ML/Ops Engineer (AWS & Databricks)

Job Title: ML/Ops Engineer (AWS & Databricks) Location: Hybrid – Miramar, FL or Dallas, TX ( 4 days onsite in a week) Duration: 1 Year | Temp Only MLOps Engineer (AWS & Databricks) • Primary Responsibilities Design, implement, and maintain CI/CD pipelines for machine learning applications using AWS CodePipeline, CodeCommit, and CodeBuild. • 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, CodePipeline, CodeCommit, 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. • Version control expertise with Git/GitHub and experience in automated deployment reviews and rollback strategies. Nice to Have • Experience with Feature Store (e.g., AWS SageMaker Feature Store, Feast). • Familiarity with Kubeflow, SageMaker Pipelines, or Vertex AI (if multi-cloud). • Exposure to LLM-based models, vector databases, or retrieval-augmented generation (RAG) pipelines. • Knowledge of Terraform or AWS CDK for infrastructure automation. • Experience with A/B testing or shadow deployments for ML models.