Role Summary:
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. Our Automotive Retail Cloud (ARC), Automotive Enterprise Cloud (AEC), and Automotive Partner Cloud (APC) connect OEMs, retailers, and consumers through one seamless platform. Utilizing cutting-edge technology, big data, machine learning, and AI, Tekion is transforming the automotive retail ecosystem. We're inventing new technology along the way to overcome barriers and solve big problems, all while having a blast doing it with offices in North America, Asia, and Europe, Tekion employs around 3,000 people worldwide.
Role Summary:
- We are looking for an Engineering Manager or Senior Engineering Manager who leads from the codebase, not just from meetings. You will take ownership of an existing AI team of engineers and
- grow it to 5–8 while remaining a core hands-on contributor — writing production code, owning critical
- architecture decisions, and setting the technical bar through your own work. Your two missions:
- embedding AI/ML capabilities across Tekion's products, and building applied GenAI and agentic
- systems that transform dealership workflows.
Key Responsibilities:
Architecture & Technical Leadership
- Design the end-to-end AI architecture for Tekion's platform: model serving, data pipelines, LLM
- orchestration layers, and integration patterns with existing microservices.
- Make build-vs-buy and model selection decisions (open-source vs. API-based, RAG vs. fine-
- tuning, agentic vs. deterministic) and own the tradeoffs.
- Write production code in critical-path areas — you are the technical anchor of the team, not a reviewer-only manager.
- Conduct deep code reviews and architecture reviews; raise the engineering bar by example,not just by mandate.
- Own technical debt prioritization and system reliability for AI components in production.
Applied GenAI & Agentic Systems
- Build LLM-powered agents, RAG pipelines, and conversational AI for dealership workflows
- (service advisor copilot, F&I assistant, customer communication agents).
- Define the agentic architecture: tool-calling patterns, orchestration frameworks, memory and
- context management, guardrails, and evaluation harnesses.
- Stay current with the rapidly evolving GenAI landscape and make pragmatic build-vs-integrate
- decisions (open-source models, fine-tuning, API-based, hybrid).
- Design robust evaluation and observability systems for LLM-powered features to ensure
- accuracy, safety, and reliability in production.
Team Building & Leadership
- Take ownership of the existing AI engineering team; quickly build trust, understand current work
- streams, and identify gaps in capability or capacity.
- Drive targeted hiring to grow the team to 5–8 engineers; define roles, source candidates, and
- close strong AI/ML talent in a competitive market.
- Lead by doing: your commits, design docs, and technical decisions set the standard for the
- team.
- Provide hands-on technical mentorship through pairing, mob programming, and architecture
- deep-dives — not just 1:1s.
- Set clear goals, run effective sprint ceremonies, and maintain delivery velocity while protecting
- space for research and exploration.
- Foster a culture of craftsmanship, experimentation, and demo-driven development.
Requirements
Must Have
- 10–14 years of software engineering experience, with at least 1+ year in AI/ML-focused roles
(applied ML, NLP, or GenAI).
- Currently writing production code — this is not a role for someone who stopped coding when they became a manager.
- 3+ years of engineering management experience, with a track record of inheriting and growing engineering teams.
- Deep hands-on experience with LLMs in production: RAG architectures, prompt engineering at scale, agentic tool-calling, evaluation and observability.
- Strong software engineering fundamentals: system design, API design, distributed systems,
CI/CD, and production operations.
- Track record of personally architecting and shipping AI-powered features in a product company (not just overseeing others who did).
- Excellent hiring instincts: ability to source, evaluate, and close strong AI/ML talent in a competitive market.
- Strong cross-functional collaboration skills: experience partnering closely with Product, Design and Data teams.
Nice to Have
- Experience in automotive, ERP, or complex vertical SaaS domains with messy real-world data.
- Familiarity with cloud-native, microservices-based architectures at scale.
- Contributions to open-source AI/ML projects or published research in applied AI.
- Experience with ML platform/infra: model serving, feature stores, experiment tracking, A/B
testing frameworks.
- Understanding of responsible AI practices: bias detection, hallucination mitigation, data privacy, and auditability.
Perks and Benefits
- Competitive compensation
- Generous stock options
- Medical Insurance coverage
- Work with some of the brightest minds from Silicon Valley's most dominant and
- successful companies
