Sr. Staff AI/ML Engineer

WEX•Boston, MA

23h•Remote

About The Position

At WEX, we’re transforming how businesses operate by embedding advanced AI into our global payments and mobility platforms. The AI Engineering team enables product development groups across WEX to build, deploy, and scale AI-powered experiences quickly and safely. We’re cloud-first, automation-driven, and passionate about building systems that make AI a core part of our products—reliably, securely, and at scale. If you’re excited by Large Language Models, Agentic AI systems, MLOps, and cutting-edge production pipelines—and you thrive in fast-moving teams where innovation meets real-world impact—this is the role for you.

Requirements

12+ years of professional software or ML engineering experience, with a track record of deploying production-grade AI systems.
Proficiency in Python and key machine learning frameworks (PyTorch, TensorFlow, or similar).
Strong working knowledge of core libraries (NumPy, Pandas, scikit-learn) and LLM development frameworks (LangChain, ADK, or similar).
Experience with cloud platforms (AWS preferred; Azure or GCP also valuable) and Infrastructure-as-Code tools like Terraform.
Deep familiarity with CI/CD pipelines and DevOps practices using GitHub Actions or similar platforms.
Demonstrated ability to operate in agile, collaborative, high-trust teams.
Bachelor’s degree in Computer Science, Engineering, or a related discipline (Master’s preferred).

Nice To Haves

Experience in financial systems, data compliance, or building multi-tenant Agentic AI applications.

Responsibilities

Lead the design, implementation, and production deployment of machine learning and AI-driven systems—including LLM-based and agentic applications.
Partner with AI platform and product engineering teams to integrate advanced AI capabilities into WEX’s enterprise systems.
Design and maintain ML pipelines, from data ingestion to model deployment, ensuring scalability, observability, and reusability across teams.
Build and expose AI functionality via RESTful APIs and micro-services architectures.
Champion engineering best practices: CI/CD, infrastructure-as-code, testing automation, and continuous improvement.
Contribute to architectural decisions with a focus on security, compliance, and performance—especially in regulated industries such as payments and healthcare.
Collaborate cross-functionally with data scientists, ML engineers, and business stakeholders to align technical solutions with strategic goals.