Lead AI Engineer (ML Ops)

About the role

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 DevOps, enabling the design, deployment, and scaling of enterprise-grade AI solutions that underpin our Consulting & Insight Technology strategy.

Key Responsibilities

• 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.

Required Qualifications

• 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 DevOps 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 (TensorFlow, PyTorch, Scikit-learn).
• Proficient in leveraging cloud platforms (AWS, Azure, GCP) and their native AI/ML services.
• Solid experience in infrastructure as code (Terraform, CloudFormation) and configuration management.
• Expertise in model monitoring, drift detection, and performance optimization for production models.
• Solid experience in data engineering pipelines and real-time data processing architectures.
• Experience designing and developing APIs and working within microservices architectures.

Preferred Qualifications

• 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.