Lead AI Engineer; ML Ops

About Gartner IT

Join a world‑class team of forward‑thinking engineers dedicated to delivering innovative digital solutions that empower our analysts and clients. At Gartner IT, we drive organizational transformation through advanced technology, fostering a culture of continuous innovation, outcome‑oriented execution, and the belief that impactful ideas can originate from any team member.

About the Role:
Lead AI Engineer

We are seeking a Lead AI Engineer to spearhead the end-to-end productionalization of AI initiatives across Gartner. This pivotal role blends deep expertise in AI engineering with hands‑on experience in MLOps, LLMOps, and Dev Ops, enabling the design, deployment, and scaling of enterprise‑grade AI solutions that underpin our Consulting & Insight Technology strategy.

• Lead the full lifecycle of AI/ML model productionalization, establishing resilient MLOps and LLMOps pipelines for seamless model deployment, orchestration, and monitoring at scale.
• Architect and implement scalable AI infrastructure and deployment strategies, ensuring robust integration with enterprise platforms and data ecosystems.
• Define and enforce best practices for AI model lifecycle management, including version control, automated testing, monitoring, and CI/CD processes.
• Build and maintain production‑ready AI systems, driving the integration of advanced analytics and machine learning into core business processes.
• Champion technical design sessions, mentor engineering teams, and cultivate expertise in modern AI engineering and MLOps tooling.
• Develop and maintain automated frameworks for model validation, performance monitoring, and drift detection in production environments.
• Collaborate closely with data science teams to operationalize experimental models, transforming prototypes into reliable, scalable solutions.
• Continuously evaluate and adopt emerging technologies in AI engineering, MLOps, and LLMOps to enhance organizational AI capabilities.
• Author comprehensive technical documentation, uphold coding standards, and ensure adherence to enterprise security, compliance, and governance requirements.

• 4+ years of progressive experience in AI/ML engineering, with a proven track record of deploying and scaling AI solutions in production environments.
• High proficiency in MLOps and LLMOps platforms (e.g., MLflow, Kubeflow, Weights & Biases).
• Strong Dev Ops background, including hands‑on experience with containerization (Docker, Kubernetes) and CI/CD pipeline automation.
• Advanced programming skills in Python, with deep familiarity in ML frameworks (Tensor Flow, PyTorch, Scikit‑learn).
• Proficient in leveraging cloud platforms (AWS, Azure, GCP) and their native AI/ML services.
• Solid experience in infrastructure as code (Terraform, Cloud Formation) and configuration management.
• Expertise in model monitoring, drift detection, and performance optimization for production models.
• Strong understanding of data engineering pipelines and real‑time data processing architectures.
• Experience designing and developing APIs and working within microservices architectures.

• Experience deploying Large Language Models (LLMs) and Generative AI solutions.
• Knowledge of AI governance, model explainability, and responsible AI practices.
• Exposure to edge computing and advanced model optimization techniques.
• Familiarity with vector databases and retrieval‑augmented generation (RAG) architectures.
• Experience with data mesh architectures and modern data stack technologies.
• Background in Agile/Scrum methodologies and technical team leadership.

• Effective at managing time and meeting deadlines while leading complex AI initiatives.
• Exceptional communicator, adept at engaging with technical teams, data scientists, and business stakeholders.
• Highly organized, with strong multitasking, prioritization, and leadership abilities.
• Eager to embrace and master emerging AI technologies and complex concepts rapidly.
• Driven by intellectual curiosity and a passion for advancing AI engineering practices.
• Demonstrated ability to deliver enterprise‑scale AI projects on time, within budget, and to the highest…