ML/OPS Engineer - IV
ML/OPS Engineer - IV
Posted on :
22-08-2025
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1 Vacancy
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Job Description
Job Title: ML/OPS Engineer - IV
Location: Dallas TX
Must Haves:
Hands on experience doing ML Ops job duties deploying ML apps
Experience with AWS services: Lambda Sagemaker CodeCommit etc.
Experience with Databricks and model serving
Proficient in Python
Hands on experience doing ML Ops job duties deploying ML apps
Experience with AWS services: Lambda Sagemaker CodeCommit etc.
Experience with Databricks and model serving
Proficient in Python
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
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.
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
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.
Employment Type
Full-time
Key Skills
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