Lead AI / ML Engineer
Building production ML/AI systems that move from research to scalable deployment.
I help teams turn AI ideas into reliable, high-impact products with measurable outcomes.
- Distributed training for SLMs, LLMs and LVLMs
- High-throughput, cost-efficient inference at scale
- Data-centric AI pipelines with production-grade observability
- Production-ready agentic AI and evaluation workflows
Focus Areas
ML/AI Systems + MLOps
SLM's, LLMs, multimodal, CV, NLP, Agentic AI
Model serving, distributed training, evaluation frameworks, scalability, inference optimization
Faster iteration cycles and production-grade performance
AI vs. Human ModeratorsRead Paper ↗
LLM Performance PredictorsRead Paper ↗
10+
Years in ML & AI Research and Engineering
2–3x
Inference Performance Gains
~50%
ML Infra Cost Reduction
Published
ICCV and AAMAS
Weeks → Hours
Governed pipelines and data workflows that accelerate model iteration
6–10%
Use-case specific model gains through architecture tradeoffs and training pipeline design
Capabilities
What I Work With
Weighted from public GitHub activity with recency, stars, and topic signals.
Projects
Personal Work
A glimpse into how I spend my personal time building, exploring, and learning.
Lumis Doc — Agentic Document Chat Engine
OngoingAn agentic document Q&A system built on a 13-node LangGraph DAG. Routes each query through intent classification, multi-source retrieval (vector search, table extraction, web), and iterative refinement — with human-in-the-loop review before answering. Responses include source citations, chain-of-thought reasoning, and per-session cost tracking.
Self-Driving Vehicle
AccomplishedPerception and control modules for autonomous vehicles built on the Udacity SDC curriculum. Covers lane detection, traffic sign classification, behavioral cloning, LIDAR/RADAR sensor fusion via Extended Kalman Filter, jerk-minimizing path planning, and a PID controller for steering and throttle.
Deep Reinforcement Learning
AccomplishedCore Deep RL algorithms implemented across Unity ML-Agents environments. Covers DQN and Double-DQN for discrete action spaces, REINFORCE for Atari Pong, DDPG for continuous robotic arm control, and Multi-Agent DDPG for a collaborative/competitive multi-agent setting.
Writing
Latest Posts
Recent articles on practical AI engineering and production model workflows.