Senior Software Engineer, Search Platform

Thomson Reuters; Eagan (MN); On-site; USD 140,000 - 190,000; Full time

Job summary

Thomson Reuters in Eagan, MN seeks a Senior Software Engineer for the AI Enabled Software Engineering team to build the core of our search platform—ingestion pipelines, APIs, and scalable cloud infrastructure. You will own end-to-end delivery, mentor teammates, and help migrate from OpenSearch to Vespa.

You will design reliable, high-performance systems, work with embedding models and retrieval strategies, and contribute to platform improvements used by many products and AI agents.

Qualifications

• 5+ years building and operating production backend or data infra with end-to-end ownership.
• Strong programming skills in Python, Java, or Go and good tool judgment.
• Solid distributed systems knowledge with queues, streaming, and data stores.
• Hands-on experience with AWS and infrastructure as code.
• On-call, incident response, and observability ownership.

Responsibilities

• Design, build, test, deploy, and operate platform services across ingestion, indexing, and APIs.
• Build self-service platform APIs for configuring search systems and monitoring health.
• Collaborate on moving from OpenSearch to Vespa with zero-downtime promotions.
• Instrument components with logging, metrics, tracing, and SLOs.
• Mentor junior engineers and drive end-to-end feature delivery.

Skills

Distributed systems fundamentals

Cloud platforms (AWS)

On-call / incident response

AI-assisted development tools

Education

Bachelor's degree in Computer Science, Engineering, or related field

Tools

Python

Java

Go

AWS

Terraform

Job description

Senior Software Engineer, Search Platform

Our vision

We are building the next-generation search platform, with a vision to democratize knowledge construction and consumption at Thomson Reuters. The platform consolidates search systems across Thomson Reuters products and services, serving millions of end users, internal agents, and internal search teams, into a fully managed, customizable, sovereign, and self-serve search platform.

Job Description

The AI Enabled Software Engineering (AIESE) team within Thomson Reuters is seeking Senior Software Engineers to build the core of that platform: the ingestion pipelines that turn content into searchable knowledge, the APIs that serve it to products and AI agents, and the cloud infrastructure that keeps it fast and reliable. Your work is what lets internal teams create, configure, and run search on their own, without waiting on a central team.

This is a full-ownership role. You design, build, test, deploy, and operate production systems at scale. We ship to production constantly, treat delivery friction as an engineering problem to solve, and use AI-assisted development as a primary tool. Senior Engineers here hold design authority within their scope, share expertise across the team, and mentor more junior engineers.

Key Responsibilities

• Design, build, and test platform services across document ingestion, search indexing, and query APIs, using the language best suited to the problem (for example Python, Java, or Go).
• Build reliable pipeline components that move documents through parsing, chunking, enrichment, and indexing, using proven patterns for retries, dead-letter queues, and idempotent processing.
• Build self-service platform APIs that let client teams configure search systems, manage index lifecycles, and monitor ingestion health.
• Design retrieval interfaces for products and AI agents with predictable contracts, explicit latency budgets, and result payloads sized for LLM context windows.
• Contribute to the move from OpenSearch to Vespa, including schema migrations and zero-downtime index promotion.
• Instrument retrieval components so offline evaluation and LLM-as-judge grading can compare pipeline versions and measure search quality.

Operate what you build

• Take part in the on-call rotation and own incidents in your scope, from triage and root cause to post-mortem and systemic fixes.
• Add logging, metrics, and distributed tracing to your components, including trace-level visibility into agentic retrieval paths, and help define SLOs.
• Remove delivery friction wherever you find it: slow deployments, flaky tests, and painful local workflows. Use AI-assisted tools fluently and share what works.

Lead and collaborate

• Take technical direction for assigned projects, break customer and product requirements into executable work, and mentor more junior engineers through review and pairing.
• Work with TR Labs and research scientists to bring new search capabilities, such as embedding models, chunking strategies, and re-ranking, into production in a well-tested and observable way.
• Work with client teams to understand how they use the platform and turn their feedback into platform improvements.

Required Qualifications

• 5+ years of software engineering experience building and operating production backend or data infrastructure systems, with end-to-end ownership of features and components. A degree in Computer Science, Engineering, or a related field, or equivalent experience
• Strong programming skills in at least one of Python, Java, Go, or an equivalent language, and the judgment to choose the right tool for the job
• Solid distributed systems fundamentals: you understand how queues, streaming pipelines, and distributed data stores fail, and you design for it
• Hands-on experience with a major cloud platform (AWS preferred) and infrastructure as code
• Operational ownership: on-call, incident response, and observability, with the view that building and operating are inseparable
• Fluency with AI-assisted development tools and clear communication with technical and non-technical stakeholders

Preferred qualifications

• Production experience with a search engine such as OpenSearch, Elasticsearch, Vespa, or Solr, including writing queries, designing indexes, or operating clusters
• Experience with Kafka or another event streaming platform
• Experience integrating AI services, such as embedding, re-ranking, and RAG components, with sound error handling and fallbacks
• Exposure to agentic retrieval patterns, such as multi-hop search or designing tool interfaces for agents
• Familiarity with search evaluation and analytics, including relevance experiments and query log analysis

What Success Looks Like

In the first 90 days

• Ramp up on the platform's architecture and operations, take on-call responsibility, and contribute production code within the first month
• Deliver one meaningful feature or reliability improvement with end-to-end ownership, from design through deployment and monitoring
• Build strong working relationships and show the team's operating principles in practice

In the first year