Engineering-Applied Science/Machine Learning/Data Science

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.

We are seeking a highly accomplished leader in Applied AI and Machine Learning to drive Tekion’s end-to-end AI strategy, research innovation, and production-scale ML platform execution. This role combines deep scientific expertise with strong systems and platform engineering capabilities to translate advanced ML and LLM research into reliable, high-performance, enterprise-grade products.

The ideal candidate will shape technical vision, lead cross-functional execution, productionize ML systems at scale, and establish best-in-class practices across the full machine learning lifecycle.

Key Responsibilities

Strategic Leadership & Innovation

• Architect and execute Tekion’s strategic vision for Applied AI and Machine Learning, ensuring strong alignment with business objectives and industry needs.
• Drive the R&D roadmap by identifying emerging technological opportunities and delivering scientifically grounded innovations.
• Serve as the primary technical liaison between the R&D organization and executive leadership.
• Contribute to the broader scientific community through publications and participation in leading academic conferences and journals.

Cross-Functional Delivery

• Partner closely with Product, Engineering, Data, and Business teams to design and integrate advanced ML capabilities into core products and services.
• Translate applied science prototypes (tabular ML, NLP/LLMs, recommendation systems, forecasting) into scalable production services.
• Review, refactor, and optimize data science models for production readiness.
• Mentor applied scientists and engineers, fostering a culture of technical excellence and innovation.

ML Platform & Production Engineering

• Build and operate robust CI/CD pipelines for machine learning systems.
• Develop high-performance inference microservices (REST/gRPC) with schema versioning, structured outputs, and strict p95 latency targets.
• Integrate with the LLM Gateway/MCP, including prompt and configuration versioning.
• Design and implement batch and streaming data pipelines using technologies such as Airflow/Kubeflow, Spark/Flink, and Kafka.
• Collaborate on enterprise system architecture with data engineers, platform teams, and architects.

LLM & Agentic Systems Excellence

• Implement advanced prompt management frameworks, including versioning, A/B testing, guardrails, and dynamic orchestration.
• Monitor, detect, and mitigate risks unique to LLMs and agent-based systems.
• Establish best practices for safe, reliable, and cost-efficient LLM deployment at scale.

Lifecycle Management, Observability & Reliability

• Own the end-to-end model and feature lifecycle, including feature store strategy, model/agent registry, versioning, and lineage.
• Build deep observability across traces, logs, metrics, drift detection, model performance, safety signals, and cost tracking.
• Ensure real-time service reliability through autoscaling, caching, circuit breakers, retries/fallbacks, and graceful degradation.
• Establish robust model evaluation frameworks and clearly quantify business impact for executive stakeholders.
• Define and govern best practices across the full ML lifecycle while championing ethical and responsible AI.

Developer Experience & Enablement

• Create reusable templates, SDKs, CLIs, sandbox datasets, and documentation that make ML delivery fast, reliable, and repeatable across teams.
• Drive platform standardization to make shipping ML the default path within the organization.

Core Competencies & Technical Expertise
The successful candidate will demonstrate mastery in the following areas:

Foundational Expertise: Deep, theoretical and practical expertise in Machine Learning, Deep Learning, Causal Inference, and Explainable AI.

Statistical Rigor: Advanced proficiency in applied probability and statistics to derive and validate insights from complex, high-dimensional data.