Top AI Engineer Jobs in NYC, NY

Designs and optimizes AI and machine learning solutions, transforms data into insights, and develops production AI systems. Responsibilities include building data infrastructure, speech recognition and NLP tools, and reusable evaluation and monitoring capabilities. The role independently tests AI systems for performance, robustness, bias, explainability, and failure modes; evaluates technical controls; documents findings; communicates recommendations to clients; and mentors junior team members.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Design and deploy AI systems and machine learning models, perform data wrangling and analysis, and integrate solutions into data infrastructure. Develop scalable AI applications, relational data systems, evaluation tools, automated tests, and monitoring capabilities. Assess AI behavior, robustness, bias, explainability, drift, hallucinations, and guardrails while evaluating technical controls and communicating findings to clients and stakeholders.

Develop and implement AI and machine learning solutions, including algorithms, data models, infrastructure, evaluation tools, automated tests, monitoring, and responsible AI controls. Lead testing for robustness, bias, drift, explainability, hallucinations, and failure modes. Manage client accounts and technical workstