Director of AI Engineering

• 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.
• 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.
• 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.
• 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.

None flagged.