Director, AI Engineering – Enterprise AI

Role Summary

We are seeking an experienced Director of AI Engineering to build and lead a world-class AI engineering team. This is a critical leadership role responsible for designing and delivering enterprise-grade AI applications that drive business value across Marketing, Finance, Legal, Enterprise Search, and R&D workflows. You will combine deep technical expertise with strong people leadership skills to grow the team, mentor engineers, establish best practices, and scale our AI capabilities across a globally distributed organization.

Key Responsibilities

• Lead and grow an AI engineering team, recruiting top talent and building a high-performing culture focused on technical excellence
• Design and oversee the delivery of enterprise AI applications across multiple business functions (Marketing, Finance, Legal, Search, R&D)
• Establish technical standards, architecture patterns, and code review practices that ensure software engineering excellence and maintainability
• Mentor engineers in modern AI/ML systems design, cloud infrastructure, production deployment, and operational excellence
• Collaborate with product, data science, and business stakeholders to translate requirements into robust technical solutions
• Drive technology decisions around AI frameworks, cloud platforms, and infrastructure to optimize for cost, performance, and reliability
• Build and maintain systems for data pipelines, model serving, monitoring, and continuous improvement of AI applications
• Lead technical strategy for scaling AI capabilities, including considerations for enterprise security, compliance, and governance

Required Qualifications

• 15+ years of professional software engineering experience with demonstrated progression to senior/leadership roles
• 3+ years of experience with AI/ML systems (including LLMs, generative AI, RAG architectures, or similar modern AI applications) in production environments
• 3+ years of technical leadership experience managing and mentoring engineering teams
• Deep hands-on experience with cloud platforms (AWS, Azure, or GCP), containerization (Docker/Kubernetes), and modern deployment practices
• Strong expertise in Python and/or other languages commonly used for AI/ML applications
• Proven ability to work effectively in globally distributed teams with experience managing across time zones
• Strong understanding of software engineering best practices: version control, testing, code review, CI/CD, observability
• Track record of shipping production systems at scale with focus on reliability, performance, and maintainability
• Excellent communication skills; ability to articulate technical concepts to both technical and non-technical stakeholders

Preferred Qualifications

• Experience in manufacturing, industrial, or enterprise software environments
• Familiarity with enterprise systems (ERP, CRM, workflow automation) and integration patterns
• Background in developing retrieval-augmented generation (RAG) systems or knowledge management applications
• Experience with MLOps, model monitoring, and LLM evaluation frameworks
• Expertise in establishing and scaling engineering culture, documentation, and knowledge sharing practices
• Experience with enterprise security, compliance, and data governance requirements
• Track record of building systems that integrate with modern API ecosystems and third-party AI services