Team Lead - AI / ML

Lead a small, high-velocity team to design, build, and ship ML and GenAI products end-to-end, from problem framing and modeling to scalable deployment and monitoring.

Responsibilities:

• Own the technical roadmap for ML/GenAI initiatives and align it with product goals and delivery timelines.
• Lead LLM workflow and agentic system design, including retrieval, tool-use, and evaluation pipelines.
• Guide model development using PyTorch and HuggingFace; enforce code quality, testing, and model evaluation standards.
• Oversee MLOps: CI/CD for models, API design, containerization, orchestration, and cost/performance optimization on AWS.
• Collaborate with product, data, and infra teams; run experiments, A/B tests, and error analysis.
• Mentor engineers, conduct code/model reviews, and grow team capabilities.

Requirements:

• At least 5 years of experience with a minimum of one year in technical leadership leading projects or teams.
• General AI/ML Team Lead requirements: roadmap ownership, sprint planning, stakeholder communication, mentoring, and setting engineering best practices.
• Strong Python understanding: idiomatic code, testing, packaging, async I/O, performance profiling.
• Strong ML, DL, and GenAI understanding: supervised/unsupervised learning, transformer architectures, prompt engineering, evaluation, and safety/guardrails.
• Experience building LLM-based workflows and agents, including retrieval (RAG), tools, memory, and evaluation loops.
• Hands-on experience with LangChain, HuggingFace, PyTorch, Neo4j, Milvus, and AWS for scalable, production-grade systems.
• MLOps experience: model deployments, API development, observability, infra management (containers, orchestration, IaC), and data/version governance.

Preferred Qualifications:

• Knowledge graph design and Neo4j schema modeling for semantic retrieval and reasoning.
• LLM fine-tuning with deployment to production: LoRA/QLoRA, PEFT, quantization, evaluation, rollback strategies, and cost optimization.
• Startup experience: comfort with ambiguity, rapid iteration, and owning outcomes across the stack.