Senior AI Engineer

At Reltio®, an SAP Company, we believe data should fuel your success in the enterprise AI era. Our  Context Intelligence Platform turns fragmented data into a trusted, connected context so AI agents and systems can act with expert-level judgement at enterprise scale. Reltio’s cloud-native SaaS platform harmonizes, unifies, and governs data across sources and formats—including unstructured data—in real time, turning them into data assets that can be mobilized in milliseconds to any application, user, or AI agent. Trusted by more than 200 of the world’s largest brands across industries such as life sciences, financial services, healthcare, and technology, we fuel frictionless operations and help enterprises accelerate  innovation and reduce risk.

At Reltio, our values guide everything we do. With an unyielding commitment to prioritizing our “Customer First”, we strive to ensure their success. We embrace our differences and are “Better Together” as One Reltio. We are always looking to “Simplify and Share” our knowledge when we collaborate to remove obstacles for each other. We hold ourselves accountable for our actions and outcomes and strive for excellence. We “Own It”. Every day, we innovate and evolve, so that today is “Always Better Than Yesterday”. If you share and embody these values, we invite you to join our team at Reltio and contribute to our mission of excellence.

Reltio has earned numerous awards and industry and analyst recognition for our technology, our culture and our people. Reltio was founded on a distributed workforce and offers flexible work arrangements to help our people manage their personal and professional lives. If you’re ready to work on unrivaled technology where your desire to be part of a collaborative team is met with a laser-focused mission to enable digital transformation with connected data, let’s talk!

Job Summary: 

We are building a highly specialized Enterprise AI Hub to engineer the "Reltio Brain" - a Context Intelligence Operating System that transforms fragmented institutional knowledge into governed, autonomous action. As the Sr. AI Engineer (Data Pipelines & Context Systems), you will build the governed data, context, and tooling layer that allows Reltio AI systems and embedded AI Business Partners to work safely across enterprise knowledge. This role is centered on data pipeline management, context organization, retrieval quality, and model/tool harnesses. You will connect structured and unstructured sources, maintain freshness and access controls, and create reusable MCP/API-style tools that let AI workflows use the right data with clear provenance. This is not primarily a front-end/UI role. You may build lightweight internal surfaces for review, administration, and workflow handoffs, but the center of gravity is the data and context foundation behind those experiences: ingestion, indexing, permissions, source trust, evaluation, and operational reliability.

Job Duties and Responsibilities:

AI Data Pipeline & Context Architecture

• Design and implement production-grade pipelines that ingest, normalize, enrich, and synchronize structured and unstructured enterprise data from sources such as Reltio/MDM, Google Workspace, Slack, Jira/Confluence, transcripts, product systems, and operational datasets.
• Build patterns for incremental indexing and vector updates so AI systems can refresh only what changed while preserving lineage, permissions, and source metadata.
• Model context boundaries across personal, team, departmental, and enterprise layers so AI systems understand where information came from, why it matters, and who can access it.

Governed MCP/API Tooling:

• Build secure MCP/API-style tools and services that expose enterprise data and actions to LLM workflows with clear schemas, guardrails, and audit trails.
• Implement OAuth/SSO, RBAC/ABAC, tenant boundaries, and server-side permission checks so retrieval and tool execution respect enterprise access controls.
• Create reusable connectors and adapters for AI Business Partner workflows, starting with pragmatic first versions that can be operated, handed off, and improved.

Retrieval, Indexing & Source Trust:

• Design RAG/retrieval systems that combine semantic search, keyword search, metadata filters, reranking, and structured queries to assemble reliable context.
• Define chunking, embedding, tagging, and provenance strategies that make responses traceable back to source documents, records, transcripts, or systems of record.
• Monitor freshness, data quality, duplicate or stale content, hallucination risk, and missing-context failure modes.

Model / Tool Harnesses & Evaluation:

• Build harnesses for testing prompts, retrieval pipelines, tool calls, structured outputs, and agentic workflows before they are used in production.
• Create evaluation datasets, acceptance criteria, observability, and regression checks for context quality, tool accuracy, latency, cost, and safety.
• Use modern AI coding assistants such as Codex, Claude Code, and Cursor to accelerate implementation while maintaining engineering discipline, security, and review standards.

Human-in-the-Loop Operations:

• Build review, approval, rollback, and exception-handling workflows for high-impact AI actions and sensitive data usage.
• Create admin and debugging tools that let operators inspect source context, tool decisions, access rules, and pipeline status.
• Partner with AI Business Partners, Security, Product, Data, and Engineering teams to translate business workflows into durable, governed AI capabilities.

Reusable Enterprise Patterns:

• Identify technical components that can be reused by Enterprise AI, Product, and partner teams, including connectors, context services, evaluation harnesses, and governance patterns.
• Document implementation patterns clearly enough for stakeholders and engineering teams to understand, operate, and extend them.
• Bootstrap first versions in partnership with existing P&T and Enterprise AI teams, then help define the path to ownership, scale, and product alignment.

Skills You Must Have:

• 5+ years of software, backend, data platform, or AI engineering experience building production data systems, internal platforms, or AI-enabled systems.
• Strong proficiency in Python and/or TypeScript/Node.js, with experience designing APIs, services, async jobs, data models, and integrations.
• Hands-on experience with data ingestion, transformation, synchronization, and operational data pipelines across multiple source systems.
• Practical experience with RAG/retrieval systems, including embeddings, vector/search stores, chunking, metadata filtering, hybrid search, reranking, citations/source provenance, and incremental updates.
• Experience building with LLM tool calling, agents, structured outputs, schema validation, and MCP-style or function/API-based tool layers.
• Strong understanding of enterprise identity, access control, and data governance, including OAuth/SSO, RBAC/ABAC, privacy, auditability, and secure handling of sensitive information.
• Ability to design evaluation harnesses and operational checks for retrieval quality, model/tool accuracy, latency, cost, freshness, and regression risk.
• Strong judgment with AI-assisted development tools such as Codex, Claude Code, and Cursor, using them to accelerate delivery while validating generated code and decisions.
• Clear communication with technical and business stakeholders; able to explain data flow, context quality, risk, and tradeoffs in practical language.

Skills That Are Nice to Have:

• Familiarity with Reltio, MDM, master data management, data governance, knowledge graphs, or customer/product data domains.
• Experience with enterprise search/vector infrastructure such as OpenSearch, Pinecone, pgvector, Bedrock Knowledge Bases, or similar platforms.
• Experience integrating with Google Workspace, Slack, Jira, Confluence, Salesforce, NetSuite, data warehouses, or other enterprise platforms.
• Front-end experience with React, Next.js, Vercel, or internal admin/review tools; enough to build simple surfaces that expose pipeline state and support human review.
• Experience with workflow orchestration, approval systems, event-driven architectures, queues, batch/stream processing, Docker/Kubernetes, infrastructure as code, or observability stacks.
• Experience in a 500-2,000 employee SaaS company or similar scale.