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

The Director of AI/Agentic Experience Strategy leads AI-driven customer lifecycle strategies, facilitates AI experience design, and collaborates across teams to enhance consumer interactions in financial services.

The Director of AI Search leads New York Life's AI Search strategy, overseeing AI acquisition strategies and driving AI innovation in marketing experiences. The role requires collaboration across functions to optimize content for AI discoverability and define future-state AI experiences while maintaining compliance and stakeholder alignment.

Lead New York Life’s enterprise AI governance function by building agentic governance solutions for security, risk, compliance, legal, and auditing across the AI lifecycle. Manage expert review teams, establish guardrails and control frameworks, oversee third-party AI governance, support regulatory examinations, and represent the organization with senior internal and external stakeholders. The role requires deep AI technology fluency, regulated-industry governance expertise, and executive-level leadership.

Design and operationalize Python-based AI services, including LLM integrations, RAG, agent workflows, MCP, vector search, guardrails, and evaluation pipelines. Build a reusable enterprise AI and agent services layer, integrate with Java and other enterprise platforms, and develop secure data pipelines. Collaborate with engineering, compliance, legal, risk, and model governance stakeholders to ensure safe, auditable AI in a regulated insurance environment. Contribute to production-quality code, testing, CI/CD, observability, architecture reviews, and operational readiness.

Paid 10-week AI and data science internship supporting data analysis, dashboards, machine learning, generative AI, LLM, and AI application projects. Interns will work with technical and business stakeholders, research emerging technologies, develop prototypes, and present insights within the asset management industry. Undergraduate and master’s tracks offer exposure to business intelligence, cloud platforms, financial technology, and production-ready AI solutions.

Lead the design and delivery of enterprise AI platform capabilities and production agentic AI solutions. Build multi-agent systems, RAG and knowledge management solutions, cloud-native infrastructure, and MLOps/LLMOps capabilities. Partner with business, architecture, security, legal, and risk stakeholders; influence architecture decisions; mentor engineers; and establish reusable engineering, governance, monitoring, and operational practices.

Build enterprise AI platform capabilities and production AI applications, including agentic workflows, retrieval-augmented generation, semantic search, vector databases, cloud-native services, and AI model operations. Collaborate with senior engineers on orchestration, developer tooling, governance, security, responsible AI, monitoring, testing, and compliance. Use Python, Google Cloud Platform, Kubernetes, CI/CD, and infrastructure as code while developing scalable AI solutions.

Designs and delivers enterprise AI platform capabilities and production-ready agentic applications. Builds multi-agent systems, RAG and knowledge platforms, orchestration frameworks, cloud-native services, and developer tooling. Implements MLOps and LLMOps for deployment, evaluation, monitoring, observability, and optimization. Partners with stakeholders, contributes to architecture and engineering standards, and independently owns initiatives from design through production deployment.

Leads end-to-end delivery of production AI, generative AI, and agentic AI solutions. Designs architectures using multi-agent orchestration, RAG, hybrid retrieval, and human-in-the-loop workflows; establishes evaluation, testing, observability, safety, and responsible AI practices. Partners with business and technical teams to measure value, mentors engineers and data scientists, defines engineering standards, and influences enterprise AI strategy and roadmaps.

Leads end-to-end AI, ML, generative AI, and agentic AI initiatives from opportunity assessment through production deployment and monitoring. Designs multi-agent systems, RAG and GraphRAG solutions, evaluation and safety frameworks, and AI-powered applications using the enterprise platform. Sets technical standards, mentors AI talent, partners with business and technology stakeholders, and measures delivered value. The role also drives responsible AI practices and AI-assisted engineering, with limited travel under 10%.