Group Lead, AI & MLE Ops

About the role

Kraft Heinz is looking for a Group Lead for AI and Machine Learning Engineering and MLOps in Bengaluru. The role leads the data science, AI engineering, ML engineering and MLOps function, including a forward-deployed engineering (FDE) model where data scientists and engineers work directly with business teams to build GenAI solutions.

Key responsibilities

• Own the applied delivery of data science, AI and ML solutions on the approved enterprise stack (Azure ML, Azure OpenAI Service, Snowflake Cortex, Copilot Studio)
• Run the FDE model: assign data scientists and engineers, manage their engagement and the quality of what they ship
• Hire, coach and develop data scientists and AI/ML engineers in Bangalore, and mentor future team leads
• Create the delivery framework: standards for scoping, code quality, CI/CD, testing, evaluation and release
• Manage delivery commitments on scope, dates, quality and cost, tracking progress and surfacing risks early
• Keep the ML platform and its SLAs reliable: MLOps, CI/CD, monitoring, incident response and cost management
• Liaise with offshore consulting partners on onboarding, work allocation, quality review and accountability

Required profile

• Hands-on experience delivering data science, GenAI and ML solutions to production, ideally on Azure
• Proven people leadership: hiring, mentoring and growing data scientists and engineers
• Experience setting up delivery frameworks, SLAs or operating processes for data science and engineering teams
• Experience managing vendors or consulting partners and holding them accountable for quality
• Clear communicator, comfortable across time zones with global leadership and non-technical stakeholders
• Experience in a GCC or hub-and-spoke operating model, and a CPG, retail or large-enterprise background

Required skills

• Strong MLOps background: containerization, CI/CD, deployment, monitoring and reliability for ML/LLM systems
• Agentic/LLM application development (LangGraph or comparable frameworks) and strong production-quality Python
• Snowflake Cortex, semantic views and text-to-SQL agents in an enterprise setting
• Responsible AI practices such as prompt injection defenses, PII handling and hallucination prevention