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Description About CloudBees CloudBees is the leading software delivery platform for modern enterprises, helping organizations accelerate innovation by streamlining and scaling DevOps. As a startup rooted in the developer operations space, we’re building the next generation of intelligent software delivery tools—powered by AI, built for scale. As a member of the AI Foundations founding team, this is a high-impact role that sits at the intersection of artificial intelligence and software delivery – ideal for someone passionate about pushing the boundaries of developer productivity and intelligent automation. The Role

CloudBees is seeking a

Machine Learning Engineer

with hands-on experience in

generative AI platforms

like

Amazon Bedrock

and

Google Vertex AI , as well as expertise in building and maintaining scalable ML pipelines. In this role, you will design, implement, and optimize ML workflows and models that support intelligent automation and insights across the CloudBees product suite. You’ll be part of a fast-paced, collaborative, and forward-thinking team, with the opportunity to make a direct impact on product direction and user experience. With the rapidly changing AI ecosystem, being able to apply the right tool at the right time and identify areas the team needs to invest in is critical. Key Responsibilities

Design, build, and maintain production-ready ML pipelines, from data ingestion to model deployment and monitoring. Integrate and fine-tune foundation models and generative AI services using Amazon Bedrock, Google Vertex AI, or similar platforms. Collaborate with software engineers, product managers, and data scientists to deliver intelligent features embedded in DevOps tools. Automate model retraining, versioning, testing, and CI/CD processes across environments. Implement observability and reliability into ML systems, ensuring scalability and performance. Contribute to the technical roadmap for AI/ML development within the CloudBees platform. Document systems and share best practices across teams. Required Qualifications

4+ years of experience in machine learning engineering or applied ML roles. Hands-on experience with

Amazon Bedrock

and/or

Google Vertex AI

for deploying and customizing foundation models. Strong understanding of ML lifecycle management and workflow orchestration (e.g., Airflow, Kubeflow, MLflow). Experience with Python, TensorFlow, PyTorch, or similar ML frameworks. Proficiency in cloud platforms (AWS and/or GCP) and containerized environments (e.g., Docker, Kubernetes). Familiar with the modern LLM training stack (eg. the Hugging Face ecosystem, PEFT, etc.) Experience in a

startup

or fast-paced product development environment. Familiarity with version control, CI/CD, and infrastructure as code (e.g., Terraform, GitHub Actions). Experience with evaluations LLM - large language model Preferred Qualifications

Background in generative AI applications (e.g., LLMs, embeddings, RAG architecture). Experience integrating ML systems into SaaS or DevOps platforms. Knowledge of data governance, privacy, and security in machine learning applications. Why Join CloudBees

Be part of an innovative, AI-driven future in the DevOps ecosystem. Work in a dynamic, remote-first startup environment with global reach. Influence technical strategy and contribute to high-impact product capabilities. Competitive compensation, equity options, and benefits. CloudBees is an Equal Opportunity Employer.

We celebrate diversity and are committed to building an inclusive team.

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