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Tier-1 employer and metro location with a senior ML role create moderate competition density.
Core ML engineering skills transfer across industries, but energy-specific governance and CV needs require some domain familiarity.
Explicit 8-10 years plus mandatory MLOps, cloud, CV and tooling requirements enforce strict filters.
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Design, build, and manage scalable machine learning infrastructure and CI/CD pipelines for model training, validation, and deployment.
Implement monitoring systems for model drift, performance, and data integrity ensuring governance, security, and reliability of ML systems.
Collaborate with cross-functional teams and mentor junior engineers to integrate ML solutions and lead technical projects.
Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.
8-10 years of experience in software engineering, data science, or ML Ops.
Proficiency in Python, Docker, Kubernetes, and cloud-native ML tools with experience in ML lifecycle platforms such as MLflow or TFX.
Work Experience Required: 8-10 years of relevant experience. Notice period: Not explicitly mentioned in the JD.
Experienced in productionizing end-to-end ML workflows including data ingestion, feature engineering, deployment, and monitoring.
Strong background in ML system reliability, cost optimization, compliance standards, and real-time inference systems.
Specialized knowledge in Computer Vision or domain-specific ML applications with expertise in distributed training and ML Ops tooling.