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Popular mid-level GenAI Data Scientist role with broad full-stack and cloud requirements increases competition.
ML/GenAI and cloud skills transfer across industries, but COE domain context demands moderate specialization.
Mandatory 5+ years (including 2+ years GenAI) and extensive tech requirements make shortlisting strict.
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Design and deliver end-to-end AI architectures and solutions including Agentic AI, GenAI, traditional ML across multiple business units and industries.
Develop, train, evaluate, and deploy AI/ML models using cloud services (Azure, AWS) and integrate them into production environments.
Lead feasibility studies, resource planning, and evangelize AI best practices including mentoring and training teams on AI adoption and responsible AI governance.
Bachelor's degree in computer science, engineering or related discipline; advanced degree preferred but not mandatory.
5+ years of AI/ML experience, with at least last 2 years focused on GenAI, LLMs, RAG frameworks, embeddings, prompt engineering, and agent orchestration.
Strong programming skills in Python and experience with ML frameworks such as TensorFlow, PyTorch, Keras.
Experience building large-scale AI platforms on Azure and AWS, plus familiarity with Agile/DevSecOps and CI/CD pipelines.
Experienced in designing scalable AI solutions combining traditional ML and advanced GenAI techniques for enterprise environments.
Comfortable operating across hybrid cloud ecosystems (Azure, AWS, on-premises) and integrating AI accelerators into complex business applications.
Capable of leading AI strategy execution, mentoring junior staff, and collaborating across geographies and business functions to drive AI adoption.