Lead ML / Data Science (IRC287885) $$$$

Job Description

• Master’s degree in Computer Science, Data Science, Applied Mathematics, or a related field.
• 7+ years of professional experience in machine learning, data science, or AI engineering.
• Proven experience as a technical lead or solution architect for ML/AI projects, with accountability for end-to-end delivery in a production environment.
• Strong proficiency in Python and the modern ML/AI ecosystem (e.g., PyTorch, Hugging Face, LangChain/LangGraph, Scikit-learn).
• Hands-on experience with data ingestion, RAG pipeline optimization, model evaluation, deployment (MLOps), and monitoring.

• Deep understanding of generative AI (LLMs, embeddings, RAG, prompt engineering, and agentic reasoning) and its practical constraints (latency, cost, safety, hallucinations).

   

Job Responsibilities

• Work with stakeholders to translate domain-specific knowledge into "Spec-Driven" ML architectures and agentic workflows.
• Design and implement solutions that combine the reasoning power of LLMs with the precision of structured knowledge (ontologies/knowledge graphs).
• Pilot the Claude Code CLI and other agentic tools to generate code, run automated tests, and maintain "Context Hygiene" within the project repository.
• Apply structured 4-phase debugging to ML pipelines, focusing on root-cause analysis of hallucinations, retrieval failures, and data drift.
• Define and automate "Skills" (prompt libraries, evaluation scripts, and deployment templates) to be re-used across multiple AI-Native pods.
• Define and implement quality gates, safety metrics, and cost-control practices for GenAI components.

• Lead by example in adopting AI-Native practices, mentoring the AI Apprentice and other team members in the "People + Agents" delivery model.

   

Department/Project Description

We are seeking a Senior ML / Data Science Lead to join our AI & Data practice and spearhead a next-generation initiative within our AI-Native Delivery Pod. In this role, you will combine high-end machine learning engineering with a revolutionary approach to software delivery. You will not just build AI; you will build AI using AI, orchestrating agentic workflows to deliver complex solutions at a velocity traditional teams cannot match.

You will focus on building practical, production-ready solutions that bridge the gap between unstructured data and structured logic using LLMs, agentic workflows, and knowledge representations (ontologies, knowledge graphs). You are expected to be an "AI-Native" pioneer, utilizing tools like Claude Code, Cursor, and custom MCP servers to automate your own development lifecycle—from data exploration and model evaluation to code generation and documentation.