You will lead the engineering function for Source.AI, McKinsey's GenAI-powered, agentic category-management and sourcing platform.
You will set and drive the end-to-end technical vision across data engineering, data science, and full-stack software development, ensuring the platform is designed, built, and scaled to meet the needs of enterprise procurement clients globally.
You will own technical direction for a multi-surface product stack: AWS based, a NestJS and Next.js monorepo (Nx, pnpm), Python FastAPI microservices, per-client Snowflake data tenants, and an agentic AI layer spanning spend intelligence, market radar, category strategy, negotiation coaching, and tail-spend repricing. You will lead a team of data engineers, data scientists, and full-stack developers, partnering with product leadership and delivery leads to ship the roadmap while maintaining delivery SLAs on live client deployments.
You will architect and operationalize GenAI capabilities at production scale, including retrieval-augmented generation (RAG), NL2SQL, prompt engineering, vector search, and multi-agent orchestration, all routed through McKinsey's enterprise AI Gateway with Responsible-AI guardrails. You will establish engineering best practices, AI-assisted SDLC workflows, and observability standards so the team ships secure, reliable, and governable AI-driven features on a continuous cadence.
You will play a critical role in scaling Source.AI from a proven PoC portfolio to an enterprise-grade SaaS platform, driving transformative impact by enabling intelligent, scalable, and secure agent-based technologies that unlock measurable savings, productivity, and risk visibility for procurement teams worldwide.