Back
Tier-1 brand, remote role, and Bangalore metro increase applicant competition.
Requires specialized MLOps and model evaluation expertise, making background highly domain-specific.
Requires explicit MLOps, Kubernetes, cloud, and leadership experience, enforcing strict technical and managerial filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and build scalable, reliable backend and platform systems optimized for ML workloads with high availability and performance.
Architect and implement end-to-end MLOps pipelines covering data ingestion, deployment, monitoring, and automated retraining with strong automation in model lifecycle management.
Lead cross-functional teams to deliver scalable AI solutions and shape ML platform strategy and adoption roadmap.
Strong foundation in software engineering with Java, Python, or Go and system design expertise.
Proven experience in MLOps frameworks and model lifecycle management, including CI/CD, containerization, Kubernetes, and infrastructure as code.
Hands-on experience with cloud platforms such as GCP, AWS, or Azure.
Prior experience managing engineering teams.
Experienced in building and operating cloud-native scalable ML and backend systems with production-level reliability and cost optimization.
Demonstrates deep knowledge of model accuracy evaluation, monitoring strategies, and governance tools like MLflow, Kubeflow, and Airflow.
Operationally capable of leading engineering teams and collaborating cross-functionally to drive ML platform strategy and AI solution delivery.