MLOps Engineer – Azure Databricks, Cloud-to-Edge ML Deployment

Job Overview

EXPERIENCE

3-6 Years

LOCATION

Columbus

JOB

81334

NO OF OPENING

1

• Own end-to-end MLOps delivery for ML solutions, from model packaging and validation to deployment, monitoring, and lifecycle management.
• Build and maintain scalable data and ML pipelines using PySpark, Spark, and Azure Databricks.
• Package, version, and deploy machine learning models developed in Databricks to Azure cloud services and edge devices.
• Design and implement cloud-to-edge ML deployment workflows, including model promotion, artifact management, rollback, and remote update strategies.
• Use MLflow for experiment tracking, model registry, model versioning, and deployment governance.
• Build CI/CD pipelines for ML workloads using tools such as Azure Dev Ops, Git Hub Actions, or similar platforms.
• Containerize ML models and inference services using Docker and deploy them to edge environments.
• Support deployment to edge platforms such as Azure IoT Edge, Kubernetes, K3s, Docker runtime, or embedded Linux based devices.
• Optimize models for edge inference, including latency, memory footprint, CPU/GPU utilization, startup time, and reliability.
• Collaborate with data scientists and ML engineers to convert trained models into production-ready inference services.
• Implement model evaluation, validation, and release gates before cloud or edge deployment.
• Monitor models in production for performance, drift, data quality, system failures, and edge-device health.
• Build logging, observability, and alerting mechanisms for deployed ML services across cloud and edge environments.
• Troubleshoot deployment, runtime, connectivity, and performance issues across cloud and edge systems.
• Work independently on ambiguous technical problems and convert them into scalable, maintainable production solutions.
• Collaborate with data engineers, cloud engineers, software engineers, infrastructure teams, and business stakeholders.
• Guide and support team members on MLOps, model deployment, production ML, and cloud-to-edge best practices.
• Write clean, production-ready, well-tested, and well documented code.

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, Electrical Engineering, or a related field.
• Equivalent practical experience will also be considered.
• 5+ years of hands‑on experience in MLOps, machine learning engineering, applied ML, AI engineering, data science, or production software engineering.
• Proven experience deploying ML or AI solutions into production environments.
• Hands‑on experience working with Azure Databricks and Azure cloud environments.
• Experience building or supporting CI/CD pipelines for ML or software systems.
• Experience deploying, monitoring, and maintaining ML models in production.
• Experience with cloud‑to‑edge, IoT, embedded, or distributed deployment environments is strongly preferred.
• Strong hands‑on experience with Python and SQL.
• Experience with PySpark, Spark, and Azure Databricks.
• Strong working knowledge of Azure cloud services and cloud native deployment patterns.
• Hands‑on experience with MLflow for experiment tracking, model registry, model packaging, and model lifecycle management.
• Strong understanding of MLOps fundamentals, including CI/CD, model versioning, automated testing, release management, monitoring, and rollback.
• Experience deploying ML models as production inference services using REST APIs, batch inference, streaming inference, or containerized services.
• Experience with Docker and container‑based deployment.
• Experience with Azure Dev Ops, Git Hub Actions, Jenkins, or similar CI/CD tools.
• Familiarity with Azure IoT Edge, edge gateways, embedded Linux, Kubernetes, K3s, or container runtimes on edge devices.
• Understanding of edge deployment challenges such as limited compute, memory constraints, intermittent connectivity, offline inference, remote updates, and device fleet management.
• Ability to optimize ML models for edge inference using approaches such as model compression, quantization, ONNX conversion, or runtime optimization.
• Good understanding of ML workflows, model development, validation, evaluation, and production deployment.
• Experience…