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.
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