Back
Mid-level Bangalore AI role with broad LLM, RAG, and production requirements attracts many qualified applicants.
Specialized LLM, RAG, embeddings, and vector DB expertise limits transferability across non-AI roles.
Explicit 4–8 years plus mandatory LLM, RAG, vector DB, and production deployment skills increases shortlisting strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and develop autonomous AI agents and multi-agent workflows to automate enterprise workflows.
Build and optimize LLM-powered applications including Retrieval-Augmented Generation systems and semantic search solutions.
Collaborate with Product and Engineering teams to deploy scalable, production-grade AI applications in talent acquisition and skills intelligence domain.
4–8 years of relevant work experience in AI engineering or related fields.
Strong proficiency in Python and experience with Large Language Models (LLMs), Agentic AI, RAG, Prompt Engineering, NLP, and embeddings.
Hands-on experience with AI frameworks such as LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen and vector databases like Pinecone, Weaviate, Milvus, Qdrant, or FAISS.
Experience deploying AI applications in production environments using FastAPI, REST APIs, Docker, Kubernetes, Git, and cloud platforms (AWS, Azure, or GCP).
Experienced in building autonomous AI agents and multi-agent workflows tailored for enterprise SaaS or HRTech products.
Technical expertise in AI model optimization for accuracy, latency, scalability, and cost in production settings.
Able to integrate AI applications across complex systems using modern orchestration and deployment practices with familiarity in knowledge graphs and Responsible AI.