Top Generative AI Jobs in NYC, NY

Lead design, build, and deploy scalable generative AI and ML solutions from prototype to production. Define semantic modeling and ontology strategy, integrate semantics across data pipelines and APIs, enable ontology-driven agents and workflows, implement MLOps, monitoring, and responsible AI controls, and mentor engineers and data scientists to deliver measurable business outcomes.

Design and implement generative ML systems (image, video, audio, multimodal LLMs) and deliver on-device and server-side inference. Build GenAI pipelines and AR experiences, prototype with cross-functional teams, and optimize efficient models for real-time mobile and wearable applications.

As a Director, Data Scientist - Generative AI Systems, you will lead a team to develop AI products using NLP and generative AI technologies, partnering with cross-functional teams. Your role involves leveraging open-source programming and machine learning to convert customer data into actionable insights and enhance customer interactions with financial services.

Own and improve the AI Social render pipeline and agent loops that generate on-brand Instagram carousels and reels. Build prompts, structured outputs, tool-calling, and media composition to ship high-quality generative image, video, voice, and music products in collaboration with product, design, and content strategy.

Design, build, and operate production generative AI services and reusable backend APIs. Combine enterprise data with large language and multimodal models, ensure scalability, performance, reliability, and responsible AI controls. Collaborate with ML, cloud, and SRE teams; translate research into production, optimize inference and cost, and drive operational improvements and measurable business outcomes.

As a Senior Manager, Data Scientist, you will lead a team to develop generative AI solutions and build machine learning models, enhancing customer experiences and operational efficiency.

As a Principal Associate Data Scientist at Capital One, you will develop generative AI-based solutions, collaborate with cross-functional teams, and build machine learning models to derive insights from large data sets.

Lead end-to-end design, development, and delivery of enterprise Generative AI and ML solutions. Architect scalable LLM, RAG, and agent-based systems, drive technical decisions, implement production-ready MLOps/CI-CD, and mentor engineers. Collaborate with product and business stakeholders, ensure monitoring, security, and operational support, and evaluate emerging AI technologies to improve delivery.

Embed with client engineering teams to operationalize LLMs and retrieval systems across multi-cloud environments. Build agentic solutions using Vertex AI Agent Builder and ADK, connect agents securely to BigQuery/Spanner via MCP, implement streaming pipelines (Dataflow) for real-time inference, and manage model training, tuning, deployment, and evaluation on Vertex AI. Provide consultative guidance, cross-functional collaboration, and clear stakeholder communication to ensure business value from Generative AI solutions.

Architect, build, and productionize Generative AI/LLM solutions (RAG, agents, prompt frameworks). Develop scalable Python services, integrate and fine-tune LLMs, design MLOps pipelines, work with vector DBs, ensure enterprise governance, mentor engineers, and translate COO operational needs into robust AI systems.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions on-site, collaborating with engineering teams to operationalize AI systems across multi-cloud platforms.

Build, test, deploy, and monitor production-grade generative AI applications and APIs. Collaborate with data scientists, engineers, and stakeholders to implement RAG, prompt orchestration, vector DBs, and evaluation pipelines on AWS. Write maintainable, well-tested code and contribute to documentation, code reviews, observability, and operational best practices.

Design, build, and deploy LLM-based applications and multi-step AI workflows (retrieval, orchestration, agents) using large-scale structured and unstructured data. Partner with engineering to productionize solutions, implement evaluation and safety measures, ensure compliance and data governance, and communicate results to technical and non-technical stakeholders.

Design, develop, and deploy production-grade generative AI and agentic systems (LLMs, RAG, AI agents). Build document intelligence, retrieval pipelines, vector search, and monitoring frameworks. Collaborate with risk, compliance, product, and engineering to deliver scalable, governed GenAI solutions in regulated environments.

Lead generative AI image production for realistic personas and models. Build and optimize scalable generative pipelines, automate batch workflows, evaluate new models/tools, define technical direction and best practices, and improve image quality, consistency, speed, and cost-efficiency.

Lead the design of generative AI solutions and multi-cloud architectures, translating business use-cases into scalable systems for enterprise deployments.

As a Software Engineer focused on generative AI, you will design scalable AI services, optimize APIs, and collaborate with cross-functional teams to enhance enterprise-wide transformation.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.

Review, evaluate, and annotate AI-generated text, images, audio, and video in Simplified Chinese. Perform quality checks, identify edge cases, participate in calibration, and provide feedback to improve datasets and annotation guidelines.

Lead and execute Generative AI strategy for Global Markets Regulatory Reporting. Define engineering approach, design scalable GenAI solutions, set technology stack, oversee cross-team design and testing, integrate GenAI tools, and drive implementation for multi-tier, microservices-based platforms to deliver business outcomes.

Review, evaluate, and annotate AI-generated text, images, audio, and video; perform quality checks for accuracy and guideline compliance; identify edge cases and inconsistencies; contribute to dataset creation and calibration; provide feedback to improve annotation workflows and guidelines.

Lead research and development of generative AI and NLP models for large-scale email data. Innovate on fine-tuning (LoRA/adapters), quantization-aware training, and knowledge distillation to meet strict latency budgets. Build scalable training/evaluation pipelines, establish robust evaluation frameworks (LLM-as-a-judge, synthetic and human-in-loop), integrate AI-augmented developer tools, and set long-term technical direction while mentoring researchers and driving production-ready model deployments.

As a Senior Solutions Architect, you will assist customers in building solutions with NVIDIA's AI technology, focusing on Generative AI and Large Language Models while collaborating across teams for performance analysis.

The Senior Software Engineer will design and implement Generative AI solutions, collaborate with teams, enhance AI applications, and mentor others in best practices.

Design and execute ML research and end-to-end model development for generative and summary-generation systems. Lead rigorous experiments, translate findings for stakeholders, partner with engineers to deploy models, track state-of-the-art advances, and define roadmaps to improve promotional writing and discovery for members.