OverviewInabia 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.
