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Mid-level LLM role in Bangalore with common skillset and metro location creates moderate applicant competition.
Specialized LLM, RAG, and agentic systems skills limit cross-industry transferability.
Explicit 4+ years plus mandatory LLM, vector DB, and cloud skills make filtering stringent.
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Design, develop, and deploy Retrieval-Augmented Generation (RAG) systems integrating vector databases and embedding models.
Architect and fine-tune custom Large Language Models (LLMs) using parameter-efficient tuning techniques like LoRA.
Create and manage multi-agent agentic AI workflows using frameworks such as LangChain, CrewAI, or OpenDevin.
4+ years of Python software development experience with strong asynchronous programming and API design skills.
Experience deploying LLM applications using frameworks like LangChain, LlamaIndex, or Haystack.
Proficiency with vector databases (FAISS, Pinecone, Weaviate, Milvus, Qdrant) and embedding models (OpenAI, sentence-transformers, Cohere, HuggingFace).
Work Experience Required: 4+ years
Expertise in end-to-end AI/ML system design including RAG pipelines and autonomous multi-agent AI systems.
Experienced using cloud-based AI deployment platforms such as AWS SageMaker, Azure ML, or Google Vertex AI.
Skilled in prompt engineering, custom model fine-tuning (RLHF, LoRA, QLoRA), and AI observability tools (LangFuse, Phoenix).