AI Engineer

Workato delivers enterprise infrastructure for the agentic era, redefining iPaaS and helping enterprises unify data, applications, processes, and AI into a single, governed platform. Responsibilities: As a Senior AI Engineer on our Enterprise Retrieval team, you’ll help build the retrieval layer that powers enterprise AI agents at Workato. Your work will let an agent answer “what’s the status of the Acme renewal?” by stitching together a Salesforce opportunity, a call summary, the latest Zendesk ticket, a Jira blocker, and a SharePoint contract — all in one ranked, permission-aware response. In this role, you will also be responsible to: - Build a unified retrieval layer across enterprise systems — Google Drive, SharePoint, Confluence, Jira, Asana, Zendesk, Freshdesk, Salesforce, Notion, and more. - Design hybrid retrieval pipelines that combine lexical (BM25), dense vector, and structured (SQL/graph) retrieval. - Engineer ingestion and freshness pipelines that incrementally sync millions of documents, tickets, tasks, and CRM records. - Own permission-aware retrieval (ACL preservation). - Build query understanding for agents — intent parsing, entity linking across systems, and LLM-assisted query rewriting. - Design chunking and embedding strategies tailored to each content type. - Build evaluation and experimentation harnesses (NDCG, MRR, recall@k, faithfulness, citation accuracy). - Ship production-grade, observable systems with strong SLOs on latency, freshness, recall, and cost. - Mentor teammates and raise the bar on retrieval architecture. Requirements: - 3-5 years building production search, retrieval, knowledge-base, or recommendation systems. - Strong proficiency in at least one modern backend language — Python, Go, Java, or similar. - Hands-on experience with search engines such as OpenSearch, Elasticsearch, Solr, or Vespa. - Solid grounding in IR fundamentals: TF-IDF, BM25, learning-to-rank, query parsing, and relevance evaluation. - Working experience with vector search and embeddings — FAISS, pgvector, Pinecone, Weaviate, Qdrant, Milvus, or native Elasticsearch/OpenSearch kNN. - Experience designing or contributing to RAG pipelines and semantic search systems in production. - Familiarity with modern NLP/LLM tooling: transformer embeddings, cross-encoder re-rankers, prompt engineering, and frameworks like LangChain, LlamaIndex, or Haystack. - Comfortable building integrations against SaaS APIs (REST/GraphQL/webhooks), handling OAuth, rate limits, pagination, and incremental sync. - Solid intuition for ACL/permission models in enterprise systems. - Strong SQL skills, comfort with NoSQL/document stores, and experience with large-scale distributed systems. - Familiarity with cloud platforms (AWS, GCP, or Azure), containerization, and CI/CD. Nice to Have: - Experience with knowledge graphs, entity resolution, or cross-source identity linking. - Experience tuning or fine-tuning embedding models. - Exposure to agentic AI patterns — tool use, function calling, MCP, or multi-step retrieval planning. - Experience with streaming/real-time ingestion (Kafka, Flink, Spark). - Background in enterprise search, e-discovery, observability, or DLP. - Open-source contributions, published research, or writing on retrieval, IR, or applied ML.