Senior Software Engineer I (Machine Learning Operations) | Remote

About Us

Do you want to help transform the global economy? Join the movement disrupting the financial world and changing how businesses gain access to the working capital they need to grow. As the largest online platform for working capital, we are named one of Forbes’ “Fintech 50” and we serve over one million businesses in 180 countries, representing more than $10.5 trillion in annual sales. Headquartered in Kansas City, C2FO has more than 700 employees worldwide, with operations throughout Europe, India, Asia Pacific, and Australia. For more information, visit www.c2fo.com.

Here at C2FO, we value the quality of our technical solutions and are passionate about building the right thing, the right way to best solve the problem at hand. But beyond that, we also value our employees' work-life balance and promote a continuous learning culture. We host bi-annual hackathons, have multiple book clubs focused on constant growth, and embrace a hyrbrid working environment. If you want to work at a place where your voice will be heard and can make a real impact, C2FO is the place for you.

Role Summary

We are seeking an experienced and talented Senior Engineer to join our MLOps team. In this role, you will play a crucial part in designing, developing, and maintaining scalable and reliable machine learning operations (MLOps) pipelines and infrastructure. You will collaborate closely with data scientists, software engineers, and other stakeholders to ensure the successful deployment and monitoring of machine learning models in production environments.

Responsibilities

• Design and implement robust MLOps pipelines for model training, evaluation, deployment, and monitoring using industry-standard tools and frameworks
• Collaborate with data scientists to streamline the model development process and ensure seamless integration with MLOps pipelines.
• Optimize and scale machine learning infrastructure to support high-performance model training and inference.
• Contribute to the development of MLOps standards, processes, and documentation within the organization.
• Mentor and support junior team members in MLOps practices and technologies.
• Stay up-to-date with the latest trends and best practices in MLOps, and explore opportunities for continuous improvement.
  
  

Qualifications

• Bachelor's or Master's degree in Computer Science, Statistics, or a related field.
• 5+ years of experience in software engineering, with 2+ years experience in ML
• Proficient in Python and at least one other programming language (e.g., Java, Go, C++).
• Extensive experience with containerization technologies (Docker, Kubernetes) and cloud platforms (AWS, GCP, Azure).
• Familiarity with machine learning frameworks and MLOps tools
• Experience with big data technologies
• Strong understanding of CI/CD principles and practices.
  
  

Preferred Qualifications

• Familiarity with model serving frameworks
• Knowledge of infrastructure as code (IaC) tools
• Experience with monitoring and observability tools
• Contributions to open-source MLOps projects or communities.
  
  

Benefits

At C2FO, we care for our customers and people – the vital human capital that helps our customers thrive. That's why we offer a comprehensive benefits package, flexible work options for work/life balance, volunteer time off, and more. Learn more about our benefits here.

Commitment To Diversity And Inclusion

As an Equal Opportunity Employer, we value diversity and equality and empower our team members to bring their authentic selves to work daily. We recognize the power of inclusion, emphasizing that each team member was chosen for their unique ability to contribute to the overall success of our mission. Our goal is to create a workplace that reflects the communities we serve and our global, multicultural clients.

We do not discriminate based on race, religion, color, sex, gender identity, sexual orientation, age, non-disqualifying physical or mental disability, national origin, veteran status, or any other basis covered by appropriate law. All employment decisions are based on qualifications, merit, and business needs.