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Mid-level GenAI role in Bangalore with sought-after LLM/vector skills yields moderate competition.
GenAI, vector DB and MLOps skills transfer across industries but require specific tooling experience, so sensitivity is medium.
Multiple mandatory technical requirements including 5+ years, expert Python, LangChain/LLM experience, vector DBs, and cloud impose high filtering.
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Design, implement, and optimize production-grade Retrieval-Augmented Generation (RAG) pipelines to enhance large language model (LLM) accuracy.
Develop clean, modular, and efficient Python backend code using frameworks like FastAPI or LangChain for GenAI applications.
Deploy, secure, and scale GenAI solutions on cloud platforms (AWS, GCP, or Azure) including management of vector databases and system monitoring for token usage, latency, and accuracy.
5+ years of cloud engineering and AI solution development experience.
Expert-level Python skills with asynchronous programming and API development focus.
Hands-on experience with GenAI frameworks such as LangChain, LlamaIndex, or Hugging Face, and practical experience with vector databases like Vespa, Pinecone, Milvus, or Chroma.
Bachelor’s degree in Computer Science, Software Engineering, or related technical field; Location: Bangalore (Onsite).
Experienced in deploying and managing cloud infrastructure including Docker, Kubernetes, and serverless compute for AI applications.
Skilled in integrating and tuning complex AI and GenAI systems, including prompt engineering for specific business use cases.
Familiar with fine-tuning open-source LLMs and setting up MLOps/LLMOps tracking tools to optimize AI model performance and monitoring.