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Strong employer brand and metro location but niche GenAI/agentic skillset limits broad applicant density.
Highly specialized agentic GenAI and multi-agent framework expertise reduces cross-industry transferability.
Multiple explicit years ranges plus mandatory generative AI frameworks, LLMs, and cloud experience increase filter strictness.
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Develop and deploy generative AI solutions focusing on autonomous and collaborative AI agents in complex environments.
Design, test, and optimize machine learning models, especially large language models (LLMs), for improved agent decision-making and task completion.
Implement and integrate multi-agent frameworks and cloud services to orchestrate AI workflows with efficiency and explainability metrics.
5 to 8 years of experience as a Data Scientist with 2 to 3 years in Generative AI solution development.
Proficiency in multi-agent AI frameworks such as AutoGen, LangGraph, LangChain, and CrewAI, and experience with LLMs including GPT, LLaMA, and Mistral.
Strong skills in machine learning model design, testing, and deployment, with mandatory experience in GenAI frameworks like LlamaIndex, Langchain, Autogen, and cloud platforms (Azure, GCP, AWS).
Work Experience Required: 5 to 8 years as Data Scientist; Notice period: Not explicitly mentioned in the JD.
Experienced in autonomous AI agent design with deep knowledge of agentic AI principles including self-improving and goal-driven systems.
Advanced expertise in large and small language models, vector databases, and reinforcement learning applied to autonomous agent behaviors.
Technical proficiency in Python, R, TensorFlow, Keras, PyTorch, and NLP toolkits for implementing complex AI and language model solutions in cloud or on-prem environments.