Transformation Leader

9 hours ago

bangalore, India beBeeEngineer Full time

About the Role We are seeking a highly skilled and experienced Senior MLOps/DevOps Engineer to join our team. The ideal candidate will have a strong background in machine learning operations, software engineering, and deploying production-ready models. Responsibilities Develop and maintain a robust ML platform to standardize model development and deployment workflows.Create and evangelize reusable MLOps components to accelerate the Data Science model lifecycle.Design and implement seamless integration of trained models with diverse production systems and products.Implement robust logging and instrumentation for real-time monitoring of model scoring requests in production.Establish automated systems for continuous model monitoring and performance-based retraining.Design and build advanced deployment strategies , including Blue-Green Deployment and A/B testing frameworks that support canary and shadow models.Optimize model inference at scale: Implement request-based scaling (e.g., KEDA), ensure eventual distribution of requests to pods, and explore removing node affinity for flexible load distribution.Enhance CI/CD and deployment efficiency: Reduce container startup time (e.g., Docker layer optimization).Integrate unit test case flow directly into the deployment pipeline.Enable auto deployment from GitHub for seamless, GitOps-style operations.Scale and manage compute resources: Implement multi-resource configuration for optimal pod provisioning.Troubleshoot and fix issues related to KEDA-based scaling in QA and Production environments.Explore advanced GPU architectures (e.g., NVIDIA MIG) and implement fractional GPU usage for cost and resource optimization.Automate Model and Infrastructure Configuration: Implement automatic model loading from MLflow using cloud storage (EFS/S3) for efficient serving.Investigate and implement Agentic auto-configuration of pods to intelligently adapt resources based on traffic/load.Integrate and optimize data pipelines necessary for scheduled model retraining and production updates.Evaluate and integrate optimal open-source frameworks and proprietary tools into the MLOps pipeline. Experience & Expertise: The successful candidate should have: 3+ years of professional experience in MLOps, software engineering, and successfully deploying production-ready machine learning models.Strong expertise in Python and advanced scripting for pipeline automation and tooling development.Extensive hands-on experience with Docker and Kubernetes for building, managing, and debugging containerized ML environments and scalable production deployments.Deep proficiency with MLOps frameworks (e.g., MLflow, Seldon, Kubeflow) and production-level cloud ML services (e.g., AWS SageMaker, Azure ML, GCP AI Platform).In-depth understanding of public cloud infrastructure and services (AWS, Azure, or GCP).Proven ability to evaluate, prototype, and implement open-source tools to define and guide the MLOps technology stack.Excellent problem-solving skills and a strong analytical mindset.

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