Title: MLOps Engineer (AWS & Databricks)

Location: Dallas, TX//Onsite

Type: Contract

Primary Responsibilities

• Design, implement, and maintain CI/CD pipelines for machine learning applications using AWS Code Pipeline, Code Commit, and Code Build.
• Automate the deployment of ML models into production using Amazon SageMaker, Databricks, and MLflow for model versioning, tracking, and lifecycle management.
• Develop, test, and deploy AWS Lambda functions for triggering model workflows, automating pre/post-processing, and integrating with other AWS services.
• Maintain and monitor Databricks model serving endpoints, ensuring scalable and low-latency inference workloads.
• Use Airflow (MWAA) or Databricks Workflows to orchestrate complex, multi-stage ML pipelines, including data ingestion, model training, evaluation, and deployment.
• Collaborate with Data Scientists and ML Engineers to productionize models and convert notebooks into reproducible and version-controlled ML pipelines.
• Integrate and automate model monitoring (drift detection, performance logging) and alerting mechanisms using tools like CloudWatch, Prometheus, or Datadog.
• Optimize compute workloads by managing infrastructure-as-code (IaC) via CloudFormation or Terraform for reproducible, secure deployments across environments.
• Ensure secure and compliant deployment pipelines using IAM roles, VPC, and secrets management with AWS Secrets Manager or SSM Parameter Store.
• Champion DevOps best practices across the ML lifecycle, including canary deployments, rollback strategies, and audit logging for model changes.

Minimum Requirements

• hands-on experience in MLOps deploying ML applications in production at scale.
• Proficient in AWS services: SageMaker, Lambda, Code Pipeline, Code Commit, ECR, ECS/Fargate, and CloudWatch.
• Strong experience with Databricks workflows and Databricks Model Serving, including MLflow for model tracking, packaging, and deployment.
• Proficient in Python and shell scripting with the ability to containerize applications using Docker.
• Deep understanding of CI/CD principles for ML, including testing ML pipelines, data validation, and model quality gates.
• Hands-on experience orchestrating ML workflows using Airflow (open-source or MWAA) or Databricks Workflows.
• Familiarity with model monitoring and logging stacks (e.g., Prometheus, ELK, Datadog, or OpenTelemetry).
• Experience deploying models as REST endpoints, batch jobs, and asynchronous workflows.