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Tier‑1 brand and metro location increase competition despite specialist ML/MLOps requirements.
Core MLOps and LLM skills transfer across industries, though banking governance increases domain specificity.
Many mandatory MLOps, cloud, containerization and LLM requirements plus Lead title make filters very strict.
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Own and lead the end-to-end production lifecycle of ML and LLM models focusing on deployability, scalability, observability, and maintainability.
Define and enforce ML engineering and MLOps standards; design and maintain CI/CD pipelines for ML workloads.
Act as technical lead and mentor for ML engineers; partner with Data Scientists; manage model monitoring, drift detection, testing, rollback, and incident analysis.
Experience leading production deployment and lifecycle management of ML and LLM models (must have).
Proficiency with Azure cloud, CI/CD tools (Jenkins, GitHub Actions, ArgoCD), Docker, Kubernetes, Airflow, and production-grade Python (all must have).
Strong skills in ML engineering including LLM integration, prompt engineering, model packaging, Pytest for ML testing, SQL and relational databases (must have).
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in technical leadership and mentoring within ML engineering teams, capable of setting and enforcing standards across groups.
Able to collaborate effectively with Data Scientists, Platform, Cloud, and Data Engineering teams to industrialize ML research with enterprise-grade infrastructure.
Practically minded about adopting new ML, GenAI, and MLOps tools while ensuring governance, reproducibility, and responsible AI practices.