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Metro location and mid-level seniority amplify competition, but niche GenAI specialization moderates it.
Role requires specialized GenAI, vector DB, and LLM deployment skills, making cross-industry transfer limited.
Explicit 5–6+ years, specialized GenAI/RAG, vector DB and cloud requirements create strict filters.
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Design, build, and optimize production-grade Retrieval-Augmented Generation (RAG) pipelines to enhance large language model (LLM) accuracy.
Develop clean, efficient Python backend code using frameworks like FastAPI or LangChain for GenAI applications.
Deploy, secure, and scale GenAI solutions on cloud infrastructure (AWS, GCP, or Azure) including managing vector databases for semantic search.
5–6+ years of professional experience in cloud engineering and AI solution development.
Expert-level Python programming skills, especially asynchronous programming and API development.
Hands-on experience with GenAI frameworks (e.g., LangChain, LlamaIndex, Hugging Face) and vector databases (e.g., Vespa, Pinecone, Milvus, Chroma).
Bachelor's degree in Computer Science, Software Engineering, or related technical field; location requirement: Bangalore (Onsite).
Experienced in deploying and managing cloud compute resources including Docker, Kubernetes, and serverless architectures.
Familiar with prompt engineering to optimize AI model outputs for business cases.
Proven ability with GenAI ecosystem tools and monitoring systems to track model performance and usage in production environments.