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

Build enterprise AI platform capabilities and production AI applications, including agentic workflows, retrieval-augmented generation, semantic search, vector databases, cloud-native services, and AI model operations. Collaborate with senior engineers on orchestration, developer tooling, governance, security, responsible AI, monitoring, testing, and compliance. Use Python, Google Cloud Platform, Kubernetes, CI/CD, and infrastructure as code while developing scalable AI solutions.

Leads end-to-end delivery of production AI, generative AI, and agentic AI solutions. Designs architectures using multi-agent orchestration, RAG, hybrid retrieval, and human-in-the-loop workflows; establishes evaluation, testing, observability, safety, and responsible AI practices. Partners with business and technical teams to measure value, mentors engineers and data scientists, defines engineering standards, and influences enterprise AI strategy and roadmaps.

Leads end-to-end AI, ML, generative AI, and agentic AI initiatives from opportunity assessment through production deployment and monitoring. Designs multi-agent systems, RAG and GraphRAG solutions, evaluation and safety frameworks, and AI-powered applications using the enterprise platform. Sets technical standards, mentors AI talent, partners with business and technology stakeholders, and measures delivered value. The role also drives responsible AI practices and AI-assisted engineering, with limited travel under 10%.

Design, build, and deploy cloud-native, agentic AI applications on AWS/GCP. Apply AI and automation to improve developer productivity, design and consume APIs, ensure security and compliance, support generative/agentic AI in production, collaborate in Agile teams, and contribute to architecture and engineering best practices.

The Director will strategize and govern AI experiences for field personas, develop frameworks, conduct hands-on AI concept experimentation, and ensure alignment across stakeholders.

Lead design and delivery of full‑stack, cloud‑native AI and GenAI solutions; define AI strategies; architect secure, scalable platforms; mentor engineering teams; run pilots, integrate successful technologies, and partner across cloud, security, data, and business stakeholders to improve developer experience and drive enterprise adoption.

Lead design and implementation of the enterprise AI platform, defining reference architectures, reusable patterns, and complex services (lifecycle registries, control plane, memory, model abstraction). Build agentic AI capabilities, ensure secure governed deployments, drive portability and engineering standards, review designs, mentor senior engineers, and deliver production-grade AI systems across cloud-native infrastructure.

Lead and build a small senior AI engineering team to design, build, and operate a governed enterprise AI platform and agentic AI solutions. Define architecture, roadmap, and governance; lead cloud and runtime architecture on Google Cloud; mentor engineers; partner with Security, Risk, and business stakeholders; implement AI FinOps, evaluation, and responsible AI controls; and deliver production-grade generative AI, retrieval, memory, orchestration, and agent lifecycle capabilities.