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services and REST/GraphQL APIs\n\n- Implement AI-powered features using LLMs and generative AI technologies\n\n- Build, maintain, and optimize RAG pipelines, including vector database integration and retrieval strategies\n\n- Develop AI agents and tool integrations, including MCP-based workflows\n\n- Apply prompt engineering and prompt management practices across AI features\n\n- Optimize AI application performance, latency, and token/cost consumption\n\n- Implement AI security controls, guardrails, and governance requirements\n\n- Monitor, troubleshoot, and continuously improve production AI systems\n\n- Collaborate with architects and product teams to deliver enterprise AI solutions\n\nRequired Skills :\n\nSoftware Engineering :\n\n- Python\n\n- Microservices architecture\n\n- REST & GraphQL APIs\n\n- Authentication & authorization\n\n- Caching strategies\n\n- Cloud-native development\n\nAI Engineering : \n\n- LLM APIs (OpenAI, Anthropic, Gemini, Azure OpenAI)\n\n- RAG architectures 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