The candidate must reside within 30 miles of one of the following locations: Boston, MA; San Francisco Bay Area, CA; Portland, ME; Chicago, IL.
About the Team/Role
We are the AI Platform Engineering team at WEX, committed to building scalable components that empower our product development teams to rapidly deploy ML and LLM functionality. Leveraging cutting-edge technologies such as Kubernetes, Docker, Terraform, and serverless architectures, we ensure our solutions are cloud-first and highly automated across infrastructure, testing, and deployment. Join us if you are passionate about Software Engineering, MLOps, and DevOps, and are eager to work with state-of-the-art tools and platforms to make a tangible impact.
How you'll make an impact
- Design, implement, and maintain robust and scalable full-stack components.
- Integrate AI components and models into WEX systems in collaboration with machine learning engineers.
- Design and implement REST APIs for seamless component communication and application operation.
- Manage version control using GitHub and implement CI/CD pipelines.
- Balance the need for rapid development with regulatory compliance in industries like payments and healthcare.
- Collaborate effectively with cross-functional teams and participate in code reviews.
- Communicate solutions to both technical and non-technical stakeholders.
- Work on small, high-performing teams.
- Advocate for your positions while fully supporting team decisions.
Experience you'll bring
- 5 years of software development experience.
- Bachelor's Degree in Computer Science, Engineering, or related field.
- Proven experience as a Full Stack Software Engineer.
- Experience with AWS, Azure, or GCP cloud services.
- Experience applying DevOps principles to software development processes.
- Proficiency in version control systems, particularly GitHub.
- Demonstrated experience using CI/CD pipelines for automated testing and deployment.
- Excellent problem-solving skills and a proactive approach to addressing challenges.
- A plus if you have experience running production AI/ML workloads at scale.