Building production-grade AI systems — multi-agent LLM orchestration, multimodal RAG, speech & video ML, legal AI, and civic technology. 3+ years of hands-on ML engineering, plus open-source and applied research.
Flagship production systems, research implementations, and agentic AI architectures.
Isochrony-aware video dubbing pipeline translating English to 11 Indic languages with zero-shot voice cloning. The v3 fine-tune constrains translation to a strict phoneme budget, cutting length error 79% (0.495→0.103 rel.) while improving translation quality (chrF++ 22.0→30.1).
A multi-account email agent consolidating Gmail, Zoho, Yahoo & Outlook into one AI-enriched inbox — a single MCP server that is itself an MCP client, fanning out to each provider and re-exposing 17 unified tools with self-healing child process reconnection. 49 passing tests.
Production civic-issue reporting platform: residents map potholes, garbage and drainage across Noida and NCR, with server-rendered social share cards, a two-tier trust model, and a hardened upload pipeline — all running for about $3/month.
An open-source bounty & issue aggregator whose moat is telling the truth about a bounty — including when the truth is "don't bother." A pure-function, three-layer trust engine rejects engagement farms and crypto-deposit scams before any network call; live-validated when a real farm's admitted count dropped from 20 to 0.
Ten cooperating AI agents that turn long podcasts into scheduled short-form videos — a provider-agnostic LLM layer with automatic fallback, a learning feedback loop, and a QA gate. Runs unattended on free providers. MIT-licensed.
Paste a YouTube channel URL and it reverse-engineers the style, writes an original script in that voice, narrates and illustrates it, and compiles a finished MP4 — for $0. A three-tier LLM failover, self-healing JSON validators, and a zero-network procedural generator guarantee output even when every API is down.
AI-powered Indian Knowledge System oracle enabling semantic querying of Vedic texts, Upanishads, and ancient Indian manuscripts using multilingual RAG & graph-based retrieval.
Interactive dashboard for valuing football actions using the VAEP framework. Analyzes match events across Top 5 European Leagues and World Cup 2018 to rank players and visualize goal-scoring sequences.
Interactive Expected Goals (xG) simulator with pitch-mapping and heatmap visualization. Evaluates scoring probability in real-time.
Advanced semantic matching engine using Sentence-Transformers. Features skill extraction and gap analysis for job applications.
CNN-based mobile diagnostic tool for identifying crop diseases in real-time. Achieved 98% accuracy on the PlantVillage dataset.
Real-time weather tracking with a secure proxy layer. Implements global rate limiting and intelligent caching to manage API quotas.
Explore and test live production ML models deployed on Hugging Face Spaces. Profile: huggingface.co/nirmitk
AI-powered Indian Knowledge System enabling semantic search & graph-based exploration over Vedic texts and ancient Indian manuscripts with multilingual embeddings.
Action-valuation dashboard quantifying offensive and defensive match events across Top 5 European Leagues and World Cup 2018 using the VAEP framework.
Interactive pitch coordinate mapper and statistical scoring probability estimator powered by a Random Forest Classifier trained on European league shot events.
Deep semantic matching and skill gap analyzer comparing candidate resumes against job descriptions using Sentence-Transformers (SBERT all-MiniLM-L6-v2).
Deep CNN vision diagnostics identifying 38 crop foliar diseases in real-time from leaf imagery with 98% validation accuracy on the PlantVillage dataset.
View the complete catalog of open-source datasets, model weights, and live web applications hosted under the @nirmitk Hugging Face organization.
Deep-dives on the engineering and research behind my projects — written to be read, not skimmed. From production systems to primary-source history.
AI dubbing drifts because translations aren't the same length as the original. The industry fixes it in the audio; I fixed it in the translator — cutting length error from 49% to 10% on a fine-tuned Llama-3.1-8B, with the full training telemetry left in.
A GPU I didn't pay for, running an app I could open from my phone, that I could kill from anywhere — a tunnel out, a public message channel back, and a kill switch. Plus the meta-tensor bug that cost the most time.
