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Machine Learning Operations Engineer

Connection
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Overview

What We Do:

CNXN Helix Center at Connection Inc., is at the forefront of AI innovation, offering cutting-edge solutions that redefine the boundaries of artificial intelligence and data management. We are dedicated to helping our clients navigate the complexities of AI integration, ensuring they stay ahead in a rapidly evolving technological landscape. Our commitment to excellence, innovation, and strategic growth makes us an industry leader. Visit us at www.connection.com/helix

Who We Are:

Our team is made stronger by a multitude of backgrounds, experiences, and perspectives. It’s what makes Connection unique—what drives us to innovate and create technology solutions that stand apart from the crowd. We’d love for you to be a part of that fabric, to share your ideas and experiences with a team that thrives on fresh thinking, creativity, and helping others.

Why You Should Join Us:

You’ll find supportive teammates and a rewarding career at Connection—plus great benefits. We take pride in supporting employees with a total rewards package that provides financial, emotional, and physical resources for you and your family. Our compensation, 401k plans, medical insurance, and other benefits are progressive and competitive. We value the importance of our employees’ emotional well-being. To support employees, we provide free therapy visits, mental health coaching and tools, and meditation resources. You’ll also enjoy a generous paid time off package that includes not only vacation and sick time, but also Wellness and Volunteer Time Off days.

Position Overview:

Reporting to the VP of Core Engineering at CNXN Helix, relying on experience and judgement to plan and accomplish goals, the Machine Learning Operations (ML Ops) Engineer is responsible for operationalizing CNXN Helix's AI and machine learning initiatives. The ML Ops Engineer collaborates with data scientists and software engineers to streamline the deployment and management of Machine Learning models. The ML Ops Engineer ensures that the company's AI solutions are scalable, reliable, and efficient, directly impacting the success of our clients. 

Responsibilities

  • Automates, develops and optimizes CI/CD pipelines for ML model deployment.
  • Manages Infrastructure. Manages and scales Machine Learning infrastructure using Kubernetes and Docker.
  • Monitors integration. Collaborates with enterprise authentication services and ensures compliance with security protocols.
  • Establishes system monitoring and create dashboards for service health.
  • Documents and maintains up-to-date documentation of systems and processes.
  • Collaborates with cross-functional teams to resolve technical issues and implement solutions.
  • Innovates by staying current with emerging Machine Learning tools and methodologies.

Requirements

  • Bachelor's degree required or equivalent combination of education and work experience.
  • Minimum 6 years' related experience.
  • Practical experience with Machine Learning model development and deployment. Direct experience in scripting and coding.
  • Frameworks: Proficient with TensorFlow, PyTorch, and other Machine Learning frameworks.
  • Containerization: Expert in Docker and Kubernetes.
  • Scripting: Strong scripting skills in Bash or Python.
  • CI/CD: Experience with GitLab CI/CD pipelines.
  • Cloud Platforms: Familiarity with deploying to on-premises (Dell or HPE Stack) and cloud infrastructure (AWS, Azure, GCP).
  • Tools: Experience with Jira, Confluence, and monitoring tools. KubeFlow, MLFlow.
  • Real world Generative and Predictive foundational experience preferred.
  • Knowledge in identity, security, access management, workflow and/or developer-facing products preferred.
  • Direct experience working with cross-organizational teams.
  • Experience in AI technology policy, compliance, trust and safety policy a plus.
  • OpenShift experience is a plus.
  • Problem-Solving: Excellent troubleshooting abilities.
  • Independence: Ability to work unsupervised and manage time effectively.
  • Communication: Strong verbal and written communication skills.
  • Teamwork: Collaborative mindset and ability to work in a team.