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Tier-1 brand, mid-level experience band, and Bangalore metro increase competition.
Core GenAI, Python, cloud and LLMOps skills transfer across industries, though enterprise RAG and Responsible AI favor domain experience.
Explicit years plus many mandatory GenAI, cloud, and LLM technology requirements make filters stringent.
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Develop and deploy enterprise-grade Generative AI applications, APIs, and microservices focused on LLM orchestration and Retrieval-Augmented Generation (RAG).
Build multi-agent autonomous workflows and integrate commercial and foundational AI models within cloud environments like Azure, AWS, or GCP.
Implement Responsible AI guardrails, safety guidelines, and monitoring frameworks to ensure ethical, compliant, and performant AI deployments; rapidly prototype PoCs for internal and client demonstrations.
3-6 years of software development experience with strong computer science fundamentals.
Expert proficiency in Python programming and API development using frameworks such as FastAPI, Flask, or Django.
Hands-on experience with at least one major cloud AI ecosystem (Azure OpenAI, AWS Bedrock, or GCP Vertex AI) and proficiency in SQL/NoSQL databases.
Education: BE/BTech/MTech/MCA/MBA (full-time); Notice period: Not explicitly mentioned in the JD.
Experienced in developing and optimizing AI systems involving LLM orchestration, vector databases, and advanced prompting techniques such as few-shot learning or chain-of-thought.
Comfortable working with DevOps practices including containerization (Docker), Kubernetes basics, CI/CD pipelines, and LLM monitoring tools (e.g., LangSmith).
Demonstrates strong cloud AI integration skills with familiarity in LangChain, LlamaIndex, vector search technologies, and responsible AI framework implementation aligned to enterprise requirements.