# Senior Manager, AI Engineering

**Company**: Adobe
**Location**: Bangalore
**Experience**: senior
**Job type**: full-time
**Category**: Engineering
**Industry**: Technology

**Apply**: https://adobe.wd5.myworkdayjobs.com/en-US/external_experienced/job/Bangalore/Sr-Mgr--Software-Development_R172115-1?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_a319d829-220

## 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.

## Skills

### Required
- Python
- LLM orchestration
- RAG
- MCP/tooling
- cloud-native deploys
- agentic AI
- LLM systems
- AI/ML engineering

### Nice to have
- Databricks
- vector databases
- graph stores
- Neo4j
- agent/LLM frameworks
- MCP
- prompt/eval tooling
- LLM cost governance

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Source: [Apply at adobe.wd5.myworkdayjobs.com](https://adobe.wd5.myworkdayjobs.com/en-US/external_experienced/job/Bangalore/Sr-Mgr--Software-Development_R172115-1?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)