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Medium — metro location and broad technical requirements increase applicant density despite seniority and niche ML leadership.
High — specialist ML/AI, MLOps, and LLMOps expertise required limits cross-industry transferability.
High due to explicit 12+ years requirement and extensive mandatory AI/ML, cloud, and MLOps skills.
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Lead AI/ML operational and platform engineering across the organization ensuring technical excellence.
Provide architecture guidance, technical leadership, and mentorship to AI Engineering, Data Engineering, and Platform teams.
Drive adoption and enablement of AI/ML tools, platforms, best practices, and monitoring standards including cloud-native, containerized, and serverless environments.
Minimum 12 years overall work experience with at least 5 years in AI/ML production environments and 3 years in technical leadership or senior engineering roles.
Proven experience designing and implementing large-scale AI/ML solutions using frameworks like TensorFlow, PyTorch, and cloud AI/ML platforms (Azure AI, AWS SageMaker, Google Vertex AI).
Strong skills in cloud platforms (Azure, AWS, GCP), container orchestration (Kubernetes, Docker), MLOps and deployment tools (MLflow, Kubeflow).
Work Location: Mumbai, Maharashtra, India; Full-time employment.
Experienced leader capable of deeply investigating and troubleshooting AI/ML tech stacks and driving continuous technical improvement.
Hands-on in end-to-end AI/ML lifecycle including model optimization, operationalization, and cloud-native architectures.
Skilled in collaborating with solution architects and business stakeholders to translate requirements into scalable AI/ML solutions with governance and security focus.