Sr. Manager, Data Engineering

Overview

Lead a blended AI engineering team building an agentic AI platform and marketing solutions on top of it. Own roadmap, execution, reliability, quality, people growth and cross-functional delivery for production AI systems used by marketing and analytics teams.

What you'll do

• Own delivery and reliability of the agentic AI platform, including LLM orchestration, retrieval/RAG pipelines, tool and MCP integration, model routing and evaluation.
• Drive engineering quality through test discipline, deployment safety, observability, cost management and latency management.
• Set technical direction with senior engineers, make architecture decisions and unblock the team through design-level involvement.
• Lead the team building AI agents and workflows for analytics, paid media, content-to-intent and executive reporting marketing use cases.
• Ensure solutions deliver business outcomes and are adopted by stakeholders.
• Balance platform investment against solution delivery.
• Manage, coach and grow a blended team.
• Run hiring to build out the function.
• Own the roadmap and quarterly planning, converting ambiguous priorities into committed, sequenced delivery.
• Represent the team's work to leadership and cross-functional partners.
• Drive alignment across a globally distributed AI organization.

What you'll need

• 12+ years in software / AI/ML engineering.
• 4+ years managing engineering teams, including hiring and growing engineers.
• Demonstrated depth in agentic AI / LLM systems, including orchestration, retrieval/RAG, tool use, agent frameworks and evaluation of AI quality.
• Track record of shipping production AI/ML systems at scale, including reliability, deployment safety and cost/latency awareness, not just prototypes.
• Strong Python and modern cloud-native / containerized delivery.
• Ability to set a high engineering bar and make sound architecture decisions while leading primarily through the team.
• Excellent communication and stakeholder management across a globally distributed organization.

Nice to have

• Marketing technology or analytics domain experience, including AEP / AJO / CJA, adtech or digital marketing analytics.
• Experience with Databricks, vector databases such as pgvector, graph stores such as Neo4j, or similar data/AI infrastructure.
• Hands-on experience with agent/LLM frameworks and MCP, prompt/eval tooling and LLM cost governance.
• Experience standing up or scaling a new AI engineering function.

Details

• Location: Bangalore.
• The team is globally distributed and operates across a global AI organization.