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Metro location, senior level, and niche MLOps/LLM skills create moderate applicant competition.
MLOps skills transfer across industries but require ML domain experience, so medium sensitivity.
Explicit 7–9 years plus mandatory Azure MLOps tooling and regulated-environment experience increases selectivity.
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Build and scale MLOps and LLMOps pipelines for deploying AI and generative AI models into production.
Drive secure, automated AI/ML service deployments on Azure using DevOps practices, ensuring governance and auditability.
Design and maintain monitoring and evaluation systems to keep AI models performant and reliable in production, including leading safe rollout strategies like A/B testing and canary releases.
7–9 years of experience in MLOps, DevOps, ML engineering, or cloud/platform engineering with hands-on deployment of production AI/ML systems.
Bachelor’s degree in Computer Science, Engineering, or related technical field (or equivalent practical experience).
Proficiency with Azure Machine Learning, MLflow, Azure OpenAI, CI/CD pipelines, and monitoring frameworks.
Location requirement: Bangalore, India.
Experienced in operating AI/ML systems in regulated environments with strong focus on governance and traceability.
Skilled in cross-functional collaboration with data scientists and product teams to operationalize AI innovation.
Demonstrated ability to lead and implement robust safe rollout and model monitoring strategies ensuring high quality AI production systems.