Overview
Hybrid
Depends on Experience
Contract - W2
Contract - 6 Month(s)
Skills
DevOps/MLOps in cloud and production environments.
Job Details
Software Engineer 4
Start: ASAP, Estimate End date: 04/20/2026
Location: San Jose, CA, Hybrid
Responsibilities and duties:
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: Enhance and 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.
Requirements:
Master's in computer science or related STEM field
Minimum 5 years in software engineering; at least 2 years dedicated to DevOps/MLOps in cloud and production environments.
Industry experiences building end-to-end software pipelines and infrastructure with deep experience with Kubernetes, Infrastructure as Code (Terraform, CloudFormation), AWS, and Google Cloud Platform.
Expert proficiency in Python; working knowledge of ML frameworks (e.g., PyTorch, TensorFlow, MLflow)
Practical experience with cloud and NoSQL databases such as DynamoDB; SQL databases a plus.
Skilled with GitHub Actions, Jenkins, GitLab CI, Docker, and related automation platforms.
Exposure to Computer Vision, Generative AI (GAN, CLIP, Diffusion, MLLM), and their practical deployment for evaluation systems.
Experience in integrating ML workflows with user-facing features and backend pipelines.
Strong problem-solving, excellent written/verbal communication, and the ability to lead and collaborate effectively across teams.
Python, Typescript, Shell script languages
Experience with ML pipeline tools (Kubeflow, Airflow, MLflow)
Services on AWS such as S3, Lambda, DynamoDB
CI/CD systems (GitHub Actions, Jenkins, GitLab)
Infrastructure-as-Code experience (Terraform, CloudFormation)
Containerization (Docker, Kubernetes)
Communication and documentation skills
Strong problem-solving skills and the ability to work collaboratively across teams.
Strong knowledge of ML-Ops a bonus
CI/CD systems (GitHub Actions, Jenkins, GitLab)
Infrastructure-as-Code experience (Terraform, CloudFormation)
Start: ASAP, Estimate End date: 04/20/2026
Location: San Jose, CA, Hybrid
Responsibilities and duties:
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: Enhance and 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.
Requirements:
Master's in computer science or related STEM field
Minimum 5 years in software engineering; at least 2 years dedicated to DevOps/MLOps in cloud and production environments.
Industry experiences building end-to-end software pipelines and infrastructure with deep experience with Kubernetes, Infrastructure as Code (Terraform, CloudFormation), AWS, and Google Cloud Platform.
Expert proficiency in Python; working knowledge of ML frameworks (e.g., PyTorch, TensorFlow, MLflow)
Practical experience with cloud and NoSQL databases such as DynamoDB; SQL databases a plus.
Skilled with GitHub Actions, Jenkins, GitLab CI, Docker, and related automation platforms.
Exposure to Computer Vision, Generative AI (GAN, CLIP, Diffusion, MLLM), and their practical deployment for evaluation systems.
Experience in integrating ML workflows with user-facing features and backend pipelines.
Strong problem-solving, excellent written/verbal communication, and the ability to lead and collaborate effectively across teams.
Python, Typescript, Shell script languages
Experience with ML pipeline tools (Kubeflow, Airflow, MLflow)
Services on AWS such as S3, Lambda, DynamoDB
CI/CD systems (GitHub Actions, Jenkins, GitLab)
Infrastructure-as-Code experience (Terraform, CloudFormation)
Containerization (Docker, Kubernetes)
Communication and documentation skills
Strong problem-solving skills and the ability to work collaboratively across teams.
Strong knowledge of ML-Ops a bonus
CI/CD systems (GitHub Actions, Jenkins, GitLab)
Infrastructure-as-Code experience (Terraform, CloudFormation)
Uday Raj Manager at Onwardpath 2701 Larsen Rd #BA142, Green Bay, WI 54303 Contact: +1 |
Certified WBE & MBE |
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