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High due to Tier-1 brand, mid-level ML role, metro location, and popular GenAI skill demand.
Low because GenAI, NLP and LLM engineering skills are highly transferable across industries despite domain-preference.
High due to explicit 5-8 years, mandatory GenAI experience, and numerous specialized LLM and tooling requirements.
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Develop and deploy advanced machine learning models with focus on generative AI and large language models (LLMs) to solve business problems.
Lead projects independently, providing technical direction and collaborating with research and market partners to deliver end-to-end AI/ML/NLP solutions including Conversational AI and document understanding.
Analyze data to identify product insights, build reusable tools and modeling pipelines, and translate analytic findings into actionable business recommendations.
5 to 8 years of experience as Data Scientist or GenAI specialist with 2 to 3 years in Generative AI solution development.
Strong hands-on expertise with LLMs (open source like Llama, Gemma; closed source like GPT, Claude), GenAI frameworks (LlamaIndex, Langchain), and associated technologies (RAG, VectorDB).
Proficiency in Python, R, Tensorflow, Keras, Pytorch and NLP libraries such as SpaCy, NLTK, and experience tuning/fine-tuning language models (e.g., BERT, XLNet).
Bachelor's degree or higher in BE/BTech, MCA, or PhD. Work Experience Required: 5-8 years explicitly stated.
Experience leading AI/ML projects independently with ability to provide technical guidance to teams and interact with cross-functional stakeholders.
Deep domain knowledge and practical skills in Generative AI technologies, LLM architectures, and NLP methods tailored to business solutions.
Comfortable working with cloud platforms (Azure, AWS, GCP) and integrating AI models into scalable production environments.