Senior Software Engineer, Search Platform

This posting is for proactive recruitment purposes and may be used to fill current openings or future vacancies within our organization. Overview of the Role The AI Enabled Software Engineering (AIESE) team within Thomson Reuters is seeking Senior Software Engineers to join the search platform team building the shared search infrastructure that powers TR’s most important AI-enabled products. This is a full-ownership engineering role on a team that designs, develops, tests, deploys,  scales,  and  operates  production systems at scale — spanning Kafka-based document ingestion pipelines, hybrid search indexing, query APIs, and the cloud infrastructure that underpins them all. The team has three operating principles that are non-negotiable: you own what you build end-to-end from the first commit to the production dashboard; you ship to production constantly and treat delivery friction as an engineering problem to solve, not a fact of life; and you use AI-assisted development as a primary tool, not an experiment — the expectation is that the majority of code is written with AI assistance. Senior Engineers on this team take technical direction for their projects, hold design decision authority within their scope, provide subject matter  expertise to the team, and mentor more junior colleagues. This is a role for someone who takes genuine pride in the quality, reliability, and p ace  of what they deliver. About the Role In this position, you will focus on: Search Platform Engineering Design, develop, and test software systems and applications incorporating AI capabilities across the platform’s key subsystems: document ingestion and processing pipelines, search engine configurations and index management, query APIs, and management and monitoring infrastructure Architect cloud-native solutions using microservices, containerization, and orchestration technologies; implement machine learning models and RAG-supported technologies to solve search quality problems; write code with a consistent focus on security and ethical AI practices Build and  maintain  Kafka-based stream processing components that move documents reliably through parsing, chunking, enrichment, and indexing stages — implementing robust unit testing (TDD) and the fault tolerance, retry, and dead-letter queue patterns that keep the pipeline healthy under real-world conditions Contribute to the platform’s migration from OpenSearch to Vespa, including ingestion pipeline changes, schema migrations, and the zero-downtime index promotion workflows that keep production search available throughout the transition Implement and  maintain  cloud infrastructure using AWS services (ECS, Lambda, DynamoDB, MSK, Step Functions) and Infrastructure as Code tooling — implementing DevOps practices for automated, reliable deployments with a focus on correctness, security, and cost efficiency Design retrieval components with agentic use cases in mind: respecting explicit latency budgets across multi-hop retrieval chains, designing chunking and result payloads with token economy in mind, and building clean tool boundaries that give agent orchestrators predictable, well-documented interf ace s to the search layer Build platform API features that enable client teams to configure search systems, manage index lifecycles, and  monitor  ingestion health through clean, self-service interf ace s Reliability & Operations Participate actively in the team’s on-call rotation and take end-to-end ownership of incidents within your scope — triage, root cause analysis, clear communication, and thorough post-mortems that  identify  systemic improvements; you built it, you run it Identify and address delivery friction proactively: if a deployment step is slow, a test is flaky, or a local development workflow is painful, treat it as a problem worth fixing — the team’s ability to ship to production constantly depends on every engineer pushing back on friction when they encounter it Leverage AI-ass ... (truncated, view full listing at source)