📍 Job Overview

Job Title: DevOps Engineer 3 (ML Infra)
Company: Behavox
Location: Canada
Job Type: Full-Time
Category: DevOps Engineer
Date Posted: June 19, 2025
Experience Level: Mid-Senior level (2-5 years)
Remote Status: Remote OK

🚀 Role Summary

Key Responsibilities: Manage and troubleshoot complex distributed software systems, build scalable container-based infrastructure, design cloud infrastructure, automate software delivery processes, and develop tools and services.
Key Technologies: Linux, AWS, GCP, Kubernetes, Python, Bash, CI/CD.
Impact: Directly contribute to the efficiency of ML models, the heart of the company's success.

📝 Enhancement Note: This role offers a unique opportunity to work on greenfield projects with significant impact, using the best tools and frameworks to fulfill requirements.

💻 Primary Responsibilities

System Management: Manage and troubleshoot complex distributed large-scale software systems.
Infrastructure Design: Build scalable, secure, and reliable container-based infrastructure using Kubernetes.
Cloud Architecture: Design and manage infrastructure in public clouds (AWS, GCP).
Automation: Automate software delivery processes with CI/CD pipelines.
Tool Development: Develop tools, services, and scripts using Python and Bash.

📝 Enhancement Note: This role requires strong problem-solving skills and the ability to work in a dynamic, fast-paced environment.

🎓 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: 2-5 years of experience in Linux system administration, cloud infrastructure, and Kubernetes.

Required Skills:

Linux system administration expertise
Solid knowledge of Linux and networks
Hands-on experience with Kubernetes
Development and scripting skills (Python, Bash)
Experience with cloud infrastructure (AWS, GCP)

Preferred Skills:

Experience with data infrastructure
Familiarity with ML services and models
Knowledge of CI/CD/CT processes

📝 Enhancement Note: Candidates with experience in machine learning infrastructure and data pipelines will have a significant advantage.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Demonstrate experience with Linux system administration, cloud infrastructure, and Kubernetes.
Showcase projects that involve managing complex distributed systems and building scalable infrastructure.
Highlight any experience with ML services and data infrastructure.

Technical Documentation:

Provide code samples and documentation demonstrating your problem-solving skills and technical expertise.
Include any relevant case studies or project presentations that showcase your ability to manage and troubleshoot complex systems.

📝 Enhancement Note: Be prepared to discuss your portfolio projects in detail, focusing on the challenges faced and how you overcame them.

💵 Compensation & Benefits

Salary Range: Competitive cash compensation package, commensurate with experience and qualifications. [Research methodology: Based on industry standards for mid-senior level DevOps engineers in Canada, considering the role's complexity and the company's size and reputation.]

Benefits:

A truly global mission with a passionate community in locations all over the world.
The ability to have a high impact and learning potential as the company's aspirations require bold innovation.
Highly competitive cash compensation package.
Comprehensive health coverage for the employee and their family.
Generous time-off policy (30 days annually) and flexible work schedule.

Working Hours: Full-time position with standard business hours, flexible for deployment windows and maintenance.

📝 Enhancement Note: Be prepared to discuss salary expectations and benefits during the initial screening process.

🎯 Team & Company Context

🏢 Company Culture

Industry: Behavox operates in the data management and machine learning space, focusing on enterprise risk and compliance management, as well as revenue maximization and value creation.

Company Size: Medium-sized company with a global presence, offering a collaborative and innovative work environment.

Founded: 2014, with a history of growth and expansion in the data management and machine learning industry.

Team Structure:

The ML Infra team is responsible for designing and building cutting-edge tooling, infrastructure, and web interfaces for efficient ML model development.
The team works closely with other departments, such as data engineering and data science, to ensure seamless integration and collaboration.

Development Methodology:

Agile development methodologies, with a focus on continuous integration, continuous delivery, and continuous testing (CI/CD/CT).
Regular code reviews, testing, and quality assurance practices to ensure high code quality and system reliability.
Deployment strategies, including CI/CD pipelines and automated deployment, to streamline the software delivery process.

Company Website: behavox.com

📝 Enhancement Note: Behavox's focus on data-driven decision-making and machine learning innovation creates a dynamic and challenging work environment for DevOps engineers.

