Python & Java Full Stack Developer (AI-Capabilities)
About the role
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Python & Java Full Stack Developer (AI-Capabilities) based in the United States. This is a senior full-stack engineering opportunity focused on building AI-enabled platforms with direct, real-world impact in manufacturing environments. You will own features end to end, spanning AWS infrastructure, Python and Java services, agentic LLM workflows, and modern Angular applications. AI coding agents are an important part of the engineering workflow, but strong fundamentals, critical thinking, and rigorous validation remain essential. You’ll work across event-driven architectures, cloud-native services, data pipelines, and retrieval systems. The role offers significant technical ownership within a large-scale, multi-repository engineering environment. You’ll help shape reliable AI capabilities while contributing to systems where correctness, observability, and production resilience matter.
Responsibilities
- Design, develop, test, and deploy end-to-end features spanning AWS infrastructure, Python and Java backend services, agentic LLM workflows, and Angular/TypeScript frontends.
- Build and evolve AI workflows using Amazon Bedrock, including function and tool calling, RAG, embeddings, prompt design, validation loops, and production fallback strategies.
- Develop and maintain Python serverless services using AWS Lambda and Java microservices using Spring Boot or Quarkus, with REST APIs, event-driven integrations, persistence, asynchronous processing, and message-bus connectivity.
- Design resilient distributed systems with appropriate idempotency, retry, dead-letter, and failure-handling mechanisms.
- Define and maintain infrastructure as code using AWS CDK across multi-stack deployments and cross-stack dependencies.
- Design and maintain production API contracts, shared schemas, and interfaces while preserving backward compatibility across services and clients.
- Develop retrieval and scoring capabilities using log chunking, embeddings, semantic/vector search, OpenSearch, and relevance-ranking techniques.
- Build operational visibility through CloudWatch dashboards, alarms, tracing, structured diagnostic events, and other observability practices.
- Own and improve CI/CD workflows using Azure DevOps, including gated, reversible, blue/green, canary, and rollback deployment strategies.
- Contribute to shared foundational code while keeping high-impact changes narrowly scoped, well-tested, secure, and maintainable.
- Use AI coding agents thoughtfully by defining precise requirements, providing appropriate context, reviewing generated code critically, and validating behavior through testing and observability.