Overview
Inabia is seeking an MLOps Engineer (Databricks/AWS) to design, develop, and maintain end-to-end machine learning operations pipelines on Databricks running on AWS. This role demands deep hands-on expertise with Databricks Machine Learning, MLflow, and a broad suite of AWS services to build scalable, production-grade ML systems. The ideal candidate will partner closely with Data Scientists, Data Engineers, and Platform Engineering teams to productionize models and ensure robust, governed, and cost-efficient deployments.
Locations: Columbus, OH - Dallas, TX - Atlanta, GA (Onsite only)
Responsibilities
• 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, repeatable deployments.
• Monitor model health, prediction quality, data drift, and system performance, and implement automated retraining strategies where required.
• Optimize Databricks workloads for performance, scalability, and cost efficiency.
• Implement Infrastructure as Code using Terraform to provision and manage 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.
Key Qualifications
• 10+ years of relevant experience in MLOps or a closely related data/ML engineering discipline.
• 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 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, training, validation, deployment, monitoring, and retraining.
• Experience building scalable ML pipelines using Databricks Workflows and Delta Lake.
• Hands-on experience with AWS services including 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.
• 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, and automated retraining strategies.
• Understanding of MLOps best practices including reproducibility, versioning, testing, and governance.
• Excellent analytical, problem-solving, and communication skills.
Preferred Qualifications
• Experience with multi-environment Databricks deployments spanning development, staging, and production.
• Familiarity with cost optimization strategies for large-scale Databricks and AWS workloads.
• Prior experience documenting MLOps operational standards for cross-functional teams.