RAG and Evaluation Engineer
LTS is seeking a RAG & Evaluation Engineer to join a small, senior engineering team applying frontier AI to one of the most consequential legacy systems still running in production today.
The mission: build agents that read, translate, and modernize a decades-old codebase that millions of people quietly depend on. The work has executive backing, real users, and a customer who knows exactly what they’re buying. Specifics shared once we’re talking.
The team is small by design. Every seat carries unusual leverage, and we hire people who are already deep in this work. We use AI tooling natively — agents in parallel, model as collaborator, no exceptions.
What You’ll Do:
The RAG & Evaluation Engineer owns the knowledge surface and the eval harness. Ingestion pipelines for source code, structured metadata, technical documentation, patches, and additional corpora the customer provides. Retrieval quality across chunking, embeddings, hybrid retrieval, reranking, freshness. Benchmarks for translation accuracy, dependency-map correctness, and overall agent quality. The feedback loop from production usage back into evals and retrieval lives here.
Own the knowledge surface — ingestion pipelines for source code, structured metadata, technical documentation, patches, and additional corpora the customer provides.
Own retrieval quality — chunking, embeddings, hybrid retrieval, reranking, and freshness.
Own the eval harness — benchmarks for translation accuracy, dependency-map correctness, and overall agent quality.
Run A/B testing and regression detection across prompts, retrieval, and model changes.
Operate the feedback loop from production usage back into evals and retrieval.
Define what “good” means for the platform when no one else has a clear view, so the team can tell whether the agent is actually improving.
Pair with the Agent Engineers on the prompt-and-eval iteration cycle.
What We’re Looking For:
Bachelor’s degree in Computer Science, Engineering, Information Science, or a related field, plus 4 years of professional software engineering experience; equivalent experience may substitute for the degree requirement.
Has shipped a production RAG system with quality the candidate can describe in numbers (rigor matters more than scale).
Ability to work in a fast-paced, collaborative environment.
Production experience with retrieval pipelines — ingestion, chunking, embedding, hybrid retrieval, reranking.
Strong applied evaluation skills — benchmark design, regression detection, LLM-as-judge patterns.
Knows when BM25 beats embeddings and when neither is enough.
Measures everything they ship; opinions about chunking are backed by benchmarks.
Patient with detail; comfortable defining metrics before the team has agreed on them.
Heavy native use of AI tooling: agents in parallel, model as collaborator.
Strong TypeScript or Python.
Demonstrated experience in a remote work environment.
Nice to Have:
Code-as-corpus retrieval (search over source code rather than prose).
Applied IR or search-engine background.
Synthetic data generation and LLM-as-judge patterns.
Open-source contributions to retrieval, eval, or RAG tooling.
Experience integrating retrieval feedback loops with production usage.
Healthcare IT or legacy modernization domain experience.
Public technical writing or conference talks on retrieval or evaluation.
What’s in it for you?
The opportunity to support high visibility federal missions in IT and healthcare
A culture that values innovation, growth, collaboration, and quality
Access to cutting-edge tools and technologies
Comprehensive benefits for you and your family
A career path that rewards ambition and performance
If you’re ready to push boundaries, sharpen your skills, and join a team that is passionate about building what’s next, we’d love to meet you. Apply today and let’s build a future together!
LTS shares salary ranges to promote transparency. Compensation ranges are provided for informational purposes, and final compensation may vary based on experience, skills, location, and role requirements.
LTS is committed to offering eligible employees comprehensive benefits that will provide them with options intended to meet their needs and the needs of their family.
The mission: build agents that read, translate, and modernize a decades-old codebase that millions of people quietly depend on. The work has executive backing, real users, and a customer who knows exactly what they’re buying. Specifics shared once we’re talking.
The team is small by design. Every seat carries unusual leverage, and we hire people who are already deep in this work. We use AI tooling natively — agents in parallel, model as collaborator, no exceptions.
What You’ll Do:
The RAG & Evaluation Engineer owns the knowledge surface and the eval harness. Ingestion pipelines for source code, structured metadata, technical documentation, patches, and additional corpora the customer provides. Retrieval quality across chunking, embeddings, hybrid retrieval, reranking, freshness. Benchmarks for translation accuracy, dependency-map correctness, and overall agent quality. The feedback loop from production usage back into evals and retrieval lives here.
Own the knowledge surface — ingestion pipelines for source code, structured metadata, technical documentation, patches, and additional corpora the customer provides.
Own retrieval quality — chunking, embeddings, hybrid retrieval, reranking, and freshness.
Own the eval harness — benchmarks for translation accuracy, dependency-map correctness, and overall agent quality.
Run A/B testing and regression detection across prompts, retrieval, and model changes.
Operate the feedback loop from production usage back into evals and retrieval.
Define what “good” means for the platform when no one else has a clear view, so the team can tell whether the agent is actually improving.
Pair with the Agent Engineers on the prompt-and-eval iteration cycle.
What We’re Looking For:
Bachelor’s degree in Computer Science, Engineering, Information Science, or a related field, plus 4 years of professional software engineering experience; equivalent experience may substitute for the degree requirement.
Has shipped a production RAG system with quality the candidate can describe in numbers (rigor matters more than scale).
Ability to work in a fast-paced, collaborative environment.
Production experience with retrieval pipelines — ingestion, chunking, embedding, hybrid retrieval, reranking.
Strong applied evaluation skills — benchmark design, regression detection, LLM-as-judge patterns.
Knows when BM25 beats embeddings and when neither is enough.
Measures everything they ship; opinions about chunking are backed by benchmarks.
Patient with detail; comfortable defining metrics before the team has agreed on them.
Heavy native use of AI tooling: agents in parallel, model as collaborator.
Strong TypeScript or Python.
Demonstrated experience in a remote work environment.
Nice to Have:
Code-as-corpus retrieval (search over source code rather than prose).
Applied IR or search-engine background.
Synthetic data generation and LLM-as-judge patterns.
Open-source contributions to retrieval, eval, or RAG tooling.
Experience integrating retrieval feedback loops with production usage.
Healthcare IT or legacy modernization domain experience.
Public technical writing or conference talks on retrieval or evaluation.
What’s in it for you?
The opportunity to support high visibility federal missions in IT and healthcare
A culture that values innovation, growth, collaboration, and quality
Access to cutting-edge tools and technologies
Comprehensive benefits for you and your family
A career path that rewards ambition and performance
If you’re ready to push boundaries, sharpen your skills, and join a team that is passionate about building what’s next, we’d love to meet you. Apply today and let’s build a future together!
LTS shares salary ranges to promote transparency. Compensation ranges are provided for informational purposes, and final compensation may vary based on experience, skills, location, and role requirements.
LTS is committed to offering eligible employees comprehensive benefits that will provide them with options intended to meet their needs and the needs of their family.