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Metro Bengaluru and a popular ML role increase competition, brand not Tier‑1 and role not remote.
Role requires specific ML engineering and governance expertise, limiting straightforward cross-industry transferability.
Multiple technical MLOps, governance, and cloud requirements create moderate filtering despite flexible years.
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Implement and operationalize AI governance framework by embedding controls into ML engineering workflows and delivery practices.
Build and maintain technical guardrails, monitoring, automation, and governance tooling to ensure responsible, scalable AI deployment.
Collaborate with cross-functional teams to enforce technical standards, compliance checks, and governance operations across the ML lifecycle.
Bachelor’s degree in Computer Science, Information Management, or related field.
0-5+ years of experience in machine learning engineering with focus on responsible AI development and deployment.
Strong Python programming skills and experience with ML frameworks (TensorFlow, PyTorch, or scikit-learn).
Knowledge of MLOps practices including CI/CD, model versioning, reproducibility, operational monitoring, and cloud/containerization technologies (AWS, Azure, GCP, Docker, Kubernetes).
Experience implementing AI governance, responsible AI, fairness, observability, traceability, and auditability in AI/ML systems.
Demonstrated ability to develop automation, APIs, and workflows for AI compliance and governance in regulated or complex environments.
Proven collaborator able to work effectively with data science, product, legal, security, and engineering teams to ensure AI solutions meet governance and security standards.