MLOps Engineer
Posted Updated
Description
Job Summary
Responsibilities
Infrastructure Pipeline Development
Model Deployment Operations
Expertise and Qualifications
Must-Have Technical Skills
Good-to-Have Technical Skills
Soft Skills
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
We are looking for a Senior MLOps / ML Engineer to own, develop, and maintain ML deployment and data processing workflows, spanning an Argo-based workflow orchestration platform and Python-based frameworks for large-scale AI model inference.
The ideal candidate should have strong Python and problem-solving skills, be comfortable designing and troubleshooting workflows, and be able to quickly learn and adapt to new technologies. A pure MLOps background is not mandatory if the candidate has strong ML/software engineering fundamentals and can work effectively with technologies such as Databricks, Argo, Azure Pipelines, and Ray.
Responsibilities
Infrastructure Pipeline Development
- Design and maintain Argo Workflows / Argo Events-based 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.
Good-to-Have Technical Skills
- Ray - distributed data processing and inference at scale.
- Deep Learning frameworks - PyTorch, TensorFlow; model formats such as ONNX, SavedModel, and TorchScript.
- Helm / ArgoCD and GitOps-based deployment practices.
- Prometheus / Grafana and production observability.
- Experience with Azure Pipelines or similar CI/CD platforms.
- Experience with ML model monitoring, data drift, and model performance monitoring.
- Large-scale data processing - experience working with large volumes of structured, unstructured, image, or video data, particularly for ML/AI workloads.
Soft Skills
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
Skills
- AI
- AKS
- Argo CD
- Azure
- CI/CD
- Cloud
- Containerization
- Databricks
- Deep Learning
- Docker
- Event Driven Architecture
- Git
- GitOps
- GPU
- Grafana
- Helm
- Kubernetes
- Machine Learning
- MLOps
- Model Deployment
- Observability
- ONNX
- Prometheus
- Python
- PyTorch
- Spark
- SQL
- TensorFlow
- Test Automation
- Version Control
- Workflow Orchestration