Building intelligent systems that ship to production
I design and build production-grade AI/ML systems — from fine-tuned LLMs and multi-agent RAG pipelines to
fully deployed MLOps platforms on Kubernetes. I obsess over the full stack: model training, API design,
DevSecOps, and GitOps.
I'm Harshit Rai, an AI/ML Engineer from Delhi, India. I build end-to-end intelligent systems —
from training LoRA fine-tuned models to shipping production-ready APIs on Kubernetes with full CI/CD
pipelines.
My sweet spot is at the intersection of AI/ML, backend engineering, and DevOps. I've shipped
multi-agent RAG systems, multilingual NLP microservices, AI talent platforms, and secure MLOps APIs —
all fully containerized and cloud-deployed.
Currently building a SaaS product for automated competitive intelligence for dev teams,
and always looking for interesting problems to solve.
locationDelhi, India
emaildev.harshitrai@gmail.com
githubgithub.com/Harshitraiii2005
linkedinlinkedin.com/in/harshitrai20
focusAI/ML · LLMs · MLOps ·
DevOps
// TECHNICAL JOURNEY
AI/ML Engineer
Production AI Systems · 2025 – Present
Building multi-agent RAG systems, fine-tuned LLMs, and enterprise MLOps pipelines
deployed on Kubernetes with ArgoCD GitOps.
Backend & DevOps
FastAPI · Docker · Jenkins · AWS · 2024 – Present
Shipping production-grade APIs with full DevSecOps: SAST scanning, container
hardening, Prometheus monitoring, and K8s HPA.
Open Source & Learning
LangChain · LangGraph · PyTorch · Groq
Exploring and documenting the AI/LLM ecosystem — from chain patterns to multi-agent
orchestration frameworks.
// 02. experience
Professional Experience
Hands-on ML engineering, production deployment, and observability delivered from a
fast-paced internship.
• Developed and deployed end-to-end ML models for data pipeline processing using Python and TensorFlow;
achieved ~45% performance gain via pruning and quantization.
• Built scalable Docker + Kubernetes deployment pipelines with automated GitHub Actions CI/CD for model
testing, versioning, and release; reduced deployment time by 60%.
• Implemented model monitoring using Prometheus and Grafana; maintained 99.9% uptime across deployed services.
// 03. projects
Things I've Built
12 projects shipped across AI/ML, DevOps, and backend engineering.
Automated first-pass legal review via a 5-agent workflow (Extractor, Scorer, Compliance Checker,
Redliner, Writer) using LangGraph-based task decomposition & reasoning loops.
Improved risk-assessment accuracy by grounding LLM outputs in precedent — RAG with Pinecone retrieves
top-3 similar historical clauses for context augmentation.
Enforced production quality by gating deployments on a 60%+ calibration accuracy threshold via Jenkins +
MLflow evaluation harness against a golden dataset.
Reduced incident response time by building an autonomous agent that performs planning, tool calling, and
reasoning over service registries, runbooks, and live alerts.
Eliminated unsafe autonomous actions with a tiered safety-guard system — auto-approving read-only calls
and routing all state-mutating actions for manual sign-off.
Achieved full audit traceability via parallel tool-calling with an immutable, append-only Postgres audit
log (revoked UPDATE/DELETE).
Built a full-stack SaaS pipeline that monitors competitor URLs on a schedule, detects changes via
SHA-256 hashing, and generates structured LLM intelligence reports.
Automated multi-channel delivery of reports (Slack, Email, Webhook) via Redis/RQ async job queue and
APScheduler-based cron scheduling.
Reduced manual competitive-tracking effort by an estimated 3–5 hours per week per engineering team
through end-to-end automation.
Improved context relevance 3.4× over a vanilla RAG baseline via hybrid FAISS dense + BM25 keyword
search, MMR diversity ranking, and cross-encoder reranking.
Reduced fine-tuning parameter count by 95% applying LoRA adapters to GPT-2, achieving 96% pass rate
across 25 evaluation cases with structured-output validation.
Delivered sub-200ms average response latency via a multimodal interface (Whisper speech-to-text, Edge
TTS) with hallucination-detection fallback logic.
