Senior Software Engineer, Search Platform

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 expertiseto the team, and mentor more junior colleagues. This is a role for someone who takes genuine pride in the quality, reliability, and pace 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 interfaces 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 interfaces 

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-assisted development tools fluently throughout your workflow — from code generation and review to test writing and debugging — and share effective patterns with the team 

• Instrument components with structured logging, metrics, and distributed tracing; for agentic retrieval paths, implement trace-level observability that captures tool call sequences and per-hop inputs so that failures in non-deterministic workflows can be diagnosed and reproduced reliably 

• Contribute to the platform’s evaluation infrastructure: instrument query and retrieval components to produce the experiment run outputs — query text, ranked results, relevance signals — that feed the search grading tool, supporting offline gold test set evaluation and LLM-as-judge scoring across pipeline versions 

• Contribute to SLO definition and monitoring for components you own, and design DevOps workflows and automation strategies that reduce operational toil and improve the team’s deployment confidence 

Technical Leadership & Collaboration 

• Take technical direction for assigned projects with design decision authority within your scope; break down customer and product requirements into work the team can execute on; lead routine and moderately complex projects using Agile/Scrum methodologies 

• Provide subject matter expertise across the team, particularly in the platform’s search and data infrastructure domains; mentor and develop more junior engineers through code review, pairing, and technical coaching 

• Clearly communicate technical concepts to stakeholders at various technical levels, adjusting your communication style to the audience; champion ethical AI practices and responsible technology implementation across the team’s work 

• Collaborate with TR Labs and research scientists to integrate new search capabilities — embedding models, chunking strategies, re-ranking approaches — into the platform in a well-tested, observable, and responsible way 

• Collaborate with client teams to understand how they use the platform, identify friction points, and translate that feedback into concrete platform improvements 

About You 

You’re an ideal fit if you have: 

Required Experience — 

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field 

• 5+ years of software engineering experience designing, developing, and testing production backend or data infrastructure systems, with demonstrated ability to take end-to-end ownership of features and components 

• Mastery of Python with the ability to make architectural decisions based on language strengths and to write high-quality production code with a focus on security and ethical AI practices 

• Solid distributed systems knowledge: you understand the failure modes of message queues, streaming pipelines, and distributed databases, and you design systems that are resilient to them — implementing robust unit testing (TDD) and security best practices across the development lifecycle 

• Proficiency with AWS cloud services (ECS, Lambda, DynamoDB, SQS/SNS, CloudWatch) and Infrastructure as Code tooling; ability to implement multi-environment architectures and scalability patterns 

• Demonstrated operational ownership: you have participated in on-call, written architectural documentation and decision records, contributed to post-mortems, and developed DevOps workflows that improve team delivery — with a clear philosophy that building and operating are inseparable 

• Fluency with AI-assisted development tools as a primary part of your workflow — you write the majority of your code with AI assistance and have a track record of using these tools to accelerate delivery without sacrificing quality 

• Proven ability to communicate technical concepts clearly to technical and non-technical stakeholders; strong written communication skills including design documents and component documentation 

Preferred Experience — 

• Experience with Kafka or other distributed event streaming platforms in production, including producer/consumer patterns, offset management, and consumer group monitoring 

• Familiarity with search engines (OpenSearch, Elasticsearch, Vespa, or Solr) at the level of writing queries, designing indexes, or operatingclusters 

• Experience integrating multiple AI services — including embedding APIs, re-ranking models, and RAG-supported retrieval components — with error handling and fallback strategies in production 

• Exposure to agentic retrieval patterns: multi-hop search, latency budget management, or designing tool interfaces for agent orchestration layers 

• Familiarity with online search analytics — query performance monitoring, A/B or interleaved ranking experiments, query log analysis — and how low-level search signals connect to downstream business metrics 

• Background in infrastructure-as-code tooling (Terraform, AWS CDK, or similar) and container orchestration (ECS or Kubernetes) with ability to configure and optimize CI/CD pipelines 

• Exposure to developing AI governance approaches for teams and championing responsible, ethical AI implementation in production search or data systems 

What Success Looks Like 

In the first 90 days: 

• Ramp up on the platform’s architecture, codebase, and operational practices — taking on-call responsibility and contributing production code within the first month 

• Deliver at least one meaningful feature or reliability improvement with full end-to-end ownership: from design documentation through deployment and monitoring 

• Build strong working relationships with teammates, identify where your skills can have the highest near-term impact, and demonstrate the team’s operating principles — owning your work end-to-end, shipping frequently, and using AI-assisted development fluently 

In the first year: 

• Own a significant platform component end-to-end: you hold design decision authority for it, you monitor its health, and you proactively drive improvements to its reliability, performance, and documentation 

• Demonstrate growing technical depth in at least one platform domain — ingestion pipelines, search indexing, query APIs, or cloud infrastructure — and take on progressively more complex design work and cross-functional technical leadership in that area 

• Contribute measurably to the team’s engineering culture: code review quality, architectural documentation, operational rigor, and the mentorship of colleagues newer to the team