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Tier-1 brand, mid-level ML role, metro location and broad in-demand GenAI skills elevate competition.
Core ML/AI and MLOps skills transfer across industries, though banking governance raises moderate domain specificity.
Explicit 4–6 years plus many mandatory MLOps, cloud, and LLM tooling requirements create strict filters.
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Lead design and delivery of enterprise-scale AI/ML solutions, including LLM/GenAI, focusing on reliability, security, and compliance.
Drive technical standards and mentor junior engineers in AI solution operationalisation (MLOps and LLMOps).
Collaborate with cross-functional teams to integrate AI APIs and ensure governance and compliance.
4–6 years of experience in software development, machine learning, or AI with proven production delivery and technical leadership.
Proficiency in Python, software engineering best practices, and working knowledge of SQL.
Experience with Docker/Kubernetes, Git-based CI/CD (GitHub/Azure DevOps), and cloud AI platforms such as Azure ML or GCP Vertex AI.
Deep understanding of LLM fundamentals, MLOps tools (MLflow/Kubeflow, Airflow), and familiarity with feature stores (e.g., Feast).
Experienced AI/ML practitioner capable of leading enterprise-grade AI solution delivery with operational excellence.
Strong software engineering background with practical skills in cloud-native MLOps and secure AI deployment.
Technical leader who can set standards, mentor teams, and manage cross-functional collaborations involving cutting-edge AI technologies like LLMs and GenAI.