Group Lead, AI & MLE Ops

Job description

Data, AI & Digital Innovation — Data Science, AI Engineering, ML Engineering & MLOps Key Partners GenAI & ML leadership in Chicago and Toronto; offshore consulting partners Lead Data Science, AI Engineering, ML Engineering & MLOps from the GCC: own applied delivery of data science, AI and ML solutions built on the approved enterprise stack (Azure ML, Azure OpenAI Service, Snowflake Cortex, Copilot Studio).

Lead Forward-Deployed Engineering (FDE) for GenAI delivery

run the FDE model — data scientists and engineers embedded directly with business teams to turn their problems into working GenAI solutions — owning how FDEs are assigned, how they engage, and the quality of what they ship.

Mentor and grow the team

hire, coach, and develop data scientists, AI/ML engineers in Bangalore; set the technical bar and build future leads.

Create the delivery framework

define how work moves from intake to production — standards for scoping, code quality, CI/CD, testing, evaluation, and release — so delivery is repeatable and predictable.

Manage delivery

own commitments on scope, dates, quality, and cost; track progress, surface risks early, and course-correct visibly.

Ensure the ML Platform and its SLAs

keep the enterprise AI/ML platform and pipelines reliable — MLOps, CI/CD, monitoring, incident response, cost management — and define, enforce, and report against SLAs for availability, support response, and delivery turnaround.

Liaise with consulting partners

manage offshore consulting partners — onboarding, work allocation, quality review, and accountability for their deliverables. Hands-on experience delivering Data science, GenAI and ML solutions to production, ideally on Azure (Azure ML, Azure OpenAI Service, AKS, Azure DevOps).

Strong MLOps background

containerization, CI/CD, deployment, monitoring, and reliability for ML/LLM systems. Experience with agentic/LLM application development (LangGraph or comparable frameworks) and strong production-quality Python.

Proven people leadership

hiring, mentoring, and growing data scientists and engineers; comfortable operating in a forward-deployed model alongside business teams. Experience setting up delivery frameworks, SLAs, or operating processes for data science and engineering team. Experience managing vendors or consulting partners and holding them accountable for quality. Clear communicator, comfortable working across time zones with global leadership and non-technical stakeholders.

Experience in a GCC or hub-and-spoke operating model. CPG, retail, or other large-enterprise background. Experience with Snowflake Cortex, semantic views, and text-to-SQL agents in an enterprise setting. Experience implementing responsible AI practices (prompt injection defenses, PII handling, grounded-ness or hallucination prevention) in a brand-sensitive environment.