Senior Software Engineer / Senior AI Engineer

RELX•Philadelphia, MA

22h•$86,600 - $144,400

About The Position

Embedded Innovation Teams are cross-functional squads embedded within our segments to rapidly turn internal AI experimentation into validated, reusable solutions, building the capabilities we need to deliver customer value and growth. We work problem-first rather than tool-first, directly inside segment and function teams, improving the internal workflows that help our people deliver better outcomes for customers, faster. As a Senior AI Engineer, you will build and ship AI-powered features as part of a cross-functional Innovation Squad, working inside a business function. You will deliver the set technical direction to production standard, working autonomously within a defined problem, building and testing tool use, retrieval pipelines, and agent workflows, integrating AI capabilities into enterprise systems, and contributing to evaluation, observability, and guardrails. You will hold a high bar on code quality, flag risks and blockers early, and work alongside host-function stakeholders to make sure what you build fits real workflows, not assumed ones. You'll also support handover and capability-building so the solution is owned and operable after the squad moves on.

Requirements

3+ years in software engineering, with hands-on experience building LLM-powered applications in production (RAG, tool-augmented agents, or agentic workflows).
Comfortable working autonomously within a defined problem and pushing back when something doesn't make sense, and delivering in short, time-boxed cycles where validated outcomes matter more than perfect solutions.
Building RAG pipelines, tool-augmented agents, and agentic workflows; familiar with prompt engineering, context management, evaluation, and observability.
Understands how agents use memory, tools, and retrieval to complete multi-step tasks.
Integrating AI into existing systems via APIs and data pipelines.
AWS, Azure or GCP.
CI/CD, modern SDLC, TDD and code review.
Working with relational, columnar and vector stores, grounded in sound data-modelling principles.
Python, Java, TypeScript/JavaScript, SQL and relevant AI SDKs.
Clear written and verbal communication with technical peers and stakeholders.
Instrumenting solutions to capture usage, productivity and quality metrics against established baselines.

Responsibilities

Building prototypes and proofs of concept and ship agentic AI solutions to production standards within a defined technical approach.
Implementing and testing tool use, retrieval pipelines, and agent workflows.
Contributing to evaluation, observability, and guardrails for agentic systems.
Integrating AI capabilities into existing enterprise workflows and systems.
Maintaining high code quality and documentation so patterns can be reused.
Flagging technical risks and blockers early.
Interface with technical peers to finalize requirements and complete moderately complex bug fixes.
Building solutions for reuse, contributing to patterns, reference implementations and starter kits so work done in one function can be picked up by another.
Implementing solutions to capture outcome data against baselines.
Work alongside host-function stakeholders to ensure the build fits real workflows, not assumed ones.
Supporting handover and capability-building in the host function so the solution is owned and operable after the squad moves on.