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

Lead design and delivery of scalable AI/ML systems: data wrangling, model training, deployment, and infrastructure. Manage teams, mentor staff, own project planning/budgeting, and collaborate with stakeholders to drive AI-driven business insights.

Lead and manage a software engineering team to design, build, test, and deploy AI-enabled applications (LLM integrations, RAG, APIs). Own project planning, budgeting, CI/CD, code quality, observability, microservices, and cross-functional collaboration while coaching and developing team members.

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.

Lead design and implementation of data architecture and scalable storage solutions, drive AI/GenAI thought leadership, oversee data governance and security, translate business requirements into technical plans, implement FastAPI endpoints, enhance LLMs through prompt engineering, and manage cross-functional teams to deliver advanced data and AI solutions.

Lead and manage data science teams to build ML and deep learning models, maintain data pipelines and lakes, ensure data quality, deliver actionable insights, and manage project timelines and client expectations.

Develop AI and data analytics solutions for AML and sanctions clients within PwC’s Risk & Regulatory practice. Responsibilities include Python-based data analysis, designing data architectures and pipelines, creating dashboards, interpreting complex datasets, engaging stakeholders, and delivering actionable insights and tailored client solutions. The role also involves client relationship building, problem-solving, continuous improvement, and contributing to strategic business outcomes.

Lead and manage data and analytics initiatives for M&A and transactions, directing teams to build ML models, dashboards, and integrated data solutions using analytics and visualization tools, ensuring data quality, stakeholder alignment, and actionable insights to drive deal decisions.

Leads fraud and risk-focused data science initiatives, using Python and advanced analytics to generate strategic insights and improve business performance. Manages client engagements, data pipelines, project execution, and teams while mentoring staff and resolving stakeholder issues. Responsibilities include building data architectures, developing visualizations, improving processes, and maintaining professional, technical, and independence standards.

Design and implement AI-enabled integration solutions, cloud architectures, and enterprise technology platforms. Lead strategic planning, analyze business requirements, conduct feasibility studies, coach teams, manage stakeholders, and validate solutions with clients. Apply generative and agentic AI, IoT, sensor data, systems thinking, and enterprise architecture practices to solve complex business problems and drive innovation.

Develop and implement generative AI solutions for client engagements, including contact center applications, machine learning and NLP integrations, prompt engineering, LLM evaluation, and LLMOps governance. Use Python, Azure AI Services, Google Vertex AI, LangChain, and LLM APIs to build, deploy, monitor, and refine AI applications while supporting data analytics and client outcomes.