Director, AI Engineering Platform
Credit Acceptance is proud to be an award-winning company recognized both locally and nationally across multiple workplace categories. Our world-class culture is shaped by dedicated team members who are driven to succeed as professionals individually andtogether as a team. Backed by a strong product, exceptional people, and a stable financial foundation, we've grown into a leading provider of used and new car financing across the country.
Our Engineering and Analytics Team Members utilize the latest technology to develop, monitor, and maintain complex practices that help optimize our success. Our Team Members value being challenged, are encouraged to express their ideas, and have the flexibility to enjoy work life balance. We build intrinsic value by partnering with all functions of our business to support their success and make strategic business decisions. We focus on professional development and continuous improvement while enjoying a casual work environment and Great Place to Work culture!
As the Director, AI Engineering Platform, you will provide leadership for Credit Acceptance's AI engineering platform, including strategy, architecture, delivery, governance, and operations.This role is accountable for aligning AI engineering capabilities with enterprise engineering needs, security and risk requirements, cost discipline, and long term technology direction.The Director, AI Engineering Platform owns the vision and execution of foundational AI platform capabilities that enable software engineering teams to build with and alongside AI safely, productively, and at scale.
This role leads a high performing platform organization and partners closely with Platform & Tools, AI/ML, Security, Infrastructure, and engineering leaders to drive operational excellence, responsible AI adoption, developer productivity, and sustainable innovation.
Outcomes and Activities
Strategic Leadership
- Define and communicate the vision for Credit Acceptance's AI engineering platform: the foundation that enables software engineering teams to build with and alongside AI safely, productively, and at scale.
- Partner closely with the CTO, VP Platform & Tools, the AI/ML organization, peer directors, and engineering leaders to prioritize platform investments and align AI engineering tooling to enterprise outcomes.
- Own the multi-year roadmap, usage strategy, and cost discipline for how AI capabilities are used across all of Credit Acceptance, including engineering, product, sales, and other functions, regardless of underlying platform (e.g., AWS, Microsoft, Databricks). This includes AI gateway and model access, MCP infrastructure, agentic development tooling, AI-assisted coding, evaluation frameworks, and governance, partnering with the FinOps practice on spend visibility and policy-driven cost controls for AI workloads.
Execution and Delivery
- Enable software engineering teams across Credit Acceptance to build AI‑powered features and adopt AI‑assisted and agentic development practices through scalable, self‑service platform capabilities.
- Partner with the AI/ML organization and Platform Engineering to define platform boundaries, integrate shared infrastructure (e.g., AI gateway, model access, evaluation tooling), and deliver coherent, non‑duplicative solutions integrated into Expressway and developer golden paths.
- Drive production‑grade system design and delivery practices, evolving CI/CD pipelines to support AI‑generated code, agentic workflows, AI‑aware validation, policy‑as‑code, and progressive delivery patterns.
Innovation and Thought Leadership
- Champion adoption of modern AI engineering practices across the software engineering organization, including agentic systems, model context protocol (MCP), AI-assisted development, and emerging patterns in enterprise AI engineering.
- Drive responsible enterprise AI practices for the engineering domain, including access controls, evaluation, cost transparency, and safe usage patterns appropriate to Credit Acceptance's regulatory and risk environment.
- Build a culture of experimentation, measurement, and continuous improvement in how AI creates leverage for software engineering and for the products engineering ships.
Team Leadership and Talent Development
- Build, scale, and mentor a high-performing organization of engineering managers, senior engineers, and platform specialists focused on AI engineering platform capabilities.
- Establish clear career paths, performance expectations, and coaching programs while fostering a collaborative, inclusive "One Team" culture across Platform & Tools, AI/ML, the broader Technology Organization, and platform consumers.
Governance, Risk and Standards
- Establish and enforce engineering and operational standards for AI gateway access, model selection, security, data privacy, cost controls, acceptable use, and agentic workflows across AI‑powered engineering systems.
- Ensure compliance with enterprise security, risk, and regulatory requirements for AI tooling and AI‑powered software systems, aligned with standards set by the AI/ML organization.
- Implement governance processes for model and agent lifecycle management, including evaluation, observability, auditability, permissioning, sandboxing, and runtime monitoring.
Competencies: The following items detail how you will be successful in this role.
- Customer Empathy: Customer Empathy is the ability to understand the perspectives, pain points, and experiences of customers. It involves actively putting oneself in the customer's shoes, comprehending their needs and challenges, and using that understanding to provide a better, more customer-centric experience.
- Engineering Excellence: Engineering Excellence is about bringing great craftsmanship and thought leadership to deliver an outstanding product that delights customers and solves for the business. This involves pursuing and achieving high standards, best practices, innovation, and superior solutions.
- One Team: A One Team mindset refers to a collaborative approach across the organization, where individuals work together seamlessly, without boundaries, as a single, cohesive team. Shared goals, open communication and mutual support create a sense of collective purpose. This enables teams to navigate challenges and pursue shared objectives more effectively.
