Manager, Engineering - AI

At Reltio®, we believe data should fuel business success. Reltio’s AI-powered data unification and management capabilities—encompassing entity resolution, multi-domain master data management (MDM), and data products—transform siloed data from disparate sources into unified, trusted, and interoperable data. Reltio Data Cloud™ delivers interoperable data where and when it's needed, empowering data and analytics leaders with unparalleled business responsiveness. Leading enterprise brands—across multiple industries around the globe—rely on our award-winning data unification and cloud-native MDM capabilities to improve efficiency, manage risk and drive growth.

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 top rankings 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!

We seek a hands-on AI Engineering Manager to lead R&D on LLM-based agentic workflows, data intelligence, and AI-first products. You'll guide a team of Data Scientists, ML Engineers, UI/Integration Engineers, and Quality Engineers—owning technical design, end-to-end execution, and delivery of agentic systems. This role blends engineering management with deep data science expertise, modern AI tooling, and agent orchestration. Drive the design, implementation, and production deployment of AI-native capabilities integrated with Reltio’s cloud-native data platform.

• →Deliver hands-on engineering management for an AI team of Data Scientists, ML Engineers, UI/Integration Engineers, and ML SDETs.
• →Provide technical leadership, mentoring, and project oversight—scoping ML initiatives, allocating resources, and driving prototype → MVP → production with minimal handoffs.
• →Own agentic AI systems that plan, reason, use tools/APIs, maintain memory/context, self-correct, and execute multi-step workflows autonomously.
• →Design, implement, and productionize end-to-end RAG pipelines, balancing chunking, embeddings, indexing, latency, and cost.
• →Lead foundation model pipelines for embedding generation, prompt engineering, and LLM-driven decisioning.
• →Collaborate with Product, Engineering, and stakeholders to integrate agentic workflows using LangChain, Semantic Kernel, or custom orchestrators.
• →Build/refine LLM/ML models for entity resolution, semantic search, matching, and data unification; identify automation opportunities for scalability.
• →Design A/B experiments to evaluate agent responses, grounding, and quality; evolve memory architectures, vector search, and context management.
• →Partner on integrations with enterprise APIs, backend systems, and cloud ML infra (e.g., AWS Bedrock).

• Proven track record leading and motivating high-performing AI/ML teams in ambiguous, fast-paced environments.
• Deep hands-on experience building and productionizing agentic/multi-step AI workflows.
• Expertise in LLMs, RAG/retrieval architectures, foundation models (embeddings, transformers), vector search, semantic similarity, and prompt engineering.
• Strong product intuition: Translate business problems into scoped ML/LLM solutions with measurable user impact.
• Ownership of production ML code in Python, including cloud deployments (AWS Bedrock, SageMaker, Azure AI Studio).
• Experience with graph memory, MCP protocol (or equivalent), and knowledge reasoning engines.

Hands-on experience with data governance, security best practices, MLOps and LLM observability.Track record contributing to enterprise-scale SaaS or AI/ML products.Familiarity with Kubernetes, containerized deployment, and scalable APIs.