Job Title
Job Description Summary
Job Description
AI Model Development
- Lead the end-to-end architecture, development, and deployment of AI, including machine learning, GenAI, and Agentic models that are tailored to business use cases.
- Drive the development of agentic AI systems — including multi-agent orchestration, tool-use, and autonomous task-execution pipelines — to automate complex enterprise workflows.
- Establish model development standards encompassing data preprocessing, feature engineering, model selection, hyperparameter tuning, evaluation, and documentation.
- Partner with data engineering teams to ensure robust, scalable, and high-quality data pipelines that support model training and inference.
AI Operations (MLOps / LLMOps)
- Mature the organization's AIOps (MLOps & LLMOps) capabilities, including CI/CD pipelines for model training, evaluation, deployment, and monitoring.
- Define and enforce standards for model versioning, experiment tracking, reproducibility, and model registry management
- Implement robust model monitoring frameworks to detect performance degradation, data drift, concept drift, and bias in production systems, with automated alerting and retraining triggers.
- Manage cloud AI/ML platform costs and optimize infrastructure utilization across training, fine-tuning, and inference workloads.
AI Innovation
- Serve as an internal AI innovation champion — identifying high-value use cases across business functions and translating them into AI-powered solutions.
- Build and maintain an enterprise AI roadmap aligned with strategic business objectives, balancing quick wins with long-term capability building.
- Foster a culture of experimentation through structured ideation programs, hackathons, and proof-of-concept sprints, ensuring rapid validation and responsible scaling of AI initiatives.
- Collaborate with product and technology leadership to embed AI capabilities into core enterprise capabilities and customer-facing products.
AI Governance
- Partner, support, and execute the organization's AI governance framework, including policies for model risk management, fairness, explainability, privacy, and security.
- Lead AI risk assessments and ensure all models in production meet internal standards and applicable regulatory requirements.
- Partner with Legal, Compliance, and Risk teams to manage data privacy obligations (GDPR, CCPA), intellectual property considerations for generative AI outputs, and third-party AI vendor due diligence.
- Champion sound AI principles organization-wide, ensuring that human oversight and accountability are embedded in every stage of the AI development lifecycle.
Team Leadership & Talent Devel