Top AI Jobs in NYC, NY

Build reusable, governed AI-driven analytics foundations: semantic standards, reusable libraries, evaluation frameworks, CI/CD quality gates, telemetry, and test suites. Partner with data owners and business stakeholders to translate analyst workflows into validated, auditable, and repeatable analytics capabilities that improve accuracy and adoption.

Designs, prototypes, and scales secure agentic AI systems across enterprise Employee Platforms. The role architects custom agents, orchestration, memory, tool use, evaluation, and human-in-the-loop controls; implements AI solutions in public cloud environments; develops production code; establishes reusable architecture and governance patterns; and advises engineering, product, security, and platform teams on resilient, auditable AI-enabled solutions.

Own and scale the foundational agentic capability across web and mobile by defining reusable building blocks, conversational foundations, and cross-channel frameworks. Partner with Product, Design, and Engineering to write PRDs, epics, and user stories, maintain the backlog, track product metrics, and establish governance, documentation, and adoption pathways to ensure scalable, resilient, consistent agentic experiences.

Develops end-to-end data science and generative AI solutions for payments and merchant services. Responsibilities include designing prompts, building chat assistants and workflow automations, creating Python and SQL data pipelines and Tableau dashboards, testing and monitoring AI models, developing algorithms and data models, and delivering customer insights and analytics. The role collaborates with business stakeholders and data teams to improve decision-making, automate processes, and support commercial growth.

Lead design, build, and scale of generative and agentic AI solutions; deliver production ML systems, reusable platforms, observability and MLOps, mentor AI teams, partner with stakeholders to prioritize and measure enterprise impact.

Conducts research on algorithms for large language models and agentic AI systems, improving learning efficiency, inference performance, accuracy, latency, and compute cost. Develops theoretical foundations, implements performant software, validates methods at scale, solves problems for internal teams, publishes findings, presents research, and contributes to intellectual property protection.

Lead and manage cross-firm AI in Operations program initiatives: develop strategic plans, apply structured problem-solving and design thinking, support multiple workstreams, engage senior stakeholders, standardize AI use cases, and drive scaling and reimagination of AI capabilities across operations.

Design, build, and operate AI-enabled applications and ML/LLM platforms. Deliver production-quality code, SDK/REST integrations, MLOps pipelines, model/versioning and CI/CD, distributed fine-tuning and evaluation, and reusable platform components. Drive adoption of agentic AI workflows, enforce responsible AI and security controls, automate reliability, and influence senior stakeholders to align platform roadmaps with business outcomes.

Lead cross-functional programs to design and automate AI/ML governance for generative and agentic models. Develop program plans, risk mitigation, scalable controls, playbooks, and stakeholder alignment to accelerate model lifecycle delivery while ensuring compliance, quality, and responsible AI practices.

Lead independent oversight of operational, technology, and cyber risks for AI and machine learning platforms and use cases. Assess threat exposures, analyze emerging risks and related events, challenge first-line mitigation strategies, evaluate controls, and collaborate with technology, risk, and corporate-function stakeholders to support resilient and responsible AI adoption.

Summer Analyst in Data & AI will design and build end-to-end data, analytics, and ML/AI solutions using cloud and modern tooling. Responsibilities include developing models and pipelines, running experiments tied to business KPIs, supporting data governance and compliance, creating dashboards, and collaborating in agile teams while receiving mentorship and training.

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.

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.

Leads the architecture, development, deployment, and optimization of large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establishes enterprise AI architecture, performance and safety standards, and multi-year platform strategies. Partners across engineering, research, product, and program management while mentoring technical leaders and driving responsible AI adoption.

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.

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architecture, reliability objectives, governance, security, and compliance while collaborating across engineering, research, product, and program teams. Lead technical reviews, shape foundational AI roadmaps, and mentor senior technical staff.

Lead the architecture and technical direction of AI-powered, 0-to-1 products. Integrate LLMs into reliable, customer-facing production systems; build proof-of-concepts; make high-impact architectural decisions; and translate ambiguous goals into executable roadmaps. The role is approximately 60% architecture, planning, and technical leadership and 40% hands-on backend development. It also involves resolving technical debt, unblocking engineers, and collaborating with Product, Design, and Engineering leadership.

Track and evaluate emerging LLM and AI research, assess models and techniques, build prototypes, and collaborate with engineers to deploy useful methods in client systems. Advise product direction, establish metrics for accuracy, reliability, and cost, and communicate research findings clearly. The role requires strong applied research, engineering, judgment, and communication skills in a fast-changing early-stage environment.

Designs, implements, and supports Python-based AI/ML services and applications. The role integrates enterprise LLM APIs, develops retrieval-enabled and agentic document workflows, builds production web services, and improves system performance and scalability. Responsibilities include troubleshooting, data analysis, document processing, DevOps best practices, cloud infrastructure, database development, stakeholder collaboration, and continuous innovation in AI technology.

Build AI-powered workflows, agents, integrations, and internal tools that automate People Operations. Partner with People, Engineering, and IT to modernize onboarding and other employee processes, while implementing secure access controls, human oversight, escalation paths, and production-ready architectures. Prototype with users, iterate quickly, and measure improvements in efficiency, employee experience, and operational consistency.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Develops AI and automation solutions for IT asset management, including stakeholder agents, anomaly detection, data quality improvements, predictive asset insights, data modeling, dashboards, and regulatory reporting. Leads resiliency review sessions, recommends actions within risk tolerances, translates business needs into data-rich outputs, and presents findings to diverse audiences. The role also supports infrastructure compliance, process improvement, project planning, and production support.