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Urgent! MLOps Engineer (AWS & Databricks) IV Job Opening In Dallas Texas – Now Hiring Artech LLC

MLOps Engineer (AWS & Databricks) IV



Job description

Job Title: MLOps Engineer (AWS & Databricks) – IV

Work Location: Dallas, TX or Miramar, FL - Onsite (4 days per week) OR nearshore: Canada - Remote

Duration of Assignment: 12+ Months Contract

Pay Rate Range: $90.00 - $105.00/hr on W2

Must Have:


  1. Experience with databricks, creating ML ops pipeline, AWS core pipeline

  2. Must have at least 4 years of hands on experience

  3. Experience with models for data scientists to use (do not expect candidates to be data scientists/engineers)


Job Description:

Building, scaling, automating and orchestrating model pipelines; Experience in specific tech stacks include: MLFlow, AutoML, MosaicML, Seldon, Airflow, Docker, Kubernetes, Helm or similar, AWS Sagemaker, Databricks, Grafana or similar, Tecton or similar, CUDA or similar

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.



Required Skill Profession

Architecture And Engineering



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