📍 Job Overview

Job Title: Senior AI Infrastructure Engineer
Company: CyberArk
Location: Newton, Massachusetts
Job Type: Full-time
Category: DevOps Engineer
Date Posted: June 18, 2025
Experience Level: 5-10 years
Remote Status: Remote OK

🚀 Role Summary

Lead the deployment and maintenance of internal AI infrastructure to enable self-service, low/no code, and enterprise solutions across CyberArk.
Collaborate with the Director, AI Technologies and the wider initiative technology and business team to ensure AI infrastructure availability for all users.
Manage the full lifecycle of AI/ML model creation, including development, testing, deployment, and monitoring.
Support enterprise technology for CI/CD, DevOps, and orchestration.

📝 Enhancement Note: This role requires a strong background in both AI/ML and infrastructure management to succeed in enabling data scientists, data engineers, and developers to deliver value efficiently and at scale.

💻 Primary Responsibilities

Infrastructure Deployment & Management: Deploy, monitor, and maintain ML/AI models and infrastructure for development, testing/staging, and production.
Self-Service Infrastructure: Ensure infrastructure is self-service and supports both standard development and low/no code solutions.
Enterprise Technology Support: Support enterprise technology for CI/CD, DevOps, and orchestration.
Agile Leadership: Lead/facilitate Agile ceremonies (standup, sprint planning, retrospective) and proactively maintain infrastructure to prevent issues before they arise.

🎓 Skills & Qualifications

Education: Bachelor's degree in Computer Science, Engineering, or a related field. Relevant experience may be considered in lieu of a degree.

Experience: 5-10 years of experience in AI/ML infrastructure, with a strong focus on production infrastructure, DevOps, CI/CD, and orchestration tools.

Required Skills:

Experience with production infrastructure.
Experience with DevOps, CI/CD, and orchestration tooling (Ansible, Terraform, Puppet, Jenkins, GitLab, Azure DevOps).
Experience in container technologies (Docker, Podman, Kubernetes, OpenShift, Cloud Foundry).
Experience with cloud computing providers (AWS, Azure, GCP).
Experience with Agile development methodologies (Scrum, Kanban).
Excellent communication, documentation, and interpersonal skills.

AI-Specific Skills:

Experience with machine learning frameworks (TensorFlow, PyTorch, or Keras).
Knowledge of AI model deployment and scaling.
Experience with AI/ML lifecycle management and MLOps practices/tools.
Familiarity with AI infrastructure tools and platforms (Kubeflow, MLflow, or Azure Machine Learning).
Understanding of data engineering principles and experience with data pipelines.

Preferred Skills:

Interest in the DevOps landscape and a passion for quickly adopting new tools and languages.
Experience with managing technical debt and writing/maintaining great code.
Familiarity with feedback loops, especially with real customers who use created solutions.

📝 Enhancement Note: Candidates with a strong background in both AI/ML and infrastructure management, as well as a proven track record of enabling data scientists, data engineers, and developers to deliver value efficiently, will be highly competitive for this role.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Demonstrate experience in AI/ML model deployment, monitoring, and maintenance through relevant projects or case studies.
Showcase proficiency in infrastructure deployment and management, with a focus on self-service solutions and enterprise technology support.
Highlight problem-solving skills and the ability to proactively maintain infrastructure to prevent issues.

Technical Documentation:

Provide documentation showcasing experience with AI/ML lifecycle management, MLOps practices/tools, and data engineering principles.
Demonstrate understanding of AI infrastructure tools and platforms through relevant project documentation.

💵 Compensation & Benefits

Salary Range: $94,000 - $130,000/year, plus commissions or discretionary bonus based on performance.

Benefits:

Medical, dental, vision, financial, and other benefits.

Working Hours: 40 hours per week, with flexible scheduling for deployment windows, maintenance, and project deadlines.

📝 Enhancement Note: The provided salary range is based on market research for senior-level AI/ML infrastructure roles in the Boston, MA area. Actual compensation may vary based on individual qualifications, skills, and experience.

🎯 Team & Company Context

🏢 Company Culture

Industry: Cybersecurity, with a focus on identity security and privileged access management.

