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Urgent! MLOps Engineer AWS & Databricks - SGWS Job Opening In Dallas Texas – Now Hiring Shiftcode Analytics, Inc

MLOps Engineer AWS & Databricks SGWS



Job description

Interview: Virtual (Need Strong candidate with LinkedIn)

Visa: USC and GC

Hybrid: Dallax, TX and Miramar, FL (Local or nearby)




JD:

Overview: We're seeking a highly skilled MLOps Engineer with deep expertise in AWS and Databricks to design, implement, and maintain scalable machine learning infrastructure.

This role is ideal for someone who thrives in fast-paced environments and is passionate about automating and optimizing the ML lifecycle—from model development to deployment and monitoring.

Primary Responsibilities:


  • Design, implement, and maintain CI/CD pipelines for ML applications using AWS CodePipeline, CodeCommit, and CodeBuild.

  • Automate deployment of ML models into production using Amazon SageMaker, Databricks, and MLflow for versioning and lifecycle management.

  • Develop, test, and deploy AWS Lambda functions for triggering workflows, automating pre/post-processing, and integrating with other AWS services.

  • Maintain and monitor Databricks model serving endpoints for scalable, low-latency inference.

  • Orchestrate complex ML pipelines using Airflow (MWAA) or Databricks Workflows, covering ingestion, training, evaluation, and deployment.

  • Collaborate with Data Scientists and ML Engineers to convert notebooks into reproducible, version-controlled pipelines.

  • Integrate model monitoring and alerting (drift detection, performance logging) using CloudWatch, Prometheus, or Datadog.

  • Manage infrastructure-as-code (IaC) via CloudFormation or Terraform for secure, reproducible deployments.

  • Ensure secure and compliant pipelines using IAM roles, VPC configurations, and secrets management (AWS Secrets Manager or SSM Parameter Store).

  • Champion DevOps best practices across the ML lifecycle, including canary deployments, rollback strategies, and audit logging.


Minimum Requirements:


  • 4+ years of hands-on MLOps experience deploying ML applications at scale.

  • Proficient in AWS services: SageMaker, Lambda, CodePipeline, CodeCommit, ECR, ECS/Fargate, and CloudWatch.

  • Strong experience with Databricks workflows and Model Serving, including MLflow for tracking and deployment.

  • Proficient in Python and shell scripting; skilled in Docker containerization.

  • Deep understanding of CI/CD principles for ML, including pipeline testing, data validation, and quality gates.

  • Experience orchestrating ML workflows using Airflow (open-source or MWAA) or Databricks Workflows.

  • Familiarity with monitoring/logging stacks: Prometheus, ELK, Datadog, or OpenTelemetry.

  • Experience deploying models as REST endpoints, batch jobs, and asynchronous workflows.

  • Strong Git/GitHub skills with experience in automated deployment reviews and rollback strategies.


Nice to Have:


  • Experience with Feature Stores (e.g., SageMaker Feature Store, Feast).

  • Familiarity with Kubeflow, SageMaker Pipelines, or Vertex AI.

  • Exposure to LLM-based models, vector databases, or RAG pipelines.

  • Knowledge of Terraform or AWS CDK for infrastructure automation.

  • Experience with A/B testing or shadow deployments for ML models.






Required Skill Profession

Architecture And Engineering



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