Top AI Engineer Jobs in NYC, NY

Lead design, build, deploy, and support foundation model training and LLM inference systems, similarity search, guardrails, evaluation, and observability. Optimize production AI for scalability, cost, latency, and throughput. Collaborate cross-functionally, mentor engineers, and shape long-term foundational AI technical vision and roadmap.

Design, build, and deploy production-grade AI and Generative AI solutions. Collaborate with data scientists and platform engineers to implement scalable, observable models, manage ML experiments, automate deployments, and review code. Prototype new AI capabilities and apply LLM/GenAI patterns (RAG, agentic workflows) for enterprise integration.

Lead technical design, development, and operational stability of AI/ML solutions on cloud. Drive adoption and governance of enterprise-authorized AI-assisted development, set validation standards, automate recurring remediation, evaluate vendors, and lead engineering communities of practice while coaching teams on responsible, secure AI usage.

Design and deliver AI/ML solutions for Business Banking: build NLP/LLM models, embed AI checks into campaign workflows, integrate data pipelines, automate quality metrics, and partner with cross-functional teams to operationalize and measure AI capabilities for marketing and campaign validation.

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 design, development, deployment, and support of foundational AI systems (foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability). Optimize large-scale AI performance (cost, latency, throughput), leverage OSS and SaaS stacks, mentor teams, and shape long-term platform vision.

Lead design, development, deployment, and support of foundational AI systems including LLM training and inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI performance (latency, throughput, cost), collaborate cross-functionally, and contribute to technical vision and roadmap for production AI infrastructure.

The AI Engineer will focus on building AI experiences, deploying models, and enhancing data workflows, working on greenfield applications. Key responsibilities include feature development, architecture collaboration, and shipping impactful product experiences.

Own technical delivery of Moveworks AI Agents for enterprise customers: gather requirements, design and build integrations and automations, tune LLM-based solutions, advise on AI roadmaps, synthesize feedback to product, and create reusable templates to scale implementations.

Lead end-to-end delivery of production AI solutions (ML, GenAI, agentic systems, RAG) on the enterprise AI platform. Design, prototype, deploy, monitor, and evaluate solutions; set technical standards; mentor engineers; and partner with stakeholders to drive measurable business value and adoption.

Lead end-to-end delivery of AI solutions (ML, Generative AI, agentic systems), design and deploy production-grade LLM/RAG and multi-agent architectures, define responsible-AI standards and evaluation, mentor engineers, and partner with stakeholders to measure and drive business value using cloud AI platforms and enterprise CI/CD/IaC.

Design and build an enterprise generative AI platform: agent orchestration, memory, guardrails, vector search, SDKs, and blueprints. Standardize agentic workflows, optimize orchestration and cost, own Helm/Kubernetes operators, ensure trust and safety, mentor engineering teams, and evangelize platform architecture internally and externally.

Lead design and delivery of an agentic AI platform: define architecture, standardize agentic workflows, build GenAI SDK/CLI, harden guardrails and observability, optimize orchestration and costs, own Helm/K8s operators for autoscaling, incubate RFCs and proofs of concept, and mentor engineering leaders while evangelizing platform practices internally and externally.

The Senior Lead AI Engineer at Capital One will design, deploy, and support AI systems, working with cross-functional teams to innovate and enhance customer interactions while ensuring scalability and performance.

Lead a cross-functional team to deliver AI-powered banking products, focusing on developing and supporting AI software components and optimizing LLM technologies.

Lead design, development, deployment, and support of production-scale AI systems (foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability). Drive LLM optimizations, technical vision, and cross-functional delivery of responsible AI products.

Design, build, and productionize core AI systems for document understanding, forecasting, and agentic workflows. Handle messy data ingestion, evaluation, monitoring, anomaly detection, entity reconciliation, and backtesting to deliver reliable, product-focused ML solutions.

Design and deliver generative AI and computer vision models and on-device inference for consumer AR experiences. Prototype research advances, optimize models (compression, quantization, distillation), collaborate across teams, and productionize scalable inference pipelines for mobile and wearable platforms.

Lead design, build, deploy, and support foundational AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and optimization. Partner with cross-functional teams to deliver scalable, performant, and responsible generative AI platform services and contribute to long-term technical vision and roadmap.

Design, build, and own scalable data pipelines and evaluation systems that power production AI features and internal analytics. Ensure data quality across ingestion, modeling, and reporting, collaborate with ML and analytics teams, deploy and monitor ML systems, and establish standards for data work and evaluation.

Design, implement, and productionize AI solutions using best-in-class LLMs. Integrate models into production, build scalable AI architecture (orchestration, caching, fallbacks), evaluate new AI technologies, and maintain testing/monitoring and data pipelines for data-intensive workflows.

The Senior Lead AI Engineer will develop and support AI software components, lead cross-functional teams, and optimize AI solutions for Capital One's banking systems.

Lead AI Engineer responsible for developing AI software components, partnering with cross-functional teams to deliver AI-driven products, and optimizing large-scale AI systems.

The Senior Lead AI Engineer will develop AI software components, partner with cross-functional teams, and contribute to AI system optimization. Key responsibilities include model training, deployment, and leveraging AI technologies to enhance banking services.

The Lead AI Engineer will design, develop, and support AI software components, optimizing AI systems for performance and scalability while collaborating with cross-functional teams.