Detection asks what just happened; anticipation asks what's about to. My SoccerNet 2026 scores say the model knows what is coming far better than when — and the decomposed metric shows exactly which half broke.
The architecture: one MCP server that is also an MCP client, consolidating four mail providers behind a single 17-tool surface — with exactly one custom component.
An MCP connection is a child process, and child processes die. The self-healing reconnection — isolation, drop-detection, and a cooldown — that makes it survivable, with two production bugs pinned in tests.
Empty JSON from an 80-token task with a 512 budget. On a thinking model the fix for a truncated answer is a bigger budget — and it fails completely silently. Floor it, double on retry.
I verified four real Algora bounties live and found every winnable-looking one saturated or already paid. The honest move was to report the negative result, not manufacture a win — which is exactly what the product promises its users.
Ideation and research behind a trust-first bounty aggregator: the polluted label space, the bounty-plaza crypto-deposit scam we reverse-engineered, and the four falsifiable hypotheses that shaped the build.
The async FastAPI + Next.js architecture, a natural-key data model, and a three-layer trust engine that rejects farms and scams before any network call — validated by unit tests and a live re-probe that dropped a farm's admitted count from 20 to 0.
The end-to-end bounty lifecycle run live: public boards on hot orgs are saturated red oceans. Why the failure created GitScout's second product axis — a viability signal — and why no completed claim was manufactured.
How ten specialised AI agents hand off work like a production company — a provider-agnostic brain with automatic fallback, prompt-injected craft playbooks, a learning loop, and a QA gate that blocks broken output.
One channel URL in, a finished narrated video out — for $0. The engineering that matters isn't any single model but the resilience layer: a three-tier failover cascade, self-healing validators, and a procedural generator that guarantees output when every API fails.
A history-to-policy argument from 18th-century primary sources: India's mechanization bottleneck has always been access to divisible draft power — not tools, cost, or awareness — and it still is.
Making a JavaScript map describe itself to WhatsApp crawlers, trusting strangers just enough to let them post, and hardening an upload path that assumes every phone photo carries home GPS — for ~$3/month.
The production v2 system — how it constrains Gemini translation to a phoneme budget, preserves the soundstage with source separation, and clones voices at a controlled duration, all on a free Kaggle T4.
I'm a certified AI/ML Engineer with a M.Tech in Modeling & Simulation and 3+ years of industry experience building intelligent systems across NLP, LLMs, multimodal ML, and agentic AI.
My work spans production deployments at Chetu India and BLU PARROT — including an agentic event-commerce chatbot, a Video-to-SOP multimodal pipeline, legal AI for Indian law, sports action anticipation research, and a participant in the SoccerNet 2026 Challenge.
Most recently AI/ML Software Engineer at Chetu India (through mid-2026). Now available for freelance, consulting, and research collaboration — remote — and open to full-time roles from September 2026.
From classical ML to cutting-edge multimodal AI — built and shipped in production.
LangGraph multi-node agents, RAG variants (Naive/LightRAG/CRAG/VideoRAG), LangSmith tracing, OpenAI & Gemini APIs, FastAPI.
CNNs, Transformers, VideoRAG, ImageBind multimodal embeddings, OpenCV, FUTR anticipation models, T-DEED temporal spotting.
BERT variants (InLegalBERT, XLM-RoBERTa, IndicBERT), FasterWhisper ASR, multilingual NMT, text classification, summarization.
FAISS, HNSWLib, Neo4j, NetworkX, HuggingFace, W&B, Python scraping, Azure OCR, data engineering pipelines.
Expected Goals (xG) modelling (89.77% accuracy), FAANTRA ball action anticipation, SoccerNet 2026 Challenge participant, pass network analysis.
LegalGPT: case summarization, 150+ contract types classification, compliance AI, Opennyai, InCaseLawBERT fine-tuning.
Building AI that ships — from startup chaos to enterprise scale.