Top AI Jobs in NYC, NY

Leads data science for enterprise agentic AI products, developing and deploying machine learning models, designing experiments, analyzing telemetry, and translating findings into business strategy. Partners with data scientists, AI/ML engineers, and product managers while guiding large-scale analysis across numeric and textual data. The role also requires technical leadership, stakeholder communication, innovation, talent development, and expertise in statistical modeling, deep learning, and cloud-based data science.

Lead analytics, AI, and machine learning strategy and delivery across Payments, including scalable data products, production deployment, governance, model lifecycle management, and Generative AI initiatives. Partner with business, Quantitative Research, Technology, Product, and enterprise AI teams to optimize deposits, liquidity, balance sheets, and revenue. Drive self-service analytics, AI adoption, stakeholder alignment, regulatory compliance, and measurable business outcomes.

Lead the AI and intelligent automation strategy for Tax operations, including governance, operating model design, roadmap development, and end-to-end solution delivery. Partner with business and technology stakeholders to design, deploy, and continuously improve automation capabilities. Track operational benefits through metrics and dashboards, manage risk and controls, develop infrastructure requirements, coach team members, and support adoption of AI-enabled analytics and workflow automation.

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 product vision and strategy for an end-to-end AI-powered software factory. Integrate planning, coding, review, and verification across GitLab Duo Agent Platform and product areas. Collaborate with engineering, design, and GTM teams, identify gaps and dependencies, present recommendations to executives, and drive execution to deliver cohesive developer workflows.

Lead design and implementation of AI and blockchain-enabled integration and platform architectures. Analyze requirements, develop technical designs, direct large projects, validate outcomes with clients, coach diverse teams, drive cloud strategy (Azure), perform feasibility studies and business process modeling to optimize enterprise technology infrastructure.

Lead one or more teams to design, build, and run cloud-native, AI- and data-intensive tax platforms. Provide technical and people leadership, guide architecture and implementation, drive agile and DevOps practices, mentor team members, collaborate cross-functionally, and transform complex tax challenges into scalable solutions.

Owns the architecture, engineering, operation, and roadmap of a secure AI research platform. Builds scalable CPU/GPU cloud infrastructure, self-service research environments, platform automation, CI/CD, orchestration, and infrastructure-as-code. Partners with AI researchers to enable LLM, agentic AI, retrieval, inference, and benchmarking workloads, while maintaining reliability, observability, security, cost controls, incident response, and operational readiness in a regulated enterprise.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead architecture, cost-performance governance, technical reviews, and long-term roadmap development. Apply advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and inference costs while ensuring responsible AI standards. Mentor senior technical leaders and collaborate with engineers, scientists, technical program managers, and product managers.

Own the vision and roadmap for AI-powered investment operations products across post-trade workflows, including reconciliations, settlements, exception management, and data quality. Partner with Operations and Engineering to deliver production-grade machine learning and generative AI solutions, define requirements, manage agile product delivery, validate use cases with clients and users, and drive adoption and measurable business outcomes.

Develop and deploy scalable AI systems, including foundation models, LLM inference, agent workflows, vector search, guardrails, evaluation, and governance. Optimize model performance for cost, latency, throughput, and accuracy; build training and fine-tuning pipelines; collaborate with research and data engineering teams; and mentor junior engineers while advancing responsible AI practices.

Designs, develops, deploys, and optimizes enterprise AI systems, including foundation models, LLM inference, agentic workflows, similarity search, model routing, and observability. Establishes AI architecture standards, improves system cost and performance, champions responsible AI, and leads technical direction, mentorship, and cross-functional collaboration across Capital One.

Design and deliver enterprise-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Set architecture and optimization strategies for scalable, cost-efficient, responsible AI platforms. Lead model routing, caching, orchestration, and cloud deployments while mentoring engineers, influencing stakeholders, and advancing AI research into production.

Build and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architecture, reliability SLOs, and technical roadmaps while collaborating across engineering, research, infrastructure, and product teams. Lead deployment reviews for security and compliance and mentor senior technical staff.

Design, develop, optimize, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead optimization for cost, latency, throughput, and hardware utilization; establish AI engineering standards; guide technical roadmaps and design reviews; and mentor senior AI engineers.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization. Define AI architecture, reliability objectives, and technical roadmaps while ensuring security, compliance, and ethical alignment. Lead technical reviews and mentor engineering teams across complex AI product initiatives.

Lead real-world evidence studies for pharmaceutical clients using complex clinical, molecular, claims, EHR, and registry data. Design observational analyses, derive endpoints, apply survival analysis and causal inference methods, interpret results, and communicate findings to technical and non-technical stakeholders. Develop reusable analytical code and standards while incorporating LLMs and agentic workflows into research processes. Collaborate cross-functionally to improve data quality, products, and RWE methodologies, with a focus on oncology and clinical development.

Lead real-world evidence studies using complex clinical and molecular data, observational study design, survival analysis, causal inference, and machine learning. Collaborate with pharmaceutical partners, derive clinical endpoints, interpret results, develop reusable analytical code, and apply LLM-based tools to accelerate workflows. Communicate findings to technical and non-technical stakeholders through reports, presentations, manuscripts, and conference materials while partnering cross-functionally to improve data quality and analytical practices.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead AI architecture, cost-performance governance, optimization, technical design reviews, and responsible AI standards. Partner with engineering, research, product, and program teams while mentoring senior technical staff and shaping the roadmap for foundational AI platforms.

Lead the architecture and development of enterprise AI platforms, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, safety, and transparency. Set long-term technical strategy, drive organization-wide standards, lead platform initiatives, partner cross-functionally, and mentor senior engineering and research leaders.

Designs, develops, deploys, and optimizes large-scale generative AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. The role leads architecture and cost-performance optimization, establishes AI engineering standards, contributes to platform roadmaps, and mentors senior technical staff. It requires deep expertise in AI systems, software engineering, cloud platforms, model optimization, and responsible AI deployment.

Build and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, GPU/TPU utilization, latency, throughput, and cost. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to foundational AI roadmaps, and mentor engineers on scalable design and research-to-production practices.

Conduct deep AI and quantitative research for electronic trading by pre-training Transformer and time-series foundation models on large-scale market data. Develop representations, objectives, architectures, distributed training methods, evaluation protocols, and model-serving components. Fine-tune models for alpha generation, pricing, market making, execution, and risk management while studying scaling laws, transferability, robustness, data efficiency, latency, and economically meaningful trading outcomes.

Lead the design, development, and operation of an enterprise GenAI serving platform. Build high-throughput LLM inference, gateway, routing, GPU optimization, quantization, distributed serving, and observability capabilities. Establish reliability practices, SLOs, performance benchmarks, cost controls, and secure production standards. Drive adoption of agentic AI-enabled engineering workflows with governance, auditability, and human validation while influencing senior stakeholders and enabling reusable platform components across teams.