Top AI & Machine Learning Jobs in New York City, NY

The role involves designing, building, and maintaining an MLOps platform, developing workflows for ML applications, and integrating various technologies while supporting agile teams.

The Director of AI & Data Science will lead AI/ML initiatives, manage the Model Development Life Cycle, and mentor technical teams, while overseeing core AI technical solutions and standards.

The Senior Associate - Data Scientist will develop AI and machine learning solutions, focusing on model lifecycle management and delivering AI initiatives in collaboration with business teams.

As a Data Science Lead, you'll drive AI/ML initiatives, mentor data scientists, collaborate with stakeholders, and ensure effective model deployment and governance.

The role involves model validation, monitoring AI model performance, challenging model developers, and translating technical requirements into practical evaluations and controls for AI and ML models.

As an AI Product Insights Analyst, you'll develop AI-enabled analytics frameworks, manage data models, and collaborate to enhance decision-making through Generative AI applications.

This role involves managing AI and data product strategy, requirements, and roadmaps, partnering with engineering and business teams to enhance AI solutions.

Lead development and deployment of AI/ML, GenAI, and agentic solutions across the model lifecycle. Perform data exploration, feature engineering, model training, validation, deployment, and monitoring using cloud and modern data platforms. Implement LLM/RAG and safety guardrails, build prototypes, collaborate with cross-functional teams, follow MLOps/CI/CD and governance, and support business-aligned outcomes. Limited travel (<10%).

The AI Analyst will leverage Generative AI technologies to develop models, create data pipelines, and collaborate with cross-functional teams to drive AI integration across the organization.

Lead and found an enterprise AI platform engineering organization: set technical vision and architecture, build teams across Data, Software/AI, and MLOps, drive adoption with senior stakeholders, manage hyperscaler and consulting partnerships, and ensure security, governance, and compliance for enterprise AI at scale.