- Owner's Mindset: Owner's Mindset involves adopting a set of behaviors that reflect a sense of responsibility, accountability, strategic thinking, and a proactive approach to managing your domain. As an owner, you understand the business and your domain(s) deeply and solve for the right outcome for the domain(s) and the business.
Requirements
- BS in Computer Science, Engineering, or related field with 12+ years in software engineering, with significant time in platform, infrastructure, or developer tooling roles. MS preferred.
- Minimum 5+ years in senior leadership roles managing managers and large technical organizations.
- Proven delivery of production-grade platform capabilities at enterprise scale, ideally including AI engineering platforms, developer platforms, or distributed infrastructure platforms.
- Experience working in or partnering with AI/ML organizations, with clear understanding of where AI engineering platform ends and AI/ML platform begins.
- Ability to influence executives and guide strategic decisions.
- Experience with budgeting, resource planning, and scaling organizations.
- Demonstrated ability to communicate complex technical concepts clearly and compellingly.
Knowledge and Skills
- Deep knowledge of the modern AI engineering stack, including LLM APIs, AI gateways (e.g., LiteLLM, Portkey, Traefik AI Gateway), MCP, agentic frameworks, evaluation tooling, and AI observability.
- Strong working knowledge of cloud-native platform engineering practices (AWS preferred), including CI/CD, observability, security, and FinOps for AI workloads.
- Demonstrated ability to drive enterprise adoption of platform capabilities, with measurable impact on engineering productivity, quality, or velocity.
- Experience designing and operating production‑grade, distributed systems with high reliability, security, and scalability requirements.
- Working knowledge of governance, risk, and compliance considerations for enterprise AI systems.
- Excellent communication skills with the ability to translate complex technical concepts for executive and non‑technical audiences.
- Strong analytical and systems‑thinking skills to balance innovation, risk, cost, and developer productivity.
Target Compensation: A competitive base salary range from $217,623 – $319,180. This position is eligible for an annual variable bonus of cash and equity, between 20-60%. Bonus amounts are based on individual performance. Final compensation within the range is influenced by many factors including role-specific skills, depth and experience level, industry background, relevant education and certifications.
Candidates who reside in the following major metropolitan areas may be eligible for a premium on top of the posted range based on their specific zone: San Francisco, Seattle, Boston, New York City, Los Angeles and San Diego.
Excellent benefits package that includes 401(K) match, adoption assistance, parental leave, tuition reimbursement, comprehensive medical/ dental/vision and many nonstandard benefits that make us a Great Place to Work
To be successful in this role, Team Members need to be:
- Positive by maintaining resiliency and focusing on
Director, AI Engineering Platform
Credit Acceptance is proud to be an award-winning company recognized both locally and nationally across multiple workplace categories. Our world-class culture is shaped by dedicated team members who are driven to succeed as professionals individually andtogether as a team. Backed by a strong product, exceptional people, and a stable financial foundation, we've grown into a leading provider of used and new car financing across the country.
Our Engineering and Analytics Team Members utilize the latest technology to develop, monitor, and maintain complex practices that help optimize our success. Our Team Members value being challenged, are encouraged to express their ideas, and have the flexibility to enjoy work life balance. We build intrinsic value by partnering with all functions of our business to support their success and make strategic business decisions. We focus on professional development and continuous improvement while enjoying a casual work environment and Great Place to Work culture!
As the Director, AI Engineering Platform, you will provide leadership for Credit Acceptance's AI engineering platform, including strategy, architecture, delivery, governance, and operations.This role is accountable for aligning AI engineering capabilities with enterprise engineering needs, security and risk requirements, cost discipline, and long term technology direction.The Director, AI Engineering Platform owns the vision and execution of foundational AI platform capabilities that enable software engineering teams to build with and alongside AI safely, productively, and at scale.
This role leads a high performing platform organization and partners closely with Platform & Tools, AI/ML, Security, Infrastructure, and engineering leaders to drive operational excellence, responsible AI adoption, developer productivity, and sustainable innovation.
Outcomes and Activities
Strategic Leadership
- Define and communicate the vision for Credit Acceptance's AI engineering platform: the foundation that enables software engineering teams to build with and alongside AI safely, productively, and at scale.
- Partner closely with the CTO, VP Platform & Tools, the AI/ML organization, peer directors, and engineering leaders to prioritize platform investments and align AI engineering tooling to enterprise outcomes.
- Own the multi-year roadmap, usage strategy, and cost discipline for how AI capabilities are used across all of Credit Acceptance, including engineering, product, sales, and other functions, regardless of underlying platform (e.g., AWS, Microsoft, Databricks). This includes AI gateway and model access, MCP infrastructure, agentic development tooling, AI-assisted coding, evaluation frameworks, and governance, partnering with the FinOps practice on spend visibility and policy-driven cost controls for AI workloads.
Execution and Delivery
- Enable software engineering teams across Credit Acceptance to build AI‑powered features and adopt AI‑assisted and agentic development practices through scalable, self‑service platform capabilities.
