Lead Data Scientist

Key Responsibilities

Solution Design & Technical Leadership:

• Design end-to-end ML/LLM and agentic solutions, from problem framing and data strategy through to deployment and monitoring.

• Architect agentic systems: multi-step, tool-using, and multi-agent workflows: including orchestration, tool/function integration, memory, and guardrails.

• Own the technical architecture for solutions: model selection, fine-tuning approach, agent/orchestration design, serving strategy, API design, and infrastructure footprint.

• Lead and mentor a team of data scientists and developers, break complex, ambiguous customer requirements into structured project plans with clear milestones and deliverables.

AI/ML Engineering & Modeling:

• Apply strong algorithmic fundamentals to select, adapt, and implement the right approach for each problem: classic ML, deep learning, or generative/agentic AI.

• Build deep learning models on unstructured data (images, video, text, audio, sensor/time-series) for real-world production use.

• Design and ship computer vision solutions (detection, classification, segmentation, OCR, tracking, etc.) at production quality and scale.

• Develop forecasting models (time-series and demand/behavioral forecasting) and integrate them into decisioning workflows.

• Work with foundation models including Claude and other LLMs: prompting, fine-tuning, evaluation, and integration.

Required Qualifications:

• Bachelor's & Master's degree in Computer Science, Data Science, Machine Learning, or a related field (or equivalent practical experience).

• 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.