Lead, AI Engineering

Lead, AI Engineering

Join us at Scout Motors and be part of shaping the future of transportation. If you're ready to drive change and make history, apply now!

The AI Team is responsible for building the organization's intelligent technology capabilities by designing, developing, and scaling AI-powered products and platforms across the enterprise. The team combines expertise in machine learning, generative AI, data engineering, and platform infrastructure to deliver innovative solutions that improve business outcomes and accelerate digital transformation.

The team focuses on:

• Developing machine learning and generative AI solutions that solve high-impact business problems.
• Building scalable AI infrastructure, including model training, deployment, and inference platforms.
• Creating reusable AI platforms, APIs, and shared services for enterprise-wide adoption.
• Partnering with product, engineering, data, and business teams to identify and prioritize AI use cases.
• Evaluating emerging AI technologies and rapidly prototyping new capabilities.
• Establishing best practices in MLOps, LLMOps, governance, security, and responsible AI.

The AI Team operates at the intersection of innovation and engineering excellence, transforming advanced AI technologies into production-grade enterprise solutions.

What you'll do:

• Lead the design, implementation, and evolution of scalable AI platforms
• Collaborate cross-functionally with product managers, architects, developers, data engineers, and business leaders to deliver robust, production-grade AI solutions.
• Design end-to-end AI data architectures, including feature stores, vector databases, knowledge repositories, and model-ready data pipelines.
• Lead the design, training, fine-tuning, deployment, and lifecycle management of machine learning and generative AI models.
• Design and oversee ETL/ELT pipelines in Python to support model training, inference, retrieval-augmented generation (RAG), and intelligent automation workflows.
• Lead development of AI-powered dashboards and observability platforms to monitor model performance, trends, forecasts, drift, and operational health.
• Design and implement resilient AI infrastructure components, ensuring high availability, scalability, reliability, and performance for training and inference workloads.
• Establish governance standards to ensure AI systems adhere to enterprise data quality, security, privacy, and compliance policies.
• Build and govern MLOps / LLMOps frameworks including model registries, CI/CD pipelines, prompt versioning, automated retraining, and inference monitoring.
• Implement proactive monitoring and alerting solutions for AI systems, including latency, hallucination risk, drift detection, and infrastructure health.
• Partner with cybersecurity and compliance teams to ensure AI platforms meet enterprise security, regulatory, and responsible AI standards.
• Evaluate emerging AI tools, frameworks, and infrastructure platforms, and prototype new capabilities to accelerate enterprise AI innovation.
• Mentor and lead AI engineers and platform teams, establishing engineering best practices for scalable AI solution delivery.

Location & Travel Expectations:

• This role may be based out of the Scout Motors corporate headquarters in Charlotte, NC.
• This role requires 4 days per week in the office, with regular in-person meetings and events.
• Applicants should expect that the role will require the ability to convene with Scout colleagues in person and travel to participate in events on behalf of the company from time to time.

What you'll bring:

• Bachelor's or master's degree in computer science, Artificial Intelligence, Information Technology, Engineering, or a related field, or equivalent practical experience.
• 8+ years of hands-on experience in AI/ML engineering, machine learning platforms, data engineering, or AI infrastructure, with experience in enterprise-scale environments such as manufacturing, automotive, or similarly complex industries.
• 3+ years of experience leading or mentoring AI engineering, ML engineering, or platform engineering teams.
• Strong experience designing and deploying end-to-end AI solutions, including machine learning, generative AI, and LLM-based applications.
• Proficiency in Python, SQL, and modern AI/ML frameworks such as PyTorch, Tensor Flow, Scikit-learn, Lang Chain, or equivalent platforms.
• Experience building scalable AI data pipelines supporting model training, inference, RAG architecture, and intelligent automation workflows.
• Hands-on expertise with cloud AI and data services such as AWS Sage Maker, Glue, Kinesis, Firehose, Azure ML, Vertex AI, or comparable platforms.
• Strong knowledge of structured, unstructured, streaming, and time-series data architecture, including experience with vector databases and embedding stores.
• Experience with enterprise data platforms and lakehouse architecture such as Databricks, Delta Lake, or equivalent modern data ecosystems.
• Solid understanding of cloud-native storage and database platforms including RDS, DynamoDB, MongoDB,…