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Senior AI Platform Engineer – Java (5+ yrs)

circles · Bangalore

New
Senior 🇬🇧 English
Java Spring Framework Spring Boot Spring MVC Spring Security RESTful APIs Maven Gradle Git Event-driven architecture Message queues SQL NoSQL AWS Azure Large Language Models Embeddings Vector similarity Prompt engineering Python Docker Kubernetes CI/CD LangChain LangGraph pgvector Pinecone

Job description

About the role

We are looking for a senior Java developer with deep Spring expertise to lead the design and delivery of AI‑enabled features on our enterprise platform. The role combines backend engineering with the integration of large language models (LLMs) and Retrieval‑Augmented Generation pipelines, working closely with clients and senior leadership.

Key responsibilities

  • Architect, develop and maintain enterprise‑grade Java Spring applications while embedding AI/ML components such as RAG pipelines and AI agent workflows.
  • Integrate and manage LLM APIs, handling rate‑limiting, error handling, caching and validation layers.
  • Lead the creation of scalable backend services and micro‑services, ensuring high performance and reliability.
  • Mentor junior developers, conduct code reviews, and enforce best practices including prompt safety and cost optimisation.
  • Collaborate with cross‑functional teams to gather requirements, provide technical guidance and drive project execution.

Required profile

  • Minimum 5 years of professional software development experience, primarily in Java and the Spring ecosystem.
  • Bachelor’s degree in Computer Science, Software Engineering or a related field.
  • Strong analytical and problem‑solving abilities with a client‑facing mindset.
  • Experience working in Agile/Scrum environments and delivering production‑ready code with comprehensive testing.

Required skills

  • Java, Spring Framework (Spring Boot, MVC, Security)
  • RESTful API design and implementation
  • Build tools: Maven, Gradle
  • Version control: Git
  • Event‑driven architectures and message queues
  • SQL and NoSQL databases
  • Cloud platforms: AWS or Azure
  • Large Language Model concepts, embeddings, vector similarity, Retrieval‑Augmented Generation
  • Prompt engineering, JSON‑mode outputs, context management
  • Python for AI prototyping
  • Containerisation: Docker, Kubernetes
  • CI/CD pipelines, test‑driven development
  • Frameworks: LangChain or LangGraph (preferred)
  • Vector databases such as pgvector or Pinecone (preferred)

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Published 10 hours ago

Expires 1 month from now

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circles

Bangalore