs, auth, queues, state machines, orchestration, and human approval paths.
RAG + Data Layer
Documents, metadata, embeddings, retrieval, citations, freshness, and permissions.
Model + Agent Layer
LLM calls, tool contracts, routing, fallback logic, and controlled agent actions.
Eval + Observability
Traces, prompt versions, golden workflows, cost, latency, quality checks, and release gates.
Learners and professionals mentored through workshops, sessions, and coaching
Product UI, backend APIs, data pipelines, AI workflows, deployment, and observability
AICTE ATAL FDP, DSCE GenAI workshop, and MIT Pune AI/ML workshop proof
Agentic AI Production Harness with runnable demo, evals, traces, and tool contracts
Glimpse of Sessions
Trusted in classrooms, faculty programs, and hands-on AI workshops.
AICTE ATAL FDP delivery, full-stack GenAI workshop sessions, national-level AI/ML training, mentoring, and judging.
SMVITM, Udupi
AICTE ATAL FDP Resource Person
Faculty Development Program on Modelling for Business Decisions with certificate and classroom.
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Dayananda Sagar College of Engineering
Full-Stack GenAI Application Development
Hands-on GenAI application development workshop with mentoring, project discussion, and judging.
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MIT Arts, Commerce & Science College, Pune
National-level AI & ML Workshop
Two-day national-level AI & ML workshop with event and session photo evidence.
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Hiring managers
Inspect full-stack AI positioning, runnable engineering artifacts, and production-system thinking.
Start path
Founders and product teams
Review how I think about AI workflow control, RAG, tool use, evals, cost, latency, and rollout risk.
Start path
Colleges and workshop hosts
See real FDP, GenAI, AI/ML workshop photos, certificate proof, and hands-on teaching formats.
Start here
The three things visitors should inspect first
Agentic AI Production Harness
A flagship build track for making AI agents observable, controlled, testable, and production-ready.
Case studiesFull-stack AI product systems
Interfaces, APIs, retrieval, workflow orchestration, evals, dashboards, and deployment patterns for practical AI products.
WritingAI engineering writing
Field notes on building AI apps that survive real users, changing data, unclear workflows, and production constraints.
Full-stack AI scope
Not only model calls. The full product system.
Strong AI products need product UX, backend design, data quality, retrieval, workflow control, evals, observability, and deployment discipline working together.
AI product frontend
Copilot screens, workflow UIs, dashboards, review queues, admin panels, streaming responses, and trust-focused UX.
Backend and orchestration
APIs, auth, queues, state machines, tool execution, workflow recovery, and human approval paths.
RAG and data layer
Document ingestion, metadata, vector search, citations, freshness checks, and retrieval evaluation.
Evaluation and operations
Prompt/version tracking, traces, cost and latency checks, rubric-based evals, regression tests, and release readiness.
Writing focus