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

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.

Leads enterprise data science initiatives for agentic AI products, including AI/ML model development, experimentation, telemetry analysis, and strategic recommendations for senior leadership. Partners with data scientists, engineers, and product managers while translating complex statistical insights into business outcomes. Requires extensive experience in quantitative analytics, large-scale data analysis, production AI/ML deployment, A/B testing, and team leadership.

Designs, develops, deploys, and supports large-scale AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, and model evaluation. Defines enterprise AI architecture, optimizes model performance, cost, latency, and throughput, establishes AI safety and governance standards, leads multi-year platform initiatives, and mentors senior technical leaders across engineering and research.

Build enterprise Generative AI solutions for Commercial Banking using multi-agent systems, advanced RAG, context engineering, tool-calling frameworks, and machine learning. Partner with data scientists, ML engineers, and product managers to develop, evaluate, validate, fine-tune, and deploy AI pipelines over large numeric and textual datasets. Translate complex technical work into business outcomes while researching emerging AI methods.

Leads a US Card data science team developing machine learning solutions for customer behavior prediction and valuation forecasting. Partners with data scientists, engineers, product managers, and business stakeholders; builds models through design, training, validation, evaluation, and implementation; analyzes large-scale numeric and textual data; translates technical findings into business outcomes; and develops team talent.

Lead development of large-scale recommender and personalization systems using transformers, LLMs, reinforcement learning, sequence modeling, and multimodal learning. Build and evaluate foundation models and low-latency production systems using customer interaction data. Collaborate with engineering and product teams, translate research into practical applications, and support innovation in generative recommendations, conversational systems, and cross-domain personalization while developing team talent.

Develop enterprise Generative AI solutions for Commercial Banking using multi-agent systems, advanced RAG, context engineering, and tool-calling frameworks. Partner with data scientists, machine learning engineers, and product managers to build, evaluate, validate, fine-tune, and deploy AI pipelines across numeric and textual data. Apply statistical modeling, machine learning, deep learning, NLP, information retrieval, and cloud technologies while translating complex technical work into business outcomes.

Conduct applied AI research and develop foundation models from design through training, evaluation, validation, and production deployment. Collaborate with data scientists, software engineers, machine learning engineers, and product managers to deliver scalable AI products. Lead research initiatives, evaluate emerging methods, optimize large-scale deep learning systems, and translate scientific findings into customer and business impact.

Design, develop, deploy, and optimize large-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, orchestration pipelines, and observability. Lead cost-performance governance, technical design reviews, AI standards, roadmap development, and mentorship while improving model scalability, latency, throughput, hardware utilization, and inference costs.

Design, develop, deploy, and support scalable generative 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 across distributed cloud platforms. Own AI system architecture and reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term platform roadmaps, and mentor engineering staff.