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BRAHMA Careers

Senior / Principal Generative AI Engineer

San Francisco, CAOn-siteFull-time$140k – $220k / year

About this role

BRAHMA Careers - Senior / Principal Generative AI Engineer Senior / Principal Generative AI Engineer Technology Bengaluru, Karnataka --- Apply Description We are looking for a passionate and experienced Generative AI Engineer to design, implement, and scale intelligent AI agents and applications. You will work on architecting agentic systems powered by LLMs, retrieval-augmented generation (RAG), and reasoning frameworks. The ideal candidate is someone who stays current with the fast-evolving GenAI ecosystem and can transform advanced research into production-grade solutions. Key Responsibilities Architect, build, and deploy agentic AI systems using frameworks such as LangGraph, Google ADK,Autogen, Semantic Kernel, CrewAI, and LangChain. Design and implement RAG pipelines leveraging vector databases (OpenSearch, Pinecone, FAISS, or Weaviate). Develop modular and reusable AI agent architectures for dynamic reasoning and tool orchestration. Integrate and fine-tune foundation models (GPT, Claude, Gemini, Mistral, Llama, etc.) for domain-specific tasks. Work with APIs, embeddings, and context optimization for large-scale AI workflows. Collaborate with platform, data, and product teams to ensure robust integration and deployment. Implement observability, performance tracking, and continuous evaluation of model and agent behavior. Stay up to date with the latest research and open-source developments in LLMs, agents, and cognitive frameworks. Mentor and guide junior engineers on prompt design, agent architecture, and optimization techniques. Required Skills & Experience 5–10 years of total experience in AI / ML engineering, including at least 2 years in Generative AI. Proven expertise with agentic frameworks — LangGraph, Google ADK, Semantic Kernel, CrewAI, LangChain, or similar. Strong programming skills in Python and familiarity with microservice or API-based systems. Hands-on experience with LLMs, vector search, and retrieval-augmented generat

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