Lead, AI Engineering

Scout Motors • Charlotte, NC

1d • $180,000 - $220,000 • Hybrid

About The Position

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.

Requirements

• 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, TensorFlow, Scikit-learn, LangChain, 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 SageMaker, 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, Cassandra, DocumentDB, Influx DB, and AI-ready vector data systems.
• Experience architecting scalable AI infrastructure using Kubernetes, Docker, GPU-enabled environments, distributed compute clusters, and Infrastructure as Code tools such as Terraform.
• Proven ability to design, train, fine-tune, deploy, and monitor scalable machine learning and generative AI models in production environments.
• Experience implementing MLOps / LLMOps practices including CI/CD pipelines, model registries, prompt versioning, automated retraining, and observability frameworks.
• Excellent problem-solving and troubleshooting skills, with a proactive ownership mindset toward resolving complex technical challenges.
• Strong communication, collaboration, and leadership skills, with the ability to influence cross-functional teams and guide architectural decisions while fostering a culture of engineering excellence.

Responsibilities

• 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.

Benefits

• Medical, dental, vision and income protection plans
• 401(k) program with an employer match and immediate vesting
• 20 days planned PTO, as accrued
• 40 hours of unplanned PTO and 14 company or floating holidays, annually
• Up to 16 weeks of paid parental leave for biological and adoptive parents of all genders
• Paid leave for circumstances related to bereavement, jury duty, voting time, or military leave
• Pay Transparency
• Annual performance bonus program

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Associate degree