📈 Career & Growth Analysis

Web Technology Career Level: Mid-Senior level DevOps engineer, responsible for managing and troubleshooting complex distributed systems, building scalable infrastructure, and automating software delivery processes.

Reporting Structure: This role reports directly to the ML Infra team lead and works closely with other teams, such as data engineering and data science, to ensure efficient ML model development and deployment.

Technical Impact: Directly contribute to the efficiency of ML models, the heart of the company's success, by managing and troubleshooting complex distributed systems, building scalable infrastructure, and automating software delivery processes.

Growth Opportunities:

Technical Growth: Expand your expertise in machine learning infrastructure, data pipelines, and emerging technologies.
Leadership Development: Gain experience in team management and architecture decision-making as the team grows and evolves.
Career Progression: Transition into senior or leadership roles within the ML Infra team or other relevant departments as you develop your skills and demonstrate your value to the organization.

📝 Enhancement Note: Behavox's focus on innovation and growth creates ample opportunities for career progression and technical development.

🌐 Work Environment

Office Type: Behavox offers a hybrid work environment, with both on-site and remote work options available.

Office Location(s): Behavox has offices in multiple locations worldwide, with a strong presence in North America, Europe, and Asia.

Workspace Context:

Collaborative workspace with a focus on cross-functional integration between developers, designers, and stakeholders.
Access to development tools, multiple monitors, and testing devices to ensure optimal productivity and performance.
Opportunities for knowledge sharing, technical mentoring, and continuous learning through collaboration with other team members.

Work Schedule: Flexible work schedule with standard business hours, accommodating deployment windows and maintenance as needed.

📝 Enhancement Note: Behavox's hybrid work environment and global presence offer DevOps engineers the opportunity to work in a dynamic and collaborative setting, with exposure to diverse perspectives and cultures.

📄 Application & Technical Interview Process

Interview Process:

Initial Screening: A brief phone or video call to discuss your qualifications, experience, and salary expectations.
Technical Assessment: A hands-on technical assessment, focusing on your problem-solving skills, system design, and coding abilities.
Behavioral Interview: A conversation to assess your cultural fit, communication skills, and problem-solving approach.
Final Evaluation: A discussion with senior leadership to ensure a strong fit and alignment with the company's mission and values.

Portfolio Review Tips:

Highlight your experience with Linux system administration, cloud infrastructure, and Kubernetes.
Demonstrate your ability to manage and troubleshoot complex distributed systems and build scalable infrastructure.
Showcase any experience with ML services and data infrastructure, emphasizing your problem-solving skills and technical expertise.

Technical Challenge Preparation:

Brush up on your Linux system administration skills, focusing on command-line tools and scripting.
Familiarize yourself with cloud infrastructure (AWS, GCP) and Kubernetes, focusing on deployment strategies and automation.
Prepare for system design questions, focusing on scalability, performance, and fault tolerance.

ATS Keywords: Linux, AWS, GCP, Kubernetes, Python, Bash, CI/CD, Data Infrastructure, ML Services, System Administration, Cloud Architecture, DevOps, Distributed Systems, Machine Learning, Data Pipelines.

📝 Enhancement Note: Be prepared to discuss your technical skills and experience in detail, focusing on your ability to manage and troubleshoot complex distributed systems and build scalable infrastructure.

🛠 Technology Stack & Web Infrastructure

Frontend Technologies: Not applicable for this role.

Backend & Server Technologies:

Linux (Ubuntu, CentOS)
AWS (EC2, RDS, S3, Lambda, EKS)
GCP (Compute Engine, Cloud Storage, Kubernetes Engine)
Kubernetes (Kubectl, Helm)
Docker
Python (Flask, Django)
Bash

Development & DevOps Tools:

Git (version control)
Jenkins (CI/CD)
Ansible (infrastructure automation)
Terraform (infrastructure as code)
Prometheus (monitoring)
Grafana (visualization)

📝 Enhancement Note: Be prepared to discuss your experience with the relevant technologies and tools, focusing on your ability to manage and troubleshoot complex distributed systems and build scalable infrastructure.

