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Strong employer brand and Bangalore location increase competition, but senior ML specialization reduces applicant density.
Skills in MLOps and computer vision are transferable across industries but energy domain adds some specificity.
Explicit 8-10 years requirement and mandatory MLOps/CV stack increase filtering stringency.
Design, build, and deploy scalable machine learning models and infrastructure on cloud platforms (AWS, Azure, GCP).
Develop and manage CI/CD pipelines for ML models automating training, validation, and deployment workflows.
Implement monitoring for model performance, compliance, and governance; lead collaboration across data science, software engineering, and DevOps teams.
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 including MLflow, TFX, or Airflow.
Experience designing scalable ML infrastructure and managing ML lifecycle including model deployment and monitoring.
Experienced in productionizing end-to-end ML workflows with strong focus on model deployment, CI/CD automation, and infrastructure scalability.
Strong background in computer vision and domain-specific ML applications, with skills in distributed training and real-time inference systems.
Capable of technical leadership and mentoring, collaborating across teams to integrate ML solutions and improve ML lifecycle management.