Senior Machine Learning Engineer

About Tekion:

Positively disrupting an industry that has not seen any innovation in over 50 years, Tekion has challenged the paradigm with the first and fastest cloud-native automotive platform that includes the revolutionary Automotive Retail Cloud (ARC) for retailers, Automotive Enterprise Cloud (AEC) for manufacturers and other large automotive enterprises and Automotive Partner Cloud (APC) for technology and industry partners. Tekion connects the entire spectrum of the automotive retail ecosystem through one seamless platform. The transformative platform uses cutting-edge technology, big data, machine learning, and AI to seamlessly bring together OEMs, retailers/dealers and consumers. With its highly configurable integration and greater customer engagement capabilities, Tekion is enabling the best automotive retail experiences ever. Tekion employs close to 3,000 people across North America, Asia and Europe.

Build and operate the production backbone that takes models from Applied Sciences (AS) and delivers reliable, low-latency ML services across Tekion’s DMS, CRM, Digital Retail, Service, Payments, and enterprise products. You’ll own pipelines, microservices, CI/CD, observability, and runtime reliability—working hand-in-hand with Applied Sciences and Product to turn ideas into measurable dealer and consumer impact.

Why this Role Matters

Accelerate the rollout of LLM-powered and agent-driven features across Tekion products.

Enable agentic workflows that automate, reason, and interact on behalf of users and internal stakeholders.

Operationalize secure, compliant, and explainable LLM and agentic services at scale.

Convert Applied Sciences models into scalable, compliant, cost‑efficient production services.

Standardize how models are trained, validated, deployed, and monitored across Tekion products.

Power real-time, context-aware experiences by integrating batch/stream features, graph context, and online inference.

What You’ll Do

Turn Applied Sciences prototype models (tabular, NLP/LLM, recommendation, forecasting) into fast, reliable services with well-defined API contracts.

Integrate with the LLM Gateway/MCP, prompt/config versioning.

Build and orchestrate CI/CD pipelines.

Review data science models; refactor and optimize code; containerize; deploy; version; and monitor for quality.

Collaborate with data scientists, data engineers, product managers, and architects to design enterprise systems.