Artificial Intelligence & Machine Learning Engineer
I design and ship production-grade AI systems — multi-agent intelligence architectures, LLM orchestration engines, and autonomous DevOps pipelines. Built on OpenAI, Anthropic, and open-weight models. Zero to deployed, at velocity.
Work
Self-Directed · Lucknow, India
Designed and shipped six production AI systems: The Orchestrator (multi-agent natural language CLI), PR Agent (automated code review via local LLMs), Context Core (persistent vector memory layer), The Autonomous Researcher (self-directed web research pipeline), Sentinel-Shield (enterprise AI security gateway), and Swarm-Tune (decentralized P2P distributed fine-tuning framework) — all built on local compute or open-weight models.
Built Swarm-Tune — a fully decentralised training mesh where libp2p nodes discover each other, extract raw PyTorch gradients without DDP, synchronise via weighted FedAvg with Sybil-resistant aggregation, and collectively fine-tune models inside a Docker-simulated 6-node network; includes a competition framework, HuggingFace Hub publishing, and a vanilla-JS metrics dashboard.
Built Sentinel-Shield to solve the enterprise Trust Gap: a security gateway with real-time data leak interception, gateway-level prompt injection defense, per-user budget enforcement, and permanent compliance audit trails targeting Finance and Healthcare.
Mastered Apple Silicon (Metal) GPU acceleration to run 8B parameter LLMs locally at $0 cloud cost; established CI/CD via GitHub Actions with multi-platform Docker image publishing and security-first credential redaction across all vector ingestion workflows.
Technical Skills
AI & Machine Learning
Security & Governance
Backend & Infrastructure
Hardware & Tools
Work
Production-grade systems built with local LLMs, autonomous agents, and real-world constraints.
A fully decentralized AI fine-tuning framework — libp2p nodes discover each other, exchange raw PyTorch gradients across the mesh, and collectively fine-tune models without any central server. 8 phases complete. 110 tests (unit + integration + chaos). v1.0.0 on Docker Hub (linux/amd64 + arm64).
An AI security gateway that sits between your app and OpenAI / Anthropic / Ollama — stripping PII, blocking prompt injections, scanning responses for leaks, enforcing rate limits, and emitting a full audit trail. Drop-in proxy, Docker-ready.
A natural language interface to control and orchestrate multiple AI agents from a single terminal. Think tmux meets AI — dispatch tasks, monitor pipelines, and collect results.
Automated code review agent that analyzes pull requests, suggests improvements, detects potential bugs, and generates meaningful commit messages using local LLMs.
A persistent memory layer for AI assistants. Stores, retrieves, and compresses conversation context using vector embeddings to give LLMs long-term memory without token bloat.
An AI agent that autonomously browses the web, synthesizes information, and produces structured research reports. Powered by Llama-3 via Ollama with a multi-step planning pipeline.
About
I'm an AI Systems Architect who designs and builds production-grade intelligent systems — from cloud-deployed LLM applications to autonomous agent pipelines and internal developer tooling.
My work spans OpenAI & Anthropic APIs, open-weight models, and multi-agent orchestration. I care about the full stack: architecture, reliability, and user-facing impact.
When I'm not shipping AI products, I'm deep in systems design, evaluating new model capabilities, or experimenting with agent reasoning frameworks.
Environment
Hardware and tooling that power day-to-day AI development and experimentation.
Machine
Apple M4 Pro
14-core CPU · 16-core GPU · 24 GB Unified Memory
LLM Runtime
Ollama
Llama-3.1 · Mistral · Phi-4
Primary Model
Llama-3.1 70B
Quantized Q4_K_M · Local inference
Daily Tools
Learning
Courses and programs actively shaping my technical foundation.