Network-Jobs in United States

Machine Learning Operations (MLOps) Engineer

Summary

We are looking for a versatile and hands-on individual who thrives on working with hardware, machine learning operations (MLOps), and supporting a dynamic data science team. The ideal candidate will have a broad technical skill set and the ability to tackle various challenges, including hardware setup, system optimization, and machine learning workflows.

What FGF Offers:

• FGF believes in Home Grown Talent, accelerated career growth with leadership training. Unleashing Your Potential
• Competitive Compensation, Health Benefits, & a generous flexible medical / Health spending account
• RRSP matching program
• Tuition reimbursement
• Discount program that covers almost everything under the sun - Restaurants, gyms, shopping etc.

Primary Responsibilities

MLOps

• Deploy machine learning models on hardware platforms with a focus on edge AI and IoT systems.
• Leverage containerization (e.g., Docker) for scalable, repeatable deployments.
• Automate workflows to streamline machine learning pipelines and maximize reproducibility.

Hardware Engineering & Optimization

• Build and configure development boards such as NVIDIA boards or similar platforms.
• Integrate cameras and peripherals for AI and computer vision applications.
• Diagnose and resolve hardware issues, ensuring peak system performance.

System & Network Configuration

• Establish seamless network connectivity for IoT devices and integrated systems.
• Maintain hardware inventory and detailed documentation of all configurations and workflows.

Collaboration with Data Science Teams

• Support data collection initiatives by designing and integrating sensor and camera systems.
• Partner with teams to create customized hardware solutions tailored to project needs.
• Maintain on-premises and edge AI setups to support real-time applications.

Required Experience

Education and Experience

• Education in computer science and electrical engineering with minimum 5 years of experience in related roles or similar technical field of study.

Technical Expertise

• Deep familiarity with platforms like NVIDIA Boards, Raspberry Pi, or comparable devices.
• Knowledge of Linux environment, machine learning workflows and MLOps best practices.
• Proficiency in setting up hardware systems, including advanced troubleshooting.
• Experience with containerization (Docker) and cloud services integration.

Programming Skills

• Proficiency in Python; familiarity with ML frameworks like PyTorch, Tensorflow is a plus.
• Experience with hardware acceleration tools such as NVIDIA TensorRT is advantageous.

Problem Solving & Collaboration

• A relentless drive to find elegant, scalable solutions to complex problems.
• Strong communication skills and a commitment to teamwork.

Disclaimer: The above describes the general responsibilities, required knowledge and skills. Please keep in mind that other duties may be added or this description may be amended at any time.