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Strong Tier-1 brand, popular mid-level ML/GenAI role, and mid experience band increase applicant competition.
Highly domain-specific GenAI, deep learning, and MLOps expertise reduces cross-industry transferability.
Explicit 4–8 year requirement, leadership expectation, and mandatory GenAI/ML stack make filters strict.
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Define AI strategy and lead development of large-scale ML and GenAI-powered products and pipelines including prompt engineering, fine-tuning, and vector retrieval.
Architect and oversee full ML lifecycle including MLOps practices like CI/CD, experiment tracking, model versioning, deployment, monitoring, and scaling.
Lead end-to-end data science projects with stakeholder management, mentoring junior data scientists, and enforcing coding and workflow standards.
Hands-on experience with large language models (e.g., Gemini, OpenAI, Anthropic, Llama) and advanced NLP techniques including prompt engineering and embedding-based retrieval.
Expert proficiency in Python and ML frameworks such as PyTorch/TF 2 and Hugging Face Transformers.
Proven experience managing production-ready ML and GenAI projects, including deployment and MLOps.
Work Experience Required: 4-8 years in Data Science/AI, with at least 2 years in a leadership or technical lead role.
Experience leading and mentoring within a global, cross-functional AI or data science team focused on advanced NLP/GenAI products.
Strong technical leadership in architecting and deploying production ML pipelines and MLOps practices.
Background or familiarity with Economics/Financial industry analytics and market-intelligence products is a plus.