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

Lead design and build of New York Life's enterprise AI platform and agentic solutions. Work across cloud-native infrastructure, model and agent lifecycle, RAG knowledge systems, multi-agent orchestration, MLOps/LLMOps, governance, CI/CD, observability, and production operations. Partner with security, legal, and architecture to embed responsible AI and deliver production-ready, compliant AI systems.

Work on statistical and machine learning solutions and generative AI (LLMs) projects. Translate business questions into models, perform exploratory analysis, feature engineering, model validation, and communicate results to technical and non-technical stakeholders throughout an 11-week summer internship.

Develop, train, and deploy machine learning and generative AI models in production. Focus on engineering aspects: scalable software, MLOps/DevOps integration, CI/CD pipelines, monitoring (e.g., New Relic), and automation frameworks to ensure model reliability and performance.

Lead design and implementation of the enterprise AI platform, defining reference architectures, reusable patterns, and complex services (lifecycle registries, control plane, memory, model abstraction). Build agentic AI capabilities, ensure secure governed deployments, drive portability and engineering standards, review designs, mentor senior engineers, and deliver production-grade AI systems across cloud-native infrastructure.

Lead and build a small senior AI engineering team to design, build, and operate a governed enterprise AI platform and agentic AI solutions. Define architecture, roadmap, and governance; lead cloud and runtime architecture on Google Cloud; mentor engineers; partner with Security, Risk, and business stakeholders; implement AI FinOps, evaluation, and responsible AI controls; and deliver production-grade generative AI, retrieval, memory, orchestration, and agent lifecycle capabilities.

Lead strategy and execution for enterprise intelligence by framing complex AI, data, and technology questions; partner with senior leaders to design and deploy intelligence products; create executive- and Board-level communications; run cross-functional initiatives and governance; and support enterprise planning, portfolio management, and adoption of AI-enabled capabilities.

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%).

Design, build, and deploy cloud-native, agentic AI applications on AWS/GCP. Apply AI and automation to improve developer productivity, design and consume APIs, ensure security and compliance, support generative/agentic AI in production, collaborate in Agile teams, and contribute to architecture and engineering best practices.

As a Data Scientist Associate, you will develop AI solutions, support model lifecycle management, and collaborate with teams in a hybrid work environment.

Design, build, and deploy production-grade AI and Generative AI solutions. Collaborate with data scientists and platform engineers to implement scalable, observable models, manage ML experiments, automate deployments, and review code. Prototype new AI capabilities and apply LLM/GenAI patterns (RAG, agentic workflows) for enterprise integration.