Tech Lead & AI/ML Engineer building practical AI systems to solve real-world engineering problems.

I work across Document AI, LLMs, VLMs, NLP, Agentic AI, and AI infrastructure — from model experimentation and evaluation to architecture, production engineering, deployment, and optimization.

• Document AI, OCR & VLMs
• LLM & Agentic AI Engineering
• NLP & Machine Learning
• AI/LLM Evaluation
• Model Serving & Inference Optimization
• Scalable AI Systems & Architecture
• Open Source

AI / ML:
PyTorch · Hugging Face · Transformers · BERT · LLMs · VLMs · OCR · LoRA / QLoRA · Synthetic Data

LLM / Agents:
OpenAI API · Prompt Engineering · Structured Outputs · LangGraph · Agentic AI

Document AI:
Docling · Chandra OCR · Document Understanding · Table Understanding

AI Infrastructure:
vLLM · LiteLLM · AWS EC2 · Auto Scaling · Docker · Docker Swarm

Observability:
Prometheus · Grafana · AI/LLM Inference Metrics

Backend & Distributed Systems:
Python · Flask · Kafka · RabbitMQ · MongoDB · MySQL · Linux · Bash

Contributor to the Docling ecosystem, working with the Docling and Chandra codebases and contributing improvements around Document AI and VLM/OCR integration.

Applying AI/ML to real-world engineering problems while exploring:

• Retrieval-Augmented Generation (RAG)
• Vector databases & retrieval systems
• Intelligent model routing & load balancing
• Advanced inference optimization
• Production-grade agentic AI systems

I enjoy understanding systems deeply, experimenting with different approaches, measuring what works, and turning those findings into reliable and scalable engineering solutions.

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