AI Technical Lead

Overview

Our AI Technical Lead is responsible for designing and delivering scalable AI systems that enable intelligent applications across the organization. This role combines hands‑on engineering, system architecture, and technical leadership to build production‑grade machine learning and generative AI platforms. The Lead works closely with product, data engineering, and infrastructure teams to bring AI capabilities from experimentation into reliable production systems while supporting the organization’s broader AI strategy and innovation initiatives.

Location:

Preferred hybrid work location (onsite and WFH). There may be an opportunity for fully remote within a mutually acceptable location.

• Technical Leadership – Provide technical leadership and mentorship to a team of AI engineers. Establish engineering standards, coding practices, and architectural guidelines for AI system development. Lead design reviews, guide technical decision making, and resolve complex engineering challenges. Serve as a technical escalation point for AI system architecture and implementation.
• AI System Architecture – Architect end‑to‑end AI systems including data pipelines, model training workflows, AI service layers, and scalable AI application infrastructure. Design and implement AI‑powered applications including large language model (LLM) systems and retrieval‑based knowledge applications. Define architecture patterns that support experimentation, rapid prototyping, and production deployment of AI capabilities. Develop service‑based architectures that enable AI functionality to be integrated across enterprise applications.
• AI Engineering & Development – Develop and deploy machine learning and generative AI solutions that support enterprise use cases. Build reusable AI services and platform components that enable teams to rapidly develop and scale AI capabilities. Implement evaluation, monitoring, and reliability systems to ensure consistent model performance. Optimize AI pipelines for performance, scalability, and operational efficiency.
• Cloud & MLOps – Design cloud‑native infrastructure supporting AI and machine learning workloads. Implement containerized AI services and automated deployment pipelines. Support the development of scalable AI platforms that enable experimentation, model deployment, and operational monitoring. Ensure AI systems follow best practices for reliability, observability, and cost management.
• Collaboration & Delivery – Work closely with product managers, data engineers, and business stakeholders to identify and deliver high‑value AI use cases. Translate business requirements into scalable AI architecture and engineering solutions. Partner with cross‑functional teams to move AI solutions from pilots and experimentation into production environments. Support initiatives that enable the organization to scale AI capabilities across multiple business domains.
• Responsible AI & Governance – Promote responsible AI practices including transparency, fairness, and privacy considerations. Implement safeguards and monitoring systems for AI applications operating in production. Collaborate with security and compliance teams to ensure AI systems meet regulatory and organizational standards.

• Bachelor or International Equivalency degree in Cybersecurity, Computer Science, Electrical Engineering, Information Systems, or closely related field of study; or equivalent work experience (Two years’ relevant work experience is equivalent to one-year college).
• Associate Degree in Computer Science, Electrical Engineering, Information Systems, or closely related field of study + 2 years additional experience.

• 6+ years of experience in software engineering, machine learning engineering, and/or related AI/ML technical roles.
• Experience designing and deploying machine learning or generative AI systems.
• Strong programming experience in Python and modern backend technologies.
• Experience building distributed systems or cloud‑native architectures.
• Experience implementing machine learning workflows or model deployment pipelines.
• Preference for additional experience in developing…