Description
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.
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.