Technical Lead - AI/ML

Key Responsibilities

• Own end-to-end technical design and delivery of enterprise-grade agentic AI systems from architecture through production, reliability, and handover.
• Lead a small engineering pod: set technical direction, run design and code reviews, mentor engineers, and remain hands-on writing production code yourself.
• Translate ambiguous business problems in commerce, fulfillment, and supply chain into well-scoped agentic workflows with clear success metrics and guardrails.
• Architect multi-agent pipelines with tool/function calling, retrieval (RAG), memory, orchestration, evaluation, and human-in-the-loop controls.
• Establish engineering standards for agent evaluation, observability, safety, cost, and latency, and drive them across the pod.
• Partner directly with client stakeholders and SMEs leading discovery, shaping solution architecture, and presenting trade-offs to technical and executive audiences.
• Build and deploy Model Context Protocol (MCP) servers and reusable tools/integrations that let agents act safely across enterprise systems (OMS, WMS, CRM, data and commerce platforms).
• Make pragmatic build-vs-buy and framework decisions and collaborate cross-functionally on requirements, sprint planning, and delivery.
• Lead the optimization phase: design optimization-based decisioning for inventory planning and warehouse slotting, integrating solvers and forecasts into agentic workflows.

Key Skills

Leadership & Domain Expertise

• 8 12 years in software/AI engineering, including 2+ years leading teams or owning technical delivery as a hands-on lead.
• Demonstrated domain exposure in commerce/retail, e-commerce, fulfillment, logistics, or supply chain able to engage client SMEs and architect solutions.
• Track record shipping AI/ML systems to production in enterprise or client-services settings; consulting or customer-facing delivery experience strongly preferred.
• Strong communication and presence: can lead discovery workshops, present architecture and trade-offs to executives, and mentor engineers.

Agentic AI & Enterprise Pipelines

• Proven, hands-on experience designing and shipping enterprise-grade agentic AI systems to production.
• Deep expertise with agent frameworks (LangGraph, LangChain, LlamaIndex, AutoGen, CrewAI, or equivalent) and orchestrating single- and multi-agent workflows.
• Expert LLM integration: function/tool calling, structured outputs, retrieval-augmented generation (RAG), agent memory, and knowledge retrieval.
• Production hardening of agentic systems: agent evaluation and regression testing, guardrails and safety, observability/tracing, prompt and context management, and cost/latency optimization.
• Model Context Protocol (MCP): designing and deploying MCP servers for tool and resource integration.
• Human-in-the-loop and approval workflows for high-stakes autonomous actions in enterprise environments.

Core Technical & Backend

• Expert-level Python with strong software-engineering fundamentals, design, and code quality.
• Strong with FastAPI (and/or Django) for high-performance APIs and services; asynchronous programming (asyncio, async/await).
• Solid ML foundations: scikit-learn, pandas, NumPy; familiarity with deep-learning frameworks (PyTorch/TensorFlow/Keras).
• API design and documentation (OpenAPI/Swagger), web security and authentication (JWT, OAuth), and testing/TDD.
• Data stores and messaging: relational and NoSQL databases; message queues and event streaming (Apache Kafka, RabbitMQ).

Cloud, MLOps & Delivery

• Production experience on a major cloud (AWS, Google Cloud Platform, or Azure) and its AI/ML services.
• Containerization (Docker) and orchestration; CI/CD for building, testing, and deploying applications.
• Vector databases and RAG infrastructure; LLMOps / observability tooling (e.g., LangSmith or equivalent).
• Git and collaborative, agile development at scale.

Nice to Have

• OMS/WMS or supply-chain platform experience (order management, warehouse management, transportation/parcel).
• Exposure to marketing/martech or finance-operations automation.
• Open-source contributions to agent/LLM tooling, or experience standing up an agentic platform or reusable framework.
• Working knowledge of mathematical / operations-research optimization: linear and integer programming (LP/MILP), constraint programming, and heuristic/metaheuristic methods.
• Hands-on with optimization solvers/libraries such as Google OR-Tools, Gurobi, CPLEX, or PuLP/Pyomo.
• Ability to model real-world supply-chain problems inventory placement/replenishment and warehouse slotting and embed optimization into agentic decisioning.

Familiarity with demand forecasting and connecting predictive models to optimization and downstream automated action.