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Senior, niche MLOps role in Bangalore with a non-Tier1 brand yields moderate competitive density.
ML Ops skills transfer across industries but require domain-specific ML, LLM and cloud expertise.
Explicit 7–9 year requirement plus Azure ML, MLflow, and governance mandates increases filter strictness.
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Build and scale operational MLOps and LLMOps pipelines to deploy AI and generative models into production.
Drive secure, automated deployment and governance of AI/ML services on Azure cloud using DevOps practices.
Design monitoring and evaluation systems for model performance, reliability, and continuous improvement in production.
7–9 years of experience in MLOps, DevOps, ML engineering, or cloud/platform engineering with production AI/ML systems.
Hands-on expertise with Azure Machine Learning, MLflow, Azure OpenAI, CI/CD pipelines, and monitoring frameworks.
Bachelor's degree in Computer Science, Engineering, or related technical field (or equivalent practical experience).
Location requirement: Bangalore, India.
Experienced in designing secure, automated AI/ML deployment pipelines with strong governance and traceability focus.
Skilled in collaborating cross-functionally with data scientists, AI engineers, and product teams to operationalize AI solutions.
Capable of leading rollout strategies like A/B testing, canary releases, and human-in-the-loop controls in regulated environments.