Cypress HCM
Overview
This range is provided by Cypress HCM. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more. Base pay range: $85.00/hr - $88.00/hr Direct message the job poster from Cypress HCM Senior Software Engineer role focused on ML-Ops, building infrastructure to deploy, monitor, and manage machine learning models. The work bridges research and engineering to ensure AI solutions are scalable, reliable, and integrated into products. Requires thriving in a fast-paced environment with a passion for AI/ML and a drive to optimize and automate ML workflows.
Responsibilities
Pipeline Development: Implement, optimize, and maintain CI/CD pipelines for ML systems, including integrations with GitHub workflows and Jenkins.
Collaboration: Partner with data scientists, frontend engineers, and platform teams to deliver seamless integration of ML models into core evaluation platforms.
Environment Management: Administer ML development/production environments using cloud-native solutions; optimize for scalability, reliability, and cost.
Tooling and Automation: Evaluate, build, and deploy automation tools to streamline the end-to-end ML lifecycle.
Quality & Monitoring: Develop quality evaluation features and ensure robust monitoring via dashboards and automated alerts.
Documentation & Best Practices: Champion engineering best practices, promote code quality, and document workflows, tools, and processes for effective team adoption.
Required Skills & Qualifications
Python, TypeScript, Shell scripting
Experience with ML pipeline tools (Kubeflow, Airflow, MLflow)
AWS services (S3, Lambda, DynamoDB)
CI/CD systems (GitHub Actions, Jenkins, GitLab)
Infrastructure-as-Code (Terraform, CloudFormation)
Strong communication and documentation skills
Strong problem-solving abilities and collaboration across teams
Knowledge of ML-Ops is a bonus
Candidate Profile
Master's degree (preferred) in computer science or related STEM field
Minimum 5 years in software engineering; at least 2 years in DevOps/MLOps in cloud/production environments
Experience building end-to-end software pipelines and infrastructure with Kubernetes, Terraform/CloudFormation, AWS, and GCP
Expert proficiency in Python; working knowledge of ML frameworks (e.g., PyTorch, TensorFlow, MLflow)
Experience with cloud and NoSQL databases (DynamoDB); SQL databases a plus
Proficient with GitHub Actions, Jenkins, GitLab CI, Docker, and related automation platforms
Exposure to Computer Vision, Generative AI (GAN, CLIP, Diffusion, MLLM) and practical deployment for evaluation systems
Experience integrating ML workflows with user-facing features and backend pipelines
Strong problem-solving skills and excellent written/verbal communication; ability to lead and collaborate across teams
Compensation
Up to $88.03 per hour
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This range is provided by Cypress HCM. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more. Base pay range: $85.00/hr - $88.00/hr Direct message the job poster from Cypress HCM Senior Software Engineer role focused on ML-Ops, building infrastructure to deploy, monitor, and manage machine learning models. The work bridges research and engineering to ensure AI solutions are scalable, reliable, and integrated into products. Requires thriving in a fast-paced environment with a passion for AI/ML and a drive to optimize and automate ML workflows.
Responsibilities
Pipeline Development: Implement, optimize, and maintain CI/CD pipelines for ML systems, including integrations with GitHub workflows and Jenkins.
Collaboration: Partner with data scientists, frontend engineers, and platform teams to deliver seamless integration of ML models into core evaluation platforms.
Environment Management: Administer ML development/production environments using cloud-native solutions; optimize for scalability, reliability, and cost.
Tooling and Automation: Evaluate, build, and deploy automation tools to streamline the end-to-end ML lifecycle.
Quality & Monitoring: Develop quality evaluation features and ensure robust monitoring via dashboards and automated alerts.
Documentation & Best Practices: Champion engineering best practices, promote code quality, and document workflows, tools, and processes for effective team adoption.
Required Skills & Qualifications
Python, TypeScript, Shell scripting
Experience with ML pipeline tools (Kubeflow, Airflow, MLflow)
AWS services (S3, Lambda, DynamoDB)
CI/CD systems (GitHub Actions, Jenkins, GitLab)
Infrastructure-as-Code (Terraform, CloudFormation)
Strong communication and documentation skills
Strong problem-solving abilities and collaboration across teams
Knowledge of ML-Ops is a bonus
Candidate Profile
Master's degree (preferred) in computer science or related STEM field
Minimum 5 years in software engineering; at least 2 years in DevOps/MLOps in cloud/production environments
Experience building end-to-end software pipelines and infrastructure with Kubernetes, Terraform/CloudFormation, AWS, and GCP
Expert proficiency in Python; working knowledge of ML frameworks (e.g., PyTorch, TensorFlow, MLflow)
Experience with cloud and NoSQL databases (DynamoDB); SQL databases a plus
Proficient with GitHub Actions, Jenkins, GitLab CI, Docker, and related automation platforms
Exposure to Computer Vision, Generative AI (GAN, CLIP, Diffusion, MLLM) and practical deployment for evaluation systems
Experience integrating ML workflows with user-facing features and backend pipelines
Strong problem-solving skills and excellent written/verbal communication; ability to lead and collaborate across teams
Compensation
Up to $88.03 per hour
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