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Tier-1 brand, metro location, mid-level experience band, and broad GenAI requirements increase candidate competition.
Specialized GenAI and ML engineering needs make background fit moderately sensitive but still transferable across industries.
Explicit 3–8 years plus mandatory GenAI, Python, and ML tooling produce moderately strict shortlisting filters.
Design, develop, evaluate, and deploy AI and Generative AI solutions for client use cases including knowledge assistants, document intelligence, workflow automation, and decision support.
Translate business problems into AI-enabled solutions, build prototypes, and reusable assets while managing model risks and responsible AI governance.
Collaborate with partners, consultants, and stakeholders to deliver client-ready AI applications, including maintaining clean, reliable code, technical documentation, and ensuring compliance with AI governance standards.
3 to 8 years of experience in AI/ML, data science, GenAI engineering, NLP, advanced analytics, or related roles.
Bachelor's or master's degree in Computer Science, Data Science, Engineering, Statistics, Mathematics, Economics, AI/ML, or similar quantitative/technical discipline.
Strong hands-on experience with Python and SQL; knowledge of PyTorch, TensorFlow, scikit-learn, Spark, or cloud analytics is an advantage.
Working knowledge of GenAI concepts such as LLMs, transformers, embeddings, prompt engineering, retrieval-augmented generation, fine-tuning, evaluation, and guardrails.
Experienced in consulting or client-facing analytics roles involving AI/GenAI solutions for sectors like financial services, insurance, risk, or operations.
Able to work independently in a fast-paced environment while collaborating effectively with global and cross-functional teams.
Strong practical problem-solving orientation combining technical depth with business impact, including ownership of model governance and responsible AI deployment.