- Partner with the AI/ML organization and Platform Engineering to define platform boundaries, integrate shared infrastructure (e.g., AI gateway, model access, evaluation tooling), and deliver coherent, non‑duplicative solutions integrated into Expressway and developer golden paths.
- Drive production‑grade system design and delivery practices, evolving CI/CD pipelines to support AI‑generated code, agentic workflows, AI‑aware validation, policy‑as‑code, and progressive delivery patterns.
Innovation and Thought Leadership
- Champion adoption of modern AI engineering practices across the software engineering organization, including agentic systems, model context protocol (MCP), AI-assisted development, and emerging patterns in enterprise AI engineering.
- Drive responsible enterprise AI practices for the engineering domain, including access controls, evaluation, cost transparency, and safe usage patterns appropriate to Credit Acceptance's regulatory and risk environment.
- Build a culture of experimentation, measurement, and continuous improvement in how AI creates leverage for software engineering and for the products engineering ships.
Team Leadership and Talent Development
- Build, scale, and mentor a high-performing organization of engineering managers, senior engineers, and platform specialists focused on AI engineering platform capabilities.
- Establish clear career paths, performance expectations, and coaching programs while fostering a collaborative, inclusive "One Team" culture across Platform & Tools, AI/ML, the broader Technology Organization, and platform consumers.
Governance, Risk and Standards
- Establish and enforce engineering and operational standards for AI gateway access, model selection, security, data privacy, cost controls, acceptable use, and agentic workflows across AI‑powered engineering systems.
- Ensure compliance with enterprise security, risk, and regulatory requirements for AI tooling and AI‑powered software systems, aligned with standards set by the AI/ML organization.
- Implement governance processes for model and agent lifecycle management, including evaluation, observability, auditability, permissioning, sandboxing, and runtime monitoring.
Competencies: The following items detail how you will be successful in this role.
- Customer Empathy: Customer Empathy is the ability to understand the perspectives, pain points, and experiences of customers. It involves actively putting oneself in the customer's shoes, comprehending their needs and challenges, and using that understanding to provide a better, more customer-centric experience.
- Engineering Excellence: Engineering Excellence is about bringing great craftsmanship and thought leadership to deliver an outstanding product that delights customers and solves for the business. This involves pursuing and achieving high standards, best practices, innovation, and superior solutions.
- One Team: A One Team mindset refers to a collaborative approach across the organization, where individuals work together seamlessly, without boundaries, as a single, cohesive team. Shared goals, open communication and mutual support create a sense of collective purpose. This enables teams to navigate challenges and pursue shared objectives more effectively.
- Owner's Mindset: Owner's Mindset involves adopting a set of behaviors that reflect a sense of responsibility, accountability, strategic thinking, and a proactive approach to managing your domain. As an owner, you understand the business and your domain(s) deeply and solve for the right outcome for the domain(s) and the business.
Requirements
- BS in Computer Science, Engineering, or related field with 12+ years in software engineering, with significant time in platform, infrastructure, or developer tooling roles. MS preferred.
- Minimum 5+ years in senior leadership roles managing managers and large technical organizations.
- Proven delivery of production-grade platform capabilities at enterprise scale, ideally including AI engineering platforms, developer platforms, or distributed infrastructure platforms.
- Experience working in or partnering with AI/ML organizations, with clear understanding of where AI engineering platform ends and AI/ML platform begins.
- Ability to influence executives and guide strategic decisions.
- Experience with budgeting, resource planning, and scaling organizations.
- Demonstrated ability to communicate complex technical concepts clearly and compellingly.
Knowledge and Skills
- Deep knowledge of the modern AI engineering stack, including LLM APIs, AI gateways (e.g., LiteLLM, Portkey, Traefik AI Gateway), MCP, agentic frameworks, evaluation tooling, and AI observability.
- Strong working knowledge of cloud-native platform engineering practices (AWS preferred), including CI/CD, observability, security, and FinOps for AI workloads.
- Demonstrated ability to drive enterprise adoption of platform capabilities, with measurable impact on engineering productivity, quality, or velocity.
- Experience designing and operating production‑grade, distributed systems with high reliability, security, and scalability requirements.
- Working knowledge of governance, risk, and compliance considerations for enterprise AI systems.
- Excellent communication skills with the ability to translate complex technical concepts for executive and non‑technical audiences.
- Strong analytical and systems‑thinking skills to balance innovation, risk, cost, and developer productivity.
Target Compensation: A competitive base salary range from $217,623 – $319,180. This position is eligible for an annual variable bonus of cash and equity, between 20-60%. Bonus amounts are based on individual performance. Final compensation within the range is influenced by many factors including role-specific skills, depth and experience level, industry background, relevant education and certifications.
Candidates who reside in the following major metropolitan areas may be eligible for a premium on top of the posted range based on their specific zone: San Francisco, Seattle, Boston, New York City, Los Angeles and San Diego.
Excellent benefits package that includes 401(K) match, adoption assistance, parental leave, tuition reimbursement, comprehensive medical/ dental/vision and many nonstandard benefits that make us a Great Place to Work
To be successful in this role, Team Members need to be:
- Positive by maintaining resiliency and focusing on
