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Tier-1 brand and metro location increase competition, while niche GenAI/agent specialization reduces generalist applicant density.
Highly specialized agentic GenAI skills and frameworks limit easy cross-industry transferability.
Explicit 5–8 years plus mandatory GenAI frameworks, LLM, and cloud experience creates strict filters.
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Design, develop, test, and deploy machine learning models with emphasis on large language models (LLMs) and generative AI frameworks.
Develop and deploy agentic AI systems involving autonomous or collaborative AI agents in complex environments using multi-agent frameworks like AutoGen, LangChain, and CrewAI.
Integrate and optimize LLMs (e.g., GPT, LLaMA, Mistral) with agentic AI workflows to improve automation, reasoning, and performance metrics such as autonomy, task completion, and resource efficiency.
5 to 8 years of experience as a Data Scientist.
2 to 3 years of experience in generative AI solution development.
Hands-on experience with GenAI frameworks (LlamaIndex, Langchain, Autogen) and multi-agent frameworks (AutoGen, LangGraph, LangChain, CrewAI).
Experience working with cloud platforms such as Azure, GCP, or AWS.
Expertise in designing and deploying autonomous and collaborative AI agents using advanced agentic AI principles (self-organizing, goal-driven).
Strong proficiency in integrating, tuning, and fine-tuning LLMs including open-source and closed-source models with an emphasis on prompt engineering, RAG, reinforcement learning, and PEFT techniques.
Proficient in leveraging vector databases, NLP libraries, and deep learning architectures (Transformers, CNNs, RNNs), with a strong focus on optimizing AI agent performance metrics and explainability.