Senior AI Engineer
Job description
About the role
You architect how agents navigate content and context, owning the end to end design of the retrieval pipeline that transforms scattered enterprise data into one precise answer. You ensure complex workflows feel simple for the user by balancing relevance, speed, and governance at every step. Your decisions directly shape how deeply agents can reason over documents, tickets, and records while maintaining strict alignment with business rules. You partner closely with product and engineering teams to turn ambiguous questions about search and summarization into concrete, testable retrieval strategies. You lead experiments that compare classical and semantic methods, measuring quality, freshness, and permission correctness for every change. You build and maintain interfaces that let external partner systems plug safely into governed workflows without compromising control. You act as a quality champion, implementing tracing and instrumentation that exposes every stage of the data journey for analysis and improvement.
Key facts
What you'll do
Design intake pipelines that normalize documents, tickets, and records into searchable structures with consistent metadata and clean schema mapping.
Orchestrate build processes that fuse vector indexes, graph relationships, and rule based logic into a unified hybrid retrieval architecture.
Implement review mechanisms that validate answer relevance, data freshness, and permission alignment before responses leave the system.
Coordinate ship routines that deliver ranked, context aware responses to agents, balancing recall, precision, and latency targets.
Establish partner interfaces that let external systems plug into governed workflows using standardized protocols and secure handshakes.
Champion tracing and instrumentation across every stage of the data journey, collecting signals from ingestion to response delivery.
Translate ambiguous requirements into concrete retrieval strategies, defining evaluation criteria, test queries, and success thresholds.
Guard quality by running controlled experiments that compare classical and semantic approaches under identical conditions and data slices.
Define operational playbooks for monitoring drift, handling edge cases, and updating indexes as source systems evolve.
Drive clarity for users by designing feedback loops that explain why a particular answer was returned and how to refine future queries.
Contribute to platform level patterns that make retrieval components reusable across products, reducing duplication and accelerating new use cases.
Collaborate with product managers and engineers to prioritize features that improve trust, transparency, and usability in agent interactions.
Support on call rotations to investigate production incidents, diagnose retrieval regressions, and guide short term and long term fixes.
Represent the retrieval discipline in cross functional discussions, ensuring that data quality, security, and performance constraints are respected.
Requirements
You hold a degree in computer science, mathematics, or a related field that demonstrates strong analytical foundations.
You bring 3-5 years of experience building search or retrieval systems, with a track record of shipped solutions in production environments.
You can read and translate documentation across heterogeneous sources, including APIs, databases, and distributed protocols.
You communicate clearly with both engineers and domain experts, explaining technical trade offs without relying on unnecessary jargon.
You are comfortable working with ambiguity and turning high level goals into concrete implementation plans and measurable experiments.
You have a strong grasp of information retrieval concepts, including ranking, recall, precision, and the limitations of different modeling approaches.
You understand the implications of security and permissions in multi tenant scenarios, and you design retrieval flows that respect data boundaries.
You care about maintainability, observability, and performance, and you use data to guide decisions rather than intuition alone.
Nice to have
Experience with enterprise content, CRM, or ticketing ecosystems, including how metadata, attachments, and versions are modeled.
Skills & tools
Python, TypeScript, Elasticsearch, vector databases, graph engines, MCP, Workato platform.
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, 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.
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. That's 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.
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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 number one best company for remote workers.
About the company
Workato connects apps and data so teams can automate workflows and govern AI agents. The platform coordinates actions across thousands of integrations, handling security, routing, and logic.
People build recipes that move data between systems, define policies for AI execution, and respond to alerts. Roles include engineers, product managers, and designers focused on reliability and clarity in how integrations work.