Senior Engineering Manager (Search and Retrieval)

Workato delivers enterprise infrastructure for the agentic era, redefining iPaaS and helping enterprises unify data, applications, processes, and AI into a single, governed platform. A leader in Enterprise MCP and trusted by 50% of the Fortune 500, Workato’s cloud-native architecture connects every application, data source, and process to power real-time orchestration at scale. With enterprise-grade security and continuous innovation at its core, Workato provides the trusted foundation for organizations to automate with confidence and operationalize AI across the business. To learn more, visit www.workato.com

As Senior Engineering Manager for Enterprise Retrieval, you’ll lead the team building the retrieval layer that grounds Workato’s enterprise AI agents. Your team is a deliberate mix of disciplines — Software Engineers who own the systems side (multi-source connectors, ingestion and freshness pipelines, permission-aware indexing, hybrid retrieval at scale) and AI Engineers who own the applied AI side (embeddings, RAG quality, re-rankers, LLM-driven query understanding, eval rigor). Your job is to make those two crafts compound into a single, world-class team.

You’ll own the team’s charter, roadmap, and outcomes — partnering with Product, the Agent Platform team, Security, and leadership to figure out what to build, then building a team that ships it. You’ll spend your time on people, technical direction, and execution — in roughly that order — and you’ll keep just enough hands on the architecture to be a credible technical partner to your engineers.

This is a senior management role for someone who has led mixed AI/systems teams before, knows what good looks like in both crafts, and wants to put that experience to work on one of the most consequential infrastructure problems in enterprise AI today.

In this role, you will also be responsible to:

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• 5+ years of engineering management experience, including 2+ years managing managers or running teams of 8+ engineers.

• Demonstrated track record of shipping non-trivial systems to production — ideally in search/retrieval, applied ML/AI, data platforms, or developer infrastructure.

• Experience leading mixed teams of software engineers and applied AI / ML engineers, and getting the two disciplines to compound rather than collide.

• Strong hiring track record — you’ve built teams from scratch or grown them substantially, and you know how to assess both systems and AI engineering talent.

• Comfort operating with ambiguity, shaping strategy, and partnering with Product on what to build (not just how).

• Hands-on engineering background earlier in your career; you can read code, lead a design review, and push back on architecture decisions credibly.

• Working understanding of modern retrieval and applied AI: hybrid search (BM25 + dense vectors), embeddings, RAG, re-rankers, LLM evaluation, and agent grounding. You don’t need to be the deepest expert — but you need to know what good looks like and what trade-offs to push on.

• Familiarity with the surrounding stack: distributed systems, search engines (OpenSearch / Elasticsearch / Solr / Vespa), vector stores, cloud platforms (AWS, GCP, or Azure), CI/CD, and observability.

• Intuition for enterprise data realities — heterogeneous content, ACL/permission models, freshness constraints, and the governance bar that enterprise security teams expect.

• People-first: you genuinely care about the engineers you manage — their growth, their craft, and their experience on the team.

• Outcome-oriented: you optimize for shipped impact, not activity — and hold the team to measurable wins.

• Empirical: you reach for evals, data, and clear metrics before opinions; you model that behavior for the team.

• Direct and kind: you give clear feedback early, address performance issues with care, and build trust by being predictable.

• Strong communicator who can move fluently between an IC technical conversation, a product trade-off discussion, and an exec update.

• Calm under ambiguity — comfortable making decisions with incomplete information and adjusting as you learn.

• Experience leading a search, retrieval, knowledge platform, or RAG team in a previous role.

• Experience managing in an enterprise SaaS, integration, or AI infrastructure context.

• Background in applied ML/AI yourself (researcher, ML engineer, or applied scientist before moving into management).

• Experience with agentic AI patterns — tool use, function calling, MCP, multi-step retrieval planning.

• History of public technical communication — talks, posts, papers — on retrieval, applied AI, or engineering leadership.