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

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.

Design, build, deploy, retrain, monitor, and maintain production machine learning models and infrastructure. Develop application code, data pipelines, testing, automation, and cloud-based architectures using distributed systems and containerized environments. Collaborate with Product and Data Science teams in Agile delivery, apply responsible and explainable AI practices, optimize model performance, and support resilient deployments at scale.

Architect, build, and deploy enterprise-scale full-stack applications and autonomous AI agent workflows for HR technology. Lead engineering projects and developers, define AI integration patterns, ensure security and regulatory compliance, mentor engineers, and establish development standards. The role involves cloud solutions, distributed microservices, databases, APIs, agentic LLM systems, RAG, vector search, CI/CD, and emerging AI development tools.

Build and lead enterprise-scale generative AI and agentic systems for HR technology. Design multi-agent workflows using LangChain, LangGraph, LLMs, RAG, and cloud services; establish secure, compliant AI architectures; lead engineering initiatives and developers; define service boundaries and integration patterns; improve system performance and reduce technical debt; mentor engineers and influence long-term technology strategy.

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.

Lead the design, development, deployment, and governance of scalable AI systems, including foundation models, LLM inference, similarity search, guardrails, evaluation, and observability. Define AI platform strategy, optimize production systems, establish Responsible AI standards, and translate enterprise priorities into execution plans. Build and mentor multi-team engineering organizations while partnering with research, product, compliance, and risk teams.

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.