Role Summary
We're looking for a Senior Manager to lead a blended AI engineering team building the agentic AI platform and the marketing solutions that run on top of it. This is a delivery-leadership role: you own the roadmap, execution, and quality bar for a team of platform engineers, applied-AI solutions builders, and data engineering — turning cutting-edge agentic AI into reliable, production systems that marketing and analytics teams depend on every day.
You will operate at the intersection of platform and product: hardening the agentic infrastructure (LLM orchestration, retrieval, tool/MCP integration, deploys, reliability) while making sure the AI solutions built on it deliver real marketing outcomes. You'll grow the people on your team, set a high engineering bar, and partner across a global AI organization.
What You'll Do
Platform & infrastructure
Own delivery and reliability of the agentic AI platform: LLM orchestration, retrieval/RAG pipelines, tool and MCP integration, model routing, and evaluation.
Drive engineering quality — test discipline, deployment safety, observability, cost and latency management — across everything the team ships.
Set technical direction with your senior engineers; make the hard architecture calls and unblock the team (design-level involvement; not expected to write production code day-to-day).
Applied AI solutions
Lead the team that builds AI agents and workflows for marketing use cases (analytics, paid media, content-to-intent, executive reporting) on top of the platform.
Ensure solutions are grounded in real business outcomes and adopted by stakeholders — not demos that stall.
Balance platform investment against solution delivery so both advance.
People leadership & delivery
Manage, coach, and grow a blended team; run hiring to build out the function.
Own the roadmap and quarterly planning; convert 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.
The Team You'll Lead
A blended engineering team of:
Agentic AI / platform engineers — Python, LLM orchestration, RAG, MCP/tooling, cloud-native deploys.
Applied-AI solutions builders — wiring AI to marketing and analytics use cases.
Data engineering — the pipelines and semantic layer the AI grounds on.
Must-Have Qualifications
~12+ years in software / AI/ML engineering, with 4+ years managing engineering teams (including hiring and growing engineers).
Demonstrated depth in agentic AI / LLM systems — orchestration, retrieval/RAG, tool use, agent frameworks, and evaluation of AI quality.
Track record of shipping production AI/ML systems at scale — reliability, deployment safety, 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.
Preferred Qualifications
Marketing technology or analytics domain experience — AEP / AJO / CJA, adtech, or digital marketing analytics.
Experience with Databricks, vector databases (pgvector), graph stores (Neo4j), or similar data/AI infrastructure.
Hands-on with agent/LLM frameworks and MCP, prompt/eval tooling, and LLM cost governance.
Experience standing up or scaling a new AI engineering function.
What Success Looks Like (First 6–12 Months)
A team operating with a clear roadmap, predictable delivery, and a visibly higher quality/reliability bar.
At least one AI solution moved from concept to in-production use, adopted by marketing/analytics stakeholders.
Platform reliability, evaluation, and cost/observability practices established as the default, not the exception.
Key hires made; team members growing and taking on larger scope.
Leadership Behaviors
Candor and clarity — lead with the bottom line, disagree…