Lead AI Engineer/ Data Scientist

• Experience in agentic frameworks and protocols (e.g., LangGraph, LlamaIndex, AutoGen, CrewAI, MCP) and with RAG and tool-use patterns.
• Familiarity with MLOps tooling (experiment tracking, CI/CD for ML, model registries, monitoring).
• Experience with distributed training and inference optimization (quantization, batching, GPU utilization).
• Exposure to containerization and orchestration (Docker, Kubernetes).

Technical Frameworks & Toolkit:

Deep Learning frameworks: PyTorch, TensorFlow, Keras, JAX; PyTorch Lightning.

GenAI & fine-tuning frameworks: Hugging Face Transformers, PEFT (LoRA/QLoRA), TRL, Accelerate, DeepSpeed, bitsandbytes, Axolotl, Unsloth; vLLM / TGI / Ollama for serving; LangChain, LlamaIndex for RAG and orchestration.

Agentic AI frameworks & protocols: Claude Agent SDK, Anthropic / OpenAI SDKs, LangGraph, AutoGen, CrewAI, Semantic Kernel, and the Model Context Protocol (MCP); tool/function calling and multi-agent patterns.

Computer vision: OpenCV, Detectron2, Segment Anything (SAM); image/video pipelines.

Forecasting & optimization: stats models, Prophet, GluonTS, Darts, scikit-learn; optimization/solver tooling (e.g., OR-Tools, SciPy, PuLP, Gurobi/CVXPY).

MLOps & infra: experiment tracking (MLflow / Weights & Biases), Docker, Kubernetes, CI/CD for ML, model registries and monitoring.