Offer summary

Qualifications:

Minimum of 7 years of engineering experience, including platform development or related fields., At least 3 years of leadership experience managing engineering teams., Hands-on experience with large-scale, reliable, and scalable systems, including AI/ML systems., Proficiency with MLOps tools like MLflow, Kubeflow, or SageMaker, and cloud infrastructure such as AWS, GCP, or Azure..

Key responsibilities:

Lead and scale the AI & ML operations team, focusing on model deployment and lifecycle management.

Design and implement scalable, observable AI/ML pipelines.

Collaborate with cross-functional teams to integrate AI/ML systems into the platform.

Establish best practices for AI/ML experimentation, CI/CD, and model governance.

Job description

Description

About CloudBees

CloudBees is the leading software delivery platform for modern enterprises, enabling companies to continuously innovate at scale. As a startup operating in the DevOps space, we empower developers and teams to build, test, and deploy software faster and more reliably. Our mission is to simplify complexity and help organizations deliver better software, faster.

The Role

CloudBees is seeking a visionary and hands-on Engineering Leader to drive our Agentic & AI Operations (AIOps) strategy. Lead the development of the Cloudbees AI platform designed to support the fine-tuning, deployment, and management of AI, ML, and Agentic Services; Guiding the strategic direction of the engineering team, with a primary focus on platform reliability, scalability, and maintainability. Build and oversee robust systems that empower our customers to optimize and personalize AI and Agents to their specific needs. You will lead a growing team of engineers focused on building reliable, scalable AI & ML infrastructure and pipelines that power intelligent features across our platform.

We’re looking for someone with startup experience, a passion for AI & ML tooling, and deep understanding of operationalizing AI/ML workflows. You'll partner closely with data scientists, product managers, and platform engineers to transform AI ideas into production-grade, secure, and efficient systems.

As the founding Engineering leader of AI Foundations 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.

Key Responsibilities

Lead and scale a team responsible for AIOps, including model deployment, monitoring, and lifecycle management.
Architect and implement AI/ML pipelines that are scalable, observable, and reproducible.
Collaborate with cross-functional teams (data science, DevOps, product) to integrate AI/ML systems into our SaaS platform.
Establish best practices for AI/ML experimentation, CI/CD for models, data versioning, and model governance.
Own the full stack of AIOps infrastructure, from data ingestion to real-time inference systems.
Drive technical vision and roadmap for ML platform development.
Act as a mentor and coach, helping engineers grow in a fast-paced, startup environment.
Manage a team of 5+
Ability to launch new platforms 0 - 1 and drive adoption internally and externally with partner teams.

Qualifications

Must-Haves:

7+ years of engineering experience, including platform engineering, system development, or related roles with at least 3 years in leadership roles.
3 years of experience with large-scale systems, with a focus on reliability, scalability, and maintainability; and 1 year of experience with AI/ML systems
Strong hands-on experience with MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Airflow, Metaflow).
Proven track record building ML pipelines in production environments.
Experience with cloud infrastructure (AWS, GCP, or Azure) and container orchestration (Kubernetes).
Deep knowledge of CI/CD practices as they relate to ML lifecycle.
Prior experience in a startup or fast-paced SaaS environment.
Strong collaboration and communication skills.
Experience deploying and managing services such as Amazon bedrock or Vertex AI - LLm

Nice-to-Haves:

Experience integrating ML capabilities into developer-centric tools or platforms.
Familiarity with data observability and ML monitoring tools (e.g., EvidentlyAI, Prometheus/Grafana for models).
Knowledge of data privacy, compliance, and security in ML systems.

Why Join CloudBees

Work at the forefront of DevOps innovation and shape how ML supports developer productivity.
Join a high-impact, mission-driven startup backed by top investors.
A flexible remote work culture with global teammates.
Competitive compensation, stock options, and benefits.

CloudBees is proud to be an Equal Opportunity Employer. We embrace diversity and are committed to creating an inclusive environment for all employees.

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