Company Size: Medium to large (approximately 1,000 employees).

Founded: 1999, with a strong history in the cybersecurity industry and a commitment to innovation and growth.

Team Structure:

The team provides support to the entire Transforming Our Work initiative by building, automating, and maintaining internal infrastructure.
It consists of development in support of internal tools, infrastructure for testing and development, and enterprise technology support for CI/CD, DevOps, and orchestration.
The team works closely with the Director, AI Technologies and the wider initiative technology and business team to ensure AI infrastructure availability for all users throughout CyberArk.

Development Methodology:

Agile development methodologies, such as Scrum and Kanban, are used to manage projects and ensure efficient delivery.
The team follows best practices for code review, testing, and quality assurance to maintain high standards in AI/ML model creation and infrastructure management.
CI/CD pipelines and automated deployment strategies are employed to streamline the software development lifecycle.

Company Website: www.cyberark.com

📝 Enhancement Note: CyberArk's commitment to innovation and growth in the cybersecurity industry creates an exciting environment for AI/ML infrastructure professionals looking to make a significant impact.

📈 Career & Growth Analysis

AI Infrastructure Engineer Career Level: This role represents a senior-level position in AI/ML infrastructure, focusing on enabling data scientists, data engineers, and developers to deliver value efficiently and at scale.

Reporting Structure: The role reports directly to the Director, AI Technologies and collaborates with the wider initiative technology and business team to ensure AI infrastructure availability for all users throughout CyberArk.

Technical Impact: The Senior AI Infrastructure Engineer plays a critical role in ensuring the availability and performance of AI/ML models and infrastructure, directly impacting the company's ability to leverage AI and automation in its products and services.

Growth Opportunities:

Technical Leadership: As a senior member of the team, there is potential for growth into technical leadership roles, such as a Principal or Staff AI Infrastructure Engineer, focusing on driving technical strategy and mentoring junior team members.
Architecture Decision-Making: With experience and proven success, the role may evolve to include making critical architecture decisions that shape the company's AI/ML infrastructure landscape.
Emerging Technology Adoption: Staying up-to-date with the latest AI/ML and infrastructure trends, the role may involve driving the adoption of emerging technologies to improve the company's competitive position.

📝 Enhancement Note: The Senior AI Infrastructure Engineer role at CyberArk offers significant growth potential for technical professionals looking to make a substantial impact in the AI/ML infrastructure space.

🌐 Work Environment

Office Type: CyberArk's Newton, MA office offers a modern, collaborative workspace designed to foster innovation and teamwork.

Office Location(s): Newton, MA, with remote work options available.

Workspace Context:

Collaborative Workspace: The office features open-plan workspaces, encouraging collaboration and communication among team members.
Development Tools & Resources: CyberArk provides access to the latest development tools, multiple monitors, and testing devices to ensure optimal productivity.
Cross-Functional Collaboration: The office layout facilitates interaction with other teams, such as design, marketing, and business teams, promoting a cross-functional approach to problem-solving and innovation.

Work Schedule: Flexible scheduling is available to accommodate deployment windows, maintenance, and project deadlines. The standard workweek is 40 hours, with core hours between 9:00 AM and 5:00 PM EST.

📝 Enhancement Note: CyberArk's commitment to fostering a collaborative and innovative work environment creates an ideal setting for AI/ML infrastructure professionals seeking to grow both personally and professionally.

📄 Application & Technical Interview Process

Interview Process:

Phone/Screen: A brief phone or video call to assess communication skills, technical proficiency, and cultural fit.
Technical Assessment: A hands-on technical assessment focused on AI/ML model deployment, monitoring, and infrastructure management. This may include live coding exercises, system design discussions, and problem-solving challenges.
On-site/Video Interview: A comprehensive on-site or video interview with team members, focusing on technical depth, problem-solving skills, and cultural fit.
Final Evaluation: A final evaluation of the candidate's technical skills, cultural fit, and potential impact on the team and company.

