MLOPs

Relevant Experience : 4 to 6 years

Job Description:

Infrastructure & Pipeline Development:

• Design and maintain Argo Workflows / Argo Eventsbased ML and data processing pipelines across environments (dev / QA / staging / prod).
• Design and maintain WorkflowTemplates, DAGs, fan-out patterns, scheduled workflows, and event-driven triggers following GitOps best practices.
• Design and develop Python-based workflows and components for ML data processing and model inference.
• Work with Databricks and Spark-based workloads for data processing, transformation, and pipeline execution.
• Build and maintain Docker images for ML and GPU-accelerated workloads, including multi-stage builds and container optimization.
• Develop scalable inference workflows using Ray where required.
• Integrate with Azure ML and Azure cloud services for model versioning, model artifacts, training workflows, and deployment.
• Maintain CI/CD pipelines with automated testing, quality gates, and environment-specific deployment processes.

Model Deployment & Operations:

• Automate deployment, monitoring, and rollback of machine learning and deep learning models on production Kubernetes environments.
• Manage model versioning and artifact movement from cloud ML workspaces and storage systems to production workloads.
• Maintain configuration systems and reusable workflow components to ensure reproducible pipeline execution across environments.
• Monitor model and pipeline performance, data processing health, resource utilization, and infrastructure reliability.
• Troubleshoot production issues involving failed workflows, data bottlenecks, resource contention, GPU utilization, and latency regressions.
• Identify reliability and performance issues proactively and implement improvements to ML and data processing workflows.
• Collaborate with data scientists and engineering teams to improve model deployment, inference, and pipeline reliability.

Expertise and Qualifications:

Must-Have Technical Skills

• Python — strong programming and problem-solving skills; experience with application development, scripting, and Python packaging.
• Workflow Design — ability to design reliable workflows with dependencies, parallel execution, retries, failure handling, scheduling, and event-driven execution.
• Argo Workflows / Argo Events — understanding of WorkflowTemplates, DAGs, event-driven workflows, and workflow execution.
• Databricks / Data Processing — hands-on experience with Databricks, Spark/Spark SQL, or similar large-scale data processing platforms.
• Kubernetes — understanding of workload orchestration, resource management, namespaces, and production troubleshooting.
• Git / Version Control — strong understanding of Git or equivalent version control systems, including branching, merging, pull requests, and maintaining code/configuration changes.
• Docker — containerization, multi-stage builds, and optimization of ML workloads.
• CI/CD — experience designing pipelines with automated testing, quality gates, artifact management, and deployment processes.
• Azure Cloud — working knowledge of Azure ML, Azure Blob Storage, AKS, and Azure-based ML workflows.
• Problem Solving — ability to troubleshoot unfamiliar technical problems and identify practical solutions.Role & responsibilities