Overview
Hybrid
Full Time
Accepts corp to corp applications
Contract - Independent
Contract - W2
Contract - 6+ MONTHS
Skills
Video
FOCUS
Workflow
Amazon Web Services
Amazon S3
Python
Terraform
GitHub
DevOps
Machine Learning Operations (ML Ops)
Amazon SageMaker
Docker
Testing
Performance Monitoring
Machine Learning (ML)
PyTorch
TensorFlow
Communication
Documentation
Collaboration
Job Details
Tittle: Lead MLOps Engineer
Localtion: Dallas, TX Hybrid || Only Locals can apply
Interview Process: Video Call + In-person Interview
Skills:
MLOps Lead
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 workflows
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
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)
MLOps Lead
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 workflows
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
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)
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.