👥 Team Culture & Values

Web Development Values:

User-Centric: Focus on the user experience and the impact of your work on the company's success.
Innovation: Embrace emerging technologies and continuously improve your skills and knowledge.
Collaboration: Work closely with other teams, such as data engineering and data science, to ensure efficient ML model development and deployment.
Quality: Maintain high coding standards and ensure the reliability and performance of the systems you manage.

Collaboration Style:

Cross-Functional Integration: Work closely with other teams, such as data engineering and data science, to ensure efficient ML model development and deployment.
Code Review Culture: Participate in regular code reviews to maintain high coding standards and ensure knowledge sharing.
Peer Programming: Collaborate with other team members to solve complex problems and learn from one another.

📝 Enhancement Note: Behavox's focus on collaboration and innovation creates a dynamic and challenging work environment for DevOps engineers.

⚡ Challenges & Growth Opportunities

Technical Challenges:

System Complexity: Manage and troubleshoot complex distributed large-scale software systems, requiring strong problem-solving skills and a deep understanding of Linux, cloud infrastructure, and Kubernetes.
Scalability: Build scalable, secure, and reliable container-based infrastructure, focusing on performance, availability, and fault tolerance.
Automation: Automate software delivery processes with CI/CD pipelines, ensuring efficiency, consistency, and reliability.
Emerging Technologies: Stay up-to-date with the latest developments in machine learning infrastructure, data pipelines, and emerging technologies.

Learning & Development Opportunities:

Technical Skill Development: Expand your expertise in machine learning infrastructure, data pipelines, and emerging technologies.
Conference Attendance: Attend industry conferences and events to learn from experts and network with other professionals.
Technical Mentoring: Seek guidance from experienced team members to improve your skills and gain insights into best practices.

📝 Enhancement Note: Be prepared to discuss your approach to problem-solving, focusing on your ability to manage and troubleshoot complex distributed systems and build scalable infrastructure.

💡 Interview Preparation

Technical Questions:

System Design: Prepare for system design questions focusing on scalability, performance, and fault tolerance.
Troubleshooting: Brush up on your troubleshooting skills, focusing on Linux, cloud infrastructure, and Kubernetes.
Coding Challenges: Practice coding challenges that focus on problem-solving, algorithm design, and data structures.

Company & Culture Questions:

Mission Alignment: Research Behavox's mission and values, and be prepared to discuss how your skills and experience align with the company's goals.
Team Dynamics: Familiarize yourself with Behavox's team structure and culture, and be prepared to discuss how you would contribute to the team's success.
Problem-Solving Approach: Prepare to discuss your approach to problem-solving, focusing on your ability to manage and troubleshoot complex distributed systems and build scalable infrastructure.

Portfolio Presentation Strategy:

Live Demonstration: Prepare a live demonstration of your portfolio projects, focusing on your ability to manage and troubleshoot complex distributed systems and build scalable infrastructure.
Code Walkthrough: Be prepared to walk through your code, explaining your problem-solving approach, design decisions, and technical implementation details.
User Experience Impact: Highlight any experience with ML services and data infrastructure, emphasizing your ability to manage and troubleshoot complex systems and build scalable infrastructure.

📝 Enhancement Note: Be prepared to discuss your technical skills and experience in detail, focusing on your ability to manage and troubleshoot complex distributed systems and build scalable infrastructure.

📌 Application Steps

To apply for this DevOps Engineer 3 (ML Infra) position:

Resume Optimization: Tailor your resume to highlight your experience with Linux system administration, cloud infrastructure, and Kubernetes. Include any experience with ML services and data infrastructure, emphasizing your problem-solving skills and technical expertise.
Portfolio Customization: Customize your portfolio to showcase your experience with Linux system administration, cloud infrastructure, and Kubernetes. Highlight any experience with ML services and data infrastructure, emphasizing your problem-solving skills and technical expertise.
Technical Interview Preparation: Brush up on your Linux system administration skills, focusing on command-line tools and scripting. Familiarize yourself with cloud infrastructure (AWS, GCP) and Kubernetes, focusing on deployment strategies and automation. Prepare for system design questions, focusing on scalability, performance, and fault tolerance.
Company Research: Research Behavox's mission, values, and team structure. Be prepared to discuss how your skills and experience align with the company's goals and how you would contribute to the team's success.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and web development/server administration industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

DevOps Engineer 3 (ML Infra)