RAG Application Engineer

RAG Application EngineerThe Opportunity:
Retrieval-Augmented Generation (RAG) systems require reliable, well-orchestrated runtime logic to integrate search, models, and metadata into a single coherent experience. As a RAG Application Engineer, you will build the application layer that makes this possible. You will implement the end-to-end query workflow, including embedding generation, hybrid retrieval, reranking, citation assembly, and request-level guardrails, ensuring analysts can access grounded, high-quality responses at scale.

We're looking for an engineer who can design and maintain RAG runtime logic in a secure, cloud-based environment. On our team, you'll develop API endpoints, manage model integrations, enforce authentication and authorization controls, and implement robust retry, timeout, and error-handling strategies.

You'll containerize services and partner closely with Cloud, Security, Data, and Site Reliability Engineering teammates to ensure the RAG runtime is stable, secure, observable, and ready for mission use.

Join us. The world can’t wait.

You Have:

• Experience building back-end or platform services using programming languages such as Python or Java
• Experience designing or integrating RAG workflows, including embeddings, hybrid retrieval, vector search, and reranking
• Experience implementing API request pipelines, orchestration logic, and service routing for AI model and search calls
• Experience enforcing authentication and authorization within service logic
• Experience developing resilient services with retry, backoff, timeout, and error-handling strategies and collaborating with data, search, cloud, and security engineers to optimize retrieval behavior
• Experience containerizing applications using Docker and deploying to ECS, EKS, or Kubernetes
• Experience building telemetry, including structured logs, metrics, and traces
• Ability to translate RAG requirements into clean, maintainable application logic
• TS/SCI clearance
• HS diploma or GED

Nice If You Have:

• Experience integrating Amazon Bedrock model inference into application workflows
• Experience implementing hybrid retrieval with OpenSearch, including BM25 and vector search
• Experience developing citation assembly logic, grounding validation, or model guardrails
• Experience designing API gateways, rate limiting, caching, or other high-availability patterns
• Experience working with message queues or asynchronous workflows, including Kafka or Amazon SQS
• Experience contributing to evaluation data pipelines or partnering with evaluation scientists
• Experience using infrastructure-as-code frameworks such as Terraform, CloudFormation, or AWS CDK