Director of AI Engineering

Current job opportunities are posted here as they become available.

Flexjet is seeking a Director of AI Engineering to lead the design, deployment, and operationalization of enterprise‑scale machine learning and generative AI systems. This role is responsible for building and managing the infrastructure, systems, and processes required to reliably deploy and maintain AI solutions in production. Combine strong engineering leadership with deep expertise in MLOps, cloud infrastructure, model lifecycle management, and Generative AI deployment. Lead a team of AI engineers and MLOps specialists to ensure scalable, secure, and compliant AI systems across the organization.

DUTIES & RESPONSIBILITIES

• Lead the strategy, architecture, and implementation of enterprise AI, Generative AI, and MLOps platforms while establishing standards for model development, deployment, monitoring, governance, and lifecycle management.
• Design and scale cloud‑native AI infrastructure, including distributed compute environments, containerized platforms, CI/CD pipelines, and cost‑optimized ML operations.
• Oversee the production deployment of machine learning and LLM‑powered applications, including RAG solutions, AI copilots, model evaluation frameworks, guardrails, and automated retraining processes.
• Ensure compliance with responsible AI, security, risk management, data privacy, auditability, reproducibility, documentation, and regulatory requirements.
• Build and manage reusable AI platform services and frameworks that support multiple data science and engineering teams.
• Lead, mentor, and grow teams of AI Engineers and MLOps Engineers, fostering engineering excellence, innovation, talent development, and performance accountability.
• Partner with Data Scientists, Software Engineering, Security, DevOps, and Product leadership teams to drive enterprise AI adoption and align technical strategy with business objectives.
• Communicate AI platform vision, roadmap, and operational performance to executive stakeholders.

EDUCATION & EXPERIENCE

• Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field, or an equivalent combination of education, training, and relevant professional experience.
• 10+ years of experience in software engineering, machine learning engineering, platform engineering, MLOps, or DevOps.
• 5+ years of leadership experience managing and mentoring technical teams in fast‑paced, technology‑driven environments.
• Experience implementing and deploying complex and integrated information systems.
• Proven experience in leading application development teams in an enterprise environment.
• Experience working with Agile methodology.
• Experience in managing large projects including setting deadlines, identifying interdependencies, communicating with stakeholders, gathering requirements, and setting expectations.

REQUIRED TECHNICAL SKILLS & QUALIFICATIONS

• Strong experience with MLOps and platform engineering, including model lifecycle management, CI/CD, model versioning, feature stores, experiment tracking, and automated retraining pipelines.
• Proficiency with cloud and infrastructure technologies, including AWS, Azure, or Google Cloud Platform (GCP), Kubernetes, Docker, Terraform, and distributed systems.
• Expertise in machine learning systems, including model deployment, monitoring and observability, data pipelines, and real‑time inference architectures.
• Experience with Generative AI and LLM technologies, including LLM deployment, Retrieval‑Augmented Generation (RAG), prompt orchestration, model governance and guardrails, and cost optimization strategies.
• Strong programming skills in Python, SQL, Bash, Git, and CI/CD tools.

PREFFERED QUALIFICATIONS

• Experience deploying Generative AI and LLM solutions in large‑scale enterprise environments.
• Experience designing and supporting multi‑tenant AI/ML platforms.
• Familiarity with RAG architectures, vector databases, and LLM evaluation frameworks.
• Experience managing GPU infrastructure and distributed training workloads.
• Knowledge of AI security, governance, risk management, and regulatory compliance frameworks.