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Mid-level (3–6 yrs) niche GenAI role but metro market increases applicant density.
GenAI technical skills are transferable but enterprise integration and domain experience matter.
Explicit 3–6 years plus mandatory LLM, LangChain, RAG, embeddings, and prompt engineering requirements.
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Develop and deploy Generative AI solutions using Large Language Models (LLMs) and prompt engineering techniques.
Create, optimize, and test prompts and build AI agents and multi-agent workflows for enterprise applications.
Integrate LLM APIs, develop Retrieval-Augmented Generation (RAG) and Agentic AI solutions, and collaborate with cross-functional teams for end-to-end AI solution integration.
3–6 years of experience in building AI-powered applications using Generative AI and LLMs.
Hands-on proficiency in Python, REST APIs, and JSON handling with API integrations.
Experience with AI frameworks such as LangChain, LlamaIndex, Semantic Kernel, and exposure to cloud AI platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI).
Bachelor’s degree in Computer Science, Engineering, AI, Data Science, or related field.
Experienced in designing and fine-tuning AI agents and prompt engineering for enterprise-scale applications.
Proficient in both AI solution development (including RAG, vector databases, semantic search) and engineering practices (Git, CI/CD, Docker).
Familiar with responsible AI practices and preferably has exposure to multi-modal AI and domain expertise in healthcare, supply chain, or financial sectors.