MLOps Engineer (Databricks/AWS)
ColumbusContractPosted Jul 22, 2026
Required Skills
- Strong hands-on experience with Databricks Machine Learning
- Proficiency in Python, PySpark, and SQL for developing and operationalizing machine learning solutions.
- Hands-on experience with MLflow for experiment tracking, model registry, model versioning, and model lifecycle management.
- Experience deploying and managing machine learning models using Databricks Model Serving and batch inference pipelines.
- Strong understanding of the end-to-end ML lifecycle, including feature engineering, model training, validation, deployment, monitoring, and retraining.
- Experience building scalable ML pipelines using Databricks Workflows and Delta Lake.
- Hands-on experience with AWS services such as S3, IAM, EC2, Lambda, ECR, ECS/EKS, CloudWatch, and Secrets Manager.
- Experience implementing CI/CD pipelines for Databricks and ML workloads using Git, Bitbucket, Jenkins, and Databricks Asset Bundles (DAB).
- Experience with infrastructure automation using Terraform (Infrastructure as Code).
- Strong understanding of Apache Spark architecture, optimization, and distributed data processing.
- Experience working with Unity Catalog for governance, security, and access management.
- Knowledge of model monitoring, data drift detection, model performance monitoring, and automated retraining strategies.
- Understanding of MLOps best practices, including reproducibility, versioning, testing, and governance.
- Strong collaboration skills to work with Data Scientists, Data Engineers, and Platform Engineering teams.
- Excellent analytical, problem-solving, and communication skills.
- Design, develop, and maintain end-to-end MLOps pipelines on Databricks running on AWS.
- Build and automate machine learning workflows covering data preparation, feature engineering, model training, evaluation, deployment, and monitoring.
- Deploy and manage ML models using MLflow Model Registry and Databricks Model Serving.
- Develop and maintain CI/CD pipelines for ML solutions across development, staging, and production environments.
- Collaborate with Data Scientists to productionize machine learning models and ensure reliable deployments.
- Monitor model health, prediction quality, data drift, and system performance, and implement retraining strategies where required.
- Optimize Databricks workloads for performance, scalability, and cost efficiency.
- Implement Infrastructure as Code (Terraform) for provisioning and managing Databricks and AWS resources.
- Ensure platform security, governance, and compliance using Unity Catalog and AWS IAM.
- Troubleshoot production issues, perform root cause analysis, and continuously improve platform reliability.
- Document MLOps processes, deployment standards, and operational best practices.