Production-ready Retrieval-Augmented Generation system powered by fine-tuned GPT-2 with LoRA adapters.
Achieves 96% test pass rate across 25 comprehensive test cases with 0.15s average response time.
Features hybrid FAISS + BM25 search, MMR diversity ranking, hallucination detection, and multimodal I/O
(Whisper speech-to-text + Edge TTS output). Fully Dockerized and scalable.
Multi-agent AI platform for research paper analysis using LangGraph orchestration. Automatically
summarizes papers, detects methodology flaws, generates insights, compares related work,
and produces PPTX presentations. Deployed with FastAPI, PostgreSQL (Neon), Redis caching, and S3 vector
storage.
Production-grade multilingual NLP microservice for English, Hindi, and Hinglish text.
Performs sentiment analysis (5-star BERT) and zero-shot topic classification (mDeBERTa).
Batch processes up to 20 texts per request with async Redis logging. Deployed on AWS with full Kubernetes +
HPA.
Enterprise-grade AI talent platform with dual RAG pipelines — one for freshers (0–3 yrs),
one for experienced candidates (3+ yrs). Features explainable AI matching, interview question generation,
resume optimization, and facial emotion recognition. Full GitOps via ArgoCD + Jenkins CI/CD.
Comprehensive guide and working implementations of 10 LangChain chain patterns — from traditional
to memory chains, router to tool-use agents. Each pattern includes working code, ASCII graph visualizations,
and detailed documentation. Built with Groq LLM backend.
Intelligent document processor extracting structured insights from PDFs/images using Claude vision API.
Generates SRT subtitles from video transcripts, auto-tags documents, and exports to multiple formats.
Built with async Celery task queue and Redis caching. Handles 100+ docs/day in production.
Monitors competitor URLs on a schedule, detects changes via SHA-256 hashing + diff, sends diffs to an LLM
(Claude/GPT-4o/Groq), and delivers structured reports via Slack, email (Resend), or webhook. Built on
FastAPI, Next.js, PostgreSQL, Redis + RQ, Playwright, and JWT auth.
Autonomous DevOps agent parsing infra alerts, reasoning over conflicts, and executing safe actions
automatically. Freezes state-mutating actions behind a human approval gate. Supports parallel multi-service
execution via thread pools with full ID-correlated audit logs.
Tier-2Human Approval Gate
ParallelMulti-service Exec
Full AuditCorrelation IDs
PythonFastAPISQLiteVector Store (RAG)Groq LLMThreadPoolExecutorReactJenkinsKubernetes
Production RAG system answering Python programming questions grounded in 50k Stack Overflow pairs. Embeds
queries with all-MiniLM-L6-v2, retrieves top-K matches from ChromaDB, and generates answers via Groq.
Features async FastAPI and health-check Docker Compose.
Production-ready Retrieval-Augmented Generation system powered by fine-tuned GPT-2 with LoRA adapters.
Achieves 96% test pass rate across 25 comprehensive test cases with 0.15s average response time.
Features hybrid FAISS + BM25 search, MMR diversity ranking, hallucination detection, and multimodal I/O
(Whisper speech-to-text + Edge TTS output). Fully Dockerized and scalable.
Multi-agent AI platform for research paper analysis using LangGraph orchestration. Automatically
summarizes papers, detects methodology flaws, generates insights, compares related work,
and produces PPTX presentations. Deployed with FastAPI, PostgreSQL (Neon), Redis caching, and S3 vector
storage.
Production-grade multilingual NLP microservice for English, Hindi, and Hinglish text.
Performs sentiment analysis (5-star BERT) and zero-shot topic classification (mDeBERTa).
Batch processes up to 20 texts per request with async Redis logging. Deployed on AWS with full Kubernetes +
HPA.
Enterprise-grade AI talent platform with dual RAG pipelines — one for freshers (0–3 yrs),
one for experienced candidates (3+ yrs). Features explainable AI matching, interview question generation,
resume optimization, and facial emotion recognition. Full GitOps via ArgoCD + Jenkins CI/CD.
Comprehensive guide and working implementations of 10 LangChain chain patterns — from traditional
to memory chains, router to tool-use agents. Each pattern includes working code, ASCII graph visualizations,
and detailed documentation. Built with Groq LLM backend.
