Director of AI EngineeringArtificial Intelligence (AI)

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.