MLOps Engineer (DatabricksAWS)

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.