Portfolio Review Tips:

Highlight relevant AI/ML model deployment, monitoring, and infrastructure management projects that demonstrate your ability to enable data scientists, data engineers, and developers to deliver value efficiently and at scale.
Include case studies showcasing your problem-solving skills, proactive infrastructure maintenance, and self-service solution development.
Tailor your portfolio to CyberArk's focus on identity security and privileged access management, emphasizing how your work has contributed to improving security and user experience.

Technical Challenge Preparation:

Brush up on your knowledge of AI/ML model deployment, monitoring, and infrastructure management best practices.
Familiarize yourself with relevant tools and platforms, such as Kubernetes, MLflow, and cloud computing providers (AWS, Azure, GCP).
Prepare for system design discussions and problem-solving challenges related to AI/ML infrastructure management.

ATS Keywords: [List of relevant AI/ML infrastructure, DevOps, and cloud computing keywords]

📝 Enhancement Note: CyberArk's interview process is designed to assess the candidate's technical proficiency, problem-solving skills, and cultural fit, with a strong emphasis on AI/ML infrastructure management and enabling data scientists, data engineers, and developers to deliver value efficiently and at scale.

🛠 Technology Stack & Web Infrastructure

AI/ML Infrastructure Tools:

Kubernetes: Experience with Kubernetes or other container orchestration platforms is essential for managing AI/ML model deployment and scaling.
MLflow: Familiarity with MLflow or other MLOps platforms is required for AI/ML lifecycle management and model tracking.
Cloud Computing Providers: Proficiency in one or more cloud computing providers (AWS, Azure, GCP) is necessary for managing AI/ML infrastructure and ensuring high availability and scalability.

DevOps & Infrastructure Tools:

Ansible, Terraform, Puppet: Experience with these or other configuration management and orchestration tools is required for infrastructure deployment and maintenance.
Jenkins, GitLab, Azure DevOps: Familiarity with these or other CI/CD pipelines and automation tools is essential for streamlining the software development lifecycle.
Docker, Podman: Proficiency in container technologies, such as Docker or Podman, is required for managing AI/ML model deployment and infrastructure.

📝 Enhancement Note: CyberArk's technology stack is designed to support the full lifecycle of AI/ML model creation, deployment, monitoring, and maintenance, with a strong focus on infrastructure management and enabling data scientists, data engineers, and developers to deliver value efficiently and at scale.

👥 Team Culture & Values

AI/ML Infrastructure Values:

Collaboration: CyberArk values a collaborative approach to problem-solving, with a strong emphasis on cross-functional teamwork and knowledge sharing.
Innovation: The company fosters a culture of innovation, encouraging team members to explore new technologies and approaches to drive continuous improvement.
Customer Focus: CyberArk is committed to understanding and addressing the unique needs of its customers, with a strong focus on delivering value and improving user experience.
Integrity: The company values integrity in all aspects of its operations, with a commitment to ethical behavior and responsible AI/ML practices.

Collaboration Style:

Cross-Functional Integration: CyberArk encourages collaboration between AI/ML infrastructure teams and other departments, such as design, marketing, and business teams, to ensure alignment with company objectives and user needs.
Code Review Culture: The company promotes a culture of code review and peer programming, with a strong emphasis on knowledge sharing and continuous learning.
Mentoring & Development: CyberArk offers mentoring and development opportunities to help team members grow both personally and professionally, with a focus on technical skill development and emerging technology adoption.

📝 Enhancement Note: CyberArk's culture is designed to foster collaboration, innovation, and customer focus, with a strong emphasis on enabling data scientists, data engineers, and developers to deliver value efficiently and at scale through effective AI/ML infrastructure management.

⚡ Challenges & Growth Opportunities

Technical Challenges:

AI/ML Model Deployment & Scaling: Address the challenges of deploying and scaling AI/ML models efficiently and effectively, ensuring high availability and performance.
Infrastructure Management: Manage the full lifecycle of AI/ML infrastructure, from development and testing to production deployment and maintenance.
Self-Service Solutions: Develop and maintain self-service solutions that enable data scientists, data engineers, and developers to deliver value efficiently and at scale.
Emerging Technologies: Stay up-to-date with the latest AI/ML and infrastructure trends, and drive the adoption of emerging technologies to improve CyberArk's competitive position.

