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Tier-1 brand and metro location increase candidate density while MLOps specialization moderates generalist competition.
MLOps skills transfer across industries but require specific tooling and platform experience, limiting perfect fit.
Many mandatory MLOps infrastructure and tooling requirements make shortlisting criteria highly rigid.
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Own deployment and maintenance of the group data science platform infrastructure supporting scalable data science pipelines.
Industrialize inference, retraining, and monitoring processes ensuring maintainability and compliance of data science workflows.
Collaborate closely with Data Scientists, Data Engineers, and business stakeholders to anticipate and address platform needs and support end-users.
Strong proficiency in Python and ML Ops tools including Kubernetes, Docker, Jenkins, Ansible, GitHub Actions, and Harbor.
Experience with SQLAlchemy, FastAPI, Numpy, Pandas, Scikit-learn, Transformers, and Pytest.
Experience in deploying and scaling data science or ML pipelines in cloud or hybrid environments, preferably with Azure and big data technologies like Spark, Hadoop.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in end-to-end ML Ops with hands-on expertise in cloud/on-premise hybrid environments, especially Azure and related big data tools.
Operates with a client-focused and collaborative mindset, able to engage with Data Scientists and business teams to tailor scalable platform solutions.
Demonstrates ownership of platform deliverables and drives innovation focused on simplicity and maintainability of ML pipelines.