MLOps LeadAIML-Ops Engineer AIML Engineer

Job Title: MLOps Lead/AI/ML-Ops Engineer/ AI/ML Engineer

Location: Dallas TX

Duration: 6 Months

Skills:

• Overall 10 years of experience with 4 years of experience in MLOps Machine Learning Engineering or a related DevOps role with a focus on ML workflow
• Extensive hands-on experience in designing and implementing MLOps solutions on AWS. Proficient with core services like SageMaker S3 ECS EKS Lambda
• Strong coding proficiency in Python. Extensive experience with automation tools including Terraform for IaC and GitHub Actions.
• A solid understanding of MLOps and DevOps principles. Hands-on experience with MLOps frameworks like Sagemaker Pipelines Model Registry Weights and
• Expertise in developing and deploying containerized applications using Docker and orchestrating them with ECS and EKS.
• Experience with model testing validation and performance monitoring. Good understanding of ML frameworks like PyTorch or TensorFlow is required
• Excellent communication and documentation skills with a proven ability to collaborate with cross-functional teams (data scientists data engineers

Job Description:

• Build & Automate ML Pipelines: Design implement and maintain CI/CD pipelines for machine learning models ensuring automated data ingestion model training testing versioning and deployment.
• Operationalize Models: Collaborate closely with data scientists to containerize optimize and deploy their models to production focusing on reproducibility scalability and performance.
• Infrastructure Management: Design and manage the underlying cloud infrastructure (AWS) that powers our MLOps platform leveraging Infrastructure-as-Code (IaC) tools to ensure consistency and cost optimization.
• Monitoring & Observability: Implement comprehensive monitoring alerting and logging solutions to track model performance data integrity and pipeline health in real-time. Proactively address issues like model or data drift.
• Governance & Security: Establish and enforce best practices for model and data versioning auditability security and access control across the entire machine learning lifecycle.
• Tooling & Frameworks: Develop and maintain reusable tools and frameworks to accelerate the ML development process and empower data science teams.
• Cloud Expertise: Extensive hands-on experience in designing and implementing MLOps solutions on AWS. Proficient with core services like SageMaker S3 ECS EKS Lambda SQS SNS and IAM.
• Coding & Automation: Strong coding proficiency in Python. Extensive experience with automation tools including Terraform for IaC and GitHub Actions.
• MLOps & DevOps: A solid understanding of MLOps and DevOps principles. Hands-on experience with MLOps frameworks like Sagemaker Pipelines Model Registry Weights and Bias MLflow or Kubeflow and orchestration tools like Airflow or Argo Workflows.
• Containerization: Expertise in developing and deploying containerized applications using Docker and orchestrating them with ECS and EKS.
• Model Lifecycle: Experience with model testing validation and performance monitoring. Good understanding of ML frameworks like PyTorch or TensorFlow is required to effectively collaborate with data scientists.
• Communication: Excellent communication and documentation skills with a proven ability to collaborate with cross-functional teams (data scientists data engineers and architects).