AI ML Engineer

We're looking for a Java engineer who builds with AI as well as with code. You'll design and build scalable, secure Java services. You'll also add AI features to those services, such as LLM-powered workflows, retrieval-augmented generation (RAG), and AI agents. And you'll use AI coding tools to speed up how the team delivers.

This role suits someone with deep backend engineering skills who has already shipped AI features to production, or is clearly on the way there. We are not looking for someone who only experiments with AI.

• Core engineering
  • Design, build and maintain high-performance microservices using Java 17+ and Spring Boot
  • Build and use REST, GraphQL and event-driven APIs (Kafka) that are secure, observable and resilient
  • Own features end to end: design, code, test, deploy and support in production
  • Write clean, testable code and keep code quality and test coverage high
• AI building
  • Build LLM-powered features into enterprise apps using Spring AI, LangChain4j or provider SDKs (Azure OpenAI, OpenAI and others)
  • Design and run RAG pipelines. This covers splitting documents into chunks, building embeddings, working with vector stores (pgvector, Azure AI Search, Elasticsearch, Pinecone) and tuning how results are retrieved and ranked
  • Build agent workflows that use tools and function calls, and connect them to internal systems, for example through the Model Context Protocol (MCP)
  • Write, version and test prompts. Set up evaluation frameworks to measure accuracy, groundedness, latency and cost
  • Build in safeguards: PII/PHI redaction, content filters, protection against prompt injection, and human review where it's needed
  • Monitor AI features in production and tune them for token use, response time and reliability
• AI-assisted engineering
  • Use AI coding assistants (such as GitHub Copilot) in daily development, testing, refactoring and documentation, and share good practices with the team
  • Find chances to automate engineering work with AI, such as generating tests, triaging code reviews, and summarizing incidents
• Collaboration and leadership
  • Work with product, data science, architecture and security teams to turn business problems into AI solutions
  • Mentor other engineers in modern Java and AI development
  • Help shape team standards for responsible AI, architecture and engineering practice
• Comply with all applicable Company policies, procedures, and business directives, changes including those relating to work location, team assignments, work schedules, and flexible work arrangements

• Bachelor's or Master's degree in Computer Science, Engineering or a related field, or equivalent hands-on experience
• Java and backend
  • 8+ years of hands-on Java work, with strong knowledge of Java 11/17+, multithreading, collections and the JVM
  • Experience with microservices, REST APIs and messaging (Kafka, RabbitMQ)
  • Experience with cloud platforms (AWS preferred, or Azure or GCP), Docker and Kubernetes
  • Experience with CI/CD (GitHub Actions, Jenkins, Azure DevOps) and test frameworks (JUnit, Mockito, Testcontainers)
  • Proven solid with Spring Boot, Spring Cloud, Spring Security and JPA/Hibernate
  • Proven solid with SQL and NoSQL databases (PostgreSQL, Oracle, MongoDB, Redis)
• AI skills
  • Hands-on experience building apps with LLMs (GPT, Claude, Llama, Gemini or similar) through APIs
  • Knowledge of responsible AI principles, data privacy and secure AI design
  • Good understanding of embeddings, vector search, RAG and prompt engineering
  • Understands the limits of LLMs, including hallucination, context limits, non-deterministic output and cost, and knows how to design around them
  • Familiar with agent concepts: tool and function calling, planning, memory and multi-agent orchestration
• Ways of working
  • Proven solid problem-solving and system design skills
  • Proven clear communicator who can explain AI trade-offs to people without a technical background
  • Proven curious and keeps up with the fast-moving AI field

• Certifications such as Azure AI Engineer Associate, AWS ML Specialty, or Oracle Java certification
• Experience with MCP servers, Copilot Studio or other agent platforms
• Experience with LLM evaluation and observability tools (LangSmith, Langfuse, Arize, OpenTelemetry)
• Experience in healthcare, with an understanding of HIPAA, PHI handling and regulated environments
• Basic ML knowledge: model fine-tuning, classic ML and NLP
• Working knowledge of Spring AI and/or LangChain4j. Python experience with LangChain or LlamaIndex
• Proven event-driven or reactive programming (Spring WebFlux)
• Public work: GitHub projects, AI side projects, blog posts or talks.