About Workato

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 Workatos 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

Why join us

Ultimately Workato believes in fostering a flexible trust-oriented culture that empowers everyone to take full ownership of their roles. We are driven by innovation and looking for team players who want to actively build our company.

But we also believe in balancing productivity with self-care. Thats why we offer all of our employees a vibrant and dynamic work environment along with a multitude of benefits they can enjoy inside and outside of their work lives.

If this sounds right up your alley please submit an application. We look forward to getting to know you!

Also feel free to check out why:

• Business Insider named us an enterprise startup to bet your career on

• Forbes Cloud 100 recognized us as one of the top 100 private cloud companies in the world

• Deloitte Tech Fast 500 ranked us as the 17th fastest growing tech company in the Bay Area and 96th in North America

• Quartz ranked us the #1 best company for remote workers

Responsibilities

As Senior Engineering Manager for Enterprise Retrieval youll lead the team building the retrieval layer that grounds Workatos 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.

Youll own the teams 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. Youll spend your time on people technical direction and execution in roughly that order and youll 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:

• Lead grow and develop the Enterprise Retrieval team hiring coaching performance career growth and team culture across a mixed group of Software and AI Engineers (target team size 812).

• Set the technical direction for the retrieval layer in partnership with senior ICs balancing classical IR vector search RAG agent grounding and the operational realities of enterprise content.

• Own the roadmap and the outcomes translate company strategy into quarterly objectives scope crisply prioritize ruthlessly and ship measurable wins on retrieval quality latency freshness and cost.

• Partner across the org with Product the Agent Platform team Security Connectors and Infra your teams output is upstream of almost every AI agent at Workato.

• Build a culture of evaluation where retrieval quality faithfulness citation accuracy and end-to-end agent success are measured rigorously and improved deliberately.

• Raise the bar on craft code review standards design review rituals on-call discipline observability and a healthy evals before opinions instinct.

• Coach senior ICs on technical leadership helping Staff-track engineers grow into broader scope and Senior engineers grow into Staff.

• Recruit relentlessly attract assess and close strong Software and AI Engineers in a competitive market; build a deep diverse pipeline.

• Communicate clearly upward and outward make the teams strategy progress risks and decisions legible to engineering leadership and execs.

Requirements

Qualifications / Experience / Technical Skills

• Leadership Experience

  • 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 youve 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).

• Technical Background

  • 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 dont 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.

Soft Skills / Personal Characteristics

• 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.

Nice to Have

• 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.

(REQ ID: 2777)