Learning & Development Opportunities:

Technical Skill Development: CyberArk offers opportunities for technical skill development, with a focus on emerging AI/ML and infrastructure technologies.
Conference Attendance & Certification: The company supports employee attendance at relevant conferences and certifications, fostering continuous learning and professional development.
Technical Mentorship & Leadership: CyberArk provides mentorship and leadership opportunities, with a focus on driving technical strategy and architecture decision-making.

📝 Enhancement Note: The Senior AI Infrastructure Engineer role at CyberArk presents significant technical challenges and growth opportunities for AI/ML infrastructure professionals seeking to make a substantial impact in the field.

💡 Interview Preparation

Technical Questions:

AI/ML Model Deployment & Scaling: Prepare for questions related to AI/ML model deployment, monitoring, and scaling, with a focus on efficiency, effectiveness, and high availability.
Infrastructure Management: Brush up on your knowledge of AI/ML infrastructure management, with a focus on full lifecycle support, self-service solutions, and enterprise technology integration.
Problem-Solving: Prepare for problem-solving challenges related to AI/ML infrastructure management, with a focus on system design, architecture, and optimization.

Company & Culture Questions:

Company Culture: Research CyberArk's company culture, values, and mission, and prepare thoughtful questions that demonstrate your understanding and alignment with the organization's goals.
AI/ML Infrastructure Impact: Prepare for questions related to the impact of AI/ML infrastructure on CyberArk's products, services, and user experience, with a focus on enabling data scientists, data engineers, and developers to deliver value efficiently and at scale.
Technical Strategy & Architecture: Prepare for questions related to technical strategy and architecture decision-making, with a focus on driving innovation and competitive advantage in the AI/ML infrastructure space.

Portfolio Presentation Strategy:

Live Demonstration: Prepare a live demonstration of your AI/ML model deployment, monitoring, and infrastructure management projects, with a focus on enabling data scientists, data engineers, and developers to deliver value efficiently and at scale.
Code Explanation: Practice explaining your code and architecture decisions clearly and concisely, with a focus on user experience and performance optimization.
User Experience Showcase: Prepare a showcase of your user experience design and interface development projects, with a focus on improving user experience and accessibility in AI/ML infrastructure management.

📝 Enhancement Note: CyberArk's interview process is designed to assess the candidate's technical proficiency, problem-solving skills, and cultural fit, with a strong emphasis on AI/ML infrastructure management and enabling data scientists, data engineers, and developers to deliver value efficiently and at scale.

📌 Application Steps

To apply for this Senior AI Infrastructure Engineer position at CyberArk:

Submit Your Application: Visit the CyberArk careers page and search for the Senior AI Infrastructure Engineer position. Click on the job title to view the job description and submit your application.
Prepare Your Portfolio: Tailor your portfolio to highlight relevant AI/ML model deployment, monitoring, and infrastructure management projects that demonstrate your ability to enable data scientists, data engineers, and developers to deliver value efficiently and at scale. Include case studies showcasing your problem-solving skills, proactive infrastructure maintenance, and self-service solution development.
Optimize Your Resume: Highlight your relevant AI/ML infrastructure, DevOps, and cloud computing skills and experiences in your resume. Emphasize your problem-solving skills, proactive infrastructure maintenance, and self-service solution development.
Prepare for Technical Interview: Brush up on your knowledge of AI/ML model deployment, monitoring, and infrastructure management best practices. Familiarize yourself with relevant tools and platforms, such as Kubernetes, MLflow, and cloud computing providers (AWS, Azure, GCP). Prepare for system design discussions and problem-solving challenges related to AI/ML infrastructure management.
Research CyberArk: Familiarize yourself with CyberArk's company culture, values, and mission. Prepare thoughtful questions that demonstrate your understanding and alignment with the organization's goals.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and AI/ML infrastructure industry-standard assumptions. All details should be verified directly with CyberArk before making application decisions.

Senior AI Infrastructure Engineer