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Mid-level role, metro location, and a recognizable global IT firm increase applicant competition.
Requires banking/regulatory experience and AI governance knowledge, making industry-specific background important.
Explicit 5+ years, mandatory LLM/MLOps skills, BFSI and AI governance requirements increase filter strictness.
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Lead development and improvement of Generative AI solutions focusing on Large Language Models (LLMs).
Implement and manage AI evaluation, testing, and ModelOps/LLMOps capabilities to ensure high quality, reliability, and governance.
Operate within cloud environments (Azure, AWS, GCP) to deploy production-ready GenAI systems, reducing hallucinations and model performance drifts.
4+ years of experience in AI/ML Engineering, Data Science, ModelOps, or related fields.
Hands-on experience with Large Language Models and Generative AI including Prompt Engineering, Retrieval Augmented Generation (RAG), AI Evaluation Frameworks, and Fine Tuning Techniques.
Strong Python programming skills and experience with ML/AI frameworks such as Azure AI Foundry, OpenAI/Azure OpenAI, Hugging Face, LangChain, Semantic Kernel, and MLflow.
Experience working with cloud platforms (Azure, AWS, or GCP) and knowledge of AI Governance, Responsible AI, and Model Risk Management (MRM).
Experienced in regulated industries such as Banking and Financial Services with knowledge of compliance and governance.
Skilled in establishing scalable AI quality control processes including AI observability and evaluation frameworks.
Able to operate strategically in AI model lifecycle management to reduce risks like hallucinations and model drift while enabling continuous improvement and governance.