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AI Engineer/ AI Senior Engineer

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Description

Role & responsibilities


  • Develop Java/Spring Boot micro-services that power LLM and agentic workflows in production
  • Build RAG pipelines and agents. tune retrieval, design tools, control flow, add human-in-the-loop gates
  • Ship every AI feature with evals: golden datasets, regression suites, accuracy thresholds
  • Add guardrails, human review, and audit trails for AI-assisted decisions
  • Own reliability and cost: latency, token spend, caching, fallbacks
  • Work with product, platform, and governance through design and release reviews.
  • AI/ML or Generative AI certifications from recognized cloud or technology providers.
  • Demonstrated continuous learning through technical contributions, open-source projects pr publications.

Qualifications/ Certifications

  • Bachelors degree in Computer Science, Engineering, Information Technology, or related technical discipline.
  • 3 to 5+ years of production Java/ Spring Boot, distributed-systems fundamentals: REST/gRPC,
  • Has shipped an LLM-backed feature: model APIs, prompt design, RAG fundamentals- chunking, embeddings, vector search.
  • Hands-on with at least one SDK- Claude Agent SDK, OpenAI Agents SDK, LangGraph, Spring AI, LangChain4j.
  • Extends the harness with custom skills, hooks, subagents, and MCP servers. Manages unit economics and reliability: token and latency budgets, caching, step budgets, loop detection, fallbacks.
  • Builds for security: prompt injection defense, output validation, least-privilege tools.
  • Strong foundation in software engineering, distributed systems, data structures, algorithms, and system design.

Skills

What Senior AI Engineering jobs ask for — and how much of it you have →
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See also

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