Lead AI Engineer Data Scientist

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:

Bachelors & Masters 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 Eligible to workimization (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; LanEligible to workhain 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 & Eligible to workimization: stats models Prophet GluonTS Darts scikit-learn; Eligible to workimization/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.