Job Description
Team: IT
This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior Platform/MLOps Engineer in United States.
This role sits at the intersection of advanced AI infrastructure and next-generation manufacturing, where software, robotics, and machine learning converge to redefine how modern factories operate. You will help build and scale the core platform that powers AI-driven inspection, computer vision, and deep learning systems used in real-world industrial environments. The work involves designing highly reliable MLOps pipelines that support training, deployment, and inference at scale, including GPU-based workloads running in Kubernetes. This is a high-impact engineering position where your contributions directly influence production systems used by leading global manufacturers. You will collaborate closely with robotics, platform, and AI teams in a deeply technical, fast-paced environment. The role is ideal for engineers who enjoy solving complex distributed systems challenges while working on cutting-edge AI applications. It combines strong software engineering rigor with real-world physical deployment impact.
Accountabilities:
- Design, build, and maintain scalable, secure, and highly reliable infrastructure supporting AI/ML pipelines, including training, deployment, and inference workflows.
- Develop and optimize MLOps systems for computer vision and deep learning models used in industrial inspection and automation use cases.
- Deploy and manage GPU-intensive workloads on Kubernetes, ensuring performance, scalability, and cost efficiency.
- Build and maintain CI/CD pipelines, GitOps workflows, and infrastructure-as-code solutions to support continuous delivery.
- Write clean, maintainable code and participate in peer code reviews to ensure engineering quality and consistency.
- Collaborate with cross-functional teams to prototype new technologies and evaluate technical feasibility of emerging solutions.
- Improve platform usability and developer velocity through continuous iteration based on real-world usage and feedback.
- 5+ years of experience in Platform Engineering, DevOps, Site Reliability Engineering, or similar infrastructure-focused roles.
- Strong proficiency in at least one modern programming language such as Python, Go, JavaScript, or C#.
- Hands-on experience building and operating MLOps systems in production environments.
- Deep knowledge of Kubernetes, including GPU workload orchestration and modern CNCF ecosystem tools.
- Experience with CI/CD pipelines, GitOps practices, and Infrastructure as Code tools such as Terraform or Ansible.
- Familiarity with observability tooling such as Prometheus, Grafana, and OpenTelemetry.
- Strong understanding of software engineering best practices across the full development lifecycle.
- Excellent collaboration and communication skills with the ability to work across engineering teams.
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent experience.
- Willingness to travel up to 25%.
- Opportunity to work on cutting-edge AI and robotics systems powering next-generation manufacturing.
- High-impact role contributing directly to real-world industrial and AI infrastructure deployments.
- Collaborative engineering culture focused on innovation, quality, and continuous improvement.
- Exposure to large-scale Kubernetes, GPU computing, and advanced MLOps architectures.
- Career growth opportunities in a rapidly evolving AI and industrial automation environment.
- Work alongside highly skilled engineers across platform, robotics, and AI disciplines.
- Competitive compensation and comprehensive benefits (exact package not specified in the source).
Requirements:
Benefits:
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Date Posted
05/15/2026
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