Senior AI Backend Java Engineer | YoE - 7Yrs | Immediate joiners | Bangalore

UST
Greater Bengaluru Area

Roles & Responsibilities:

  • Design and build high-volume, low-latency, and high-availability applications throughout the full software development lifecycle.
  • Write clean, testable, specification-compliant code, managing software components through release.
  • Integrate AI/ML capabilities, LLM APIs, and intelligent backend services (RAG, semantic search, automation) to power AI-driven workflows.
  • Build AI agents for automated root cause analysis (RCA), self-healing, and intelligent test automation.
  • Partner with product teams (e.g., Express, Firefly, Photoshop) to deliver end-to-end solutions, drive product adoption, and enable self-service platform engineering.
  • Leverage AI-assisted tools (e.g., GitHub Copilot, Cursor) to accelerate development and support continuous architectural improvements.

NOTE: We are looking for 7 years of experience with super niche skills such as Micro services, Multi Threading, Spring and AI.

TOP 5 Skills must have- Micro services, Multi Threading, Exp with AWS, Working exp with AI.

Required Skills & Expertise

  • Java Core & Frameworks: 5–8 years of experience building scalable, multithreaded server-side applications using Java/Java EE, Spring Framework, and object-oriented design patterns.
  • Data & Performance: Deep proficiency with Relational Databases (SQL, high-performance transactional queries) and MongoDB, using ORMs (JPA2, Hibernate) optimized for speed and scalability.
  • AI/ML Integration: Hands-on experience integrating LLMs/AI APIs (OpenAI, Claude, Gemini) into Java services using frameworks like Spring AI or LangChain4j, including prompt engineering and context management.
  • AI Infrastructure & RAG: Familiarity with vector databases (e.g., Pinecone, Milvus, pgvector) and Retrieval-Augmented Generation (RAG) architecture to build reliable backend features.
  • Modern AI Practices: Practical experience with AI-assisted dev tools (Copilot, Cursor) alongside an understanding of model deployment, cost/latency optimization, and responsible AI guardrails (privacy, hallucination mitigation).

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