Note: The job is a remote job and is open to candidates in USA. Credit Acceptance is an award-winning company recognized for its exceptional workplace culture. As the Director of AI Engineering Platform, you will provide leadership for the AI engineering platform, aligning capabilities with enterprise needs and driving operational excellence. This role involves strategic leadership, execution, innovation, and team development to enhance AI adoption and productivity.
Responsibilities
- 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
- 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
- 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
- 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
- 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
Skills
- BS in Computer Science, Engineering, or related field with 12+ years in software engineering, with significant time in platform, infrastructure, or developer tooling roles
- 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
- 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
- MS in Computer Science, Engineering, or related field
Benefits
- 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
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