Intelligent document processor extracting structured insights from PDFs/images using Claude vision API.
Generates SRT subtitles from video transcripts, auto-tags documents, and exports to multiple formats.
Built with async Celery task queue and Redis caching. Handles 100+ docs/day in production.
Monitors competitor URLs on a schedule, detects changes via SHA-256 hashing + diff, sends diffs to an LLM
(Claude/GPT-4o/Groq), and delivers structured reports via Slack, email (Resend), or webhook. Built on
FastAPI, Next.js, PostgreSQL, Redis + RQ, Playwright, and JWT auth.
Autonomous DevOps agent parsing infra alerts, reasoning over conflicts, and executing safe actions
automatically. Freezes state-mutating actions behind a human approval gate. Supports parallel multi-service
execution via thread pools with full ID-correlated audit logs.
Tier-2Human Approval Gate
ParallelMulti-service Exec
Full AuditCorrelation IDs
PythonFastAPISQLiteVector Store (RAG)Groq LLMThreadPoolExecutorReactJenkinsKubernetes
Production RAG system answering Python programming questions grounded in 50k Stack Overflow pairs. Embeds
queries with all-MiniLM-L6-v2, retrieves top-K matches from ChromaDB, and generates answers via Groq.
Features async FastAPI and health-check Docker Compose.
Production-ready Retrieval-Augmented Generation system powered by fine-tuned GPT-2 with LoRA adapters.
Achieves 96% test pass rate across 25 comprehensive test cases with 0.15s average response time.
Features hybrid FAISS + BM25 search, MMR diversity ranking, hallucination detection, and multimodal I/O
(Whisper speech-to-text + Edge TTS output). Fully Dockerized and scalable.
Multi-agent AI platform for research paper analysis using LangGraph orchestration. Automatically
summarizes papers, detects methodology flaws, generates insights, compares related work,
and produces PPTX presentations. Deployed with FastAPI, PostgreSQL (Neon), Redis caching, and S3 vector
storage.
Production-grade multilingual NLP microservice for English, Hindi, and Hinglish text.
Performs sentiment analysis (5-star BERT) and zero-shot topic classification (mDeBERTa).
Batch processes up to 20 texts per request with async Redis logging. Deployed on AWS with full Kubernetes +
HPA.
Enterprise-grade AI talent platform with dual RAG pipelines — one for freshers (0–3 yrs),
one for experienced candidates (3+ yrs). Features explainable AI matching, interview question generation,
resume optimization, and facial emotion recognition. Full GitOps via ArgoCD + Jenkins CI/CD.
Comprehensive guide and working implementations of 10 LangChain chain patterns — from traditional
to memory chains, router to tool-use agents. Each pattern includes working code, ASCII graph visualizations,
and detailed documentation. Built with Groq LLM backend.
Intelligent document processor extracting structured insights from PDFs/images using Claude vision API.
Generates SRT subtitles from video transcripts, auto-tags documents, and exports to multiple formats.
Built with async Celery task queue and Redis caching. Handles 100+ docs/day in production.
Monitors competitor URLs on a schedule, detects changes via SHA-256 hashing + diff, sends diffs to an LLM
(Claude/GPT-4o/Groq), and delivers structured reports via Slack, email (Resend), or webhook. Built on
FastAPI, Next.js, PostgreSQL, Redis + RQ, Playwright, and JWT auth.
Autonomous DevOps agent parsing infra alerts, reasoning over conflicts, and executing safe actions
automatically. Freezes state-mutating actions behind a human approval gate. Supports parallel multi-service
execution via thread pools with full ID-correlated audit logs.
Tier-2Human Approval Gate
ParallelMulti-service Exec
Full AuditCorrelation IDs
PythonFastAPISQLiteVector Store (RAG)Groq LLMThreadPoolExecutorReactJenkinsKubernetes
Production RAG system answering Python programming questions grounded in 50k Stack Overflow pairs. Embeds
queries with all-MiniLM-L6-v2, retrieves top-K matches from ChromaDB, and generates answers via Groq.
Features async FastAPI and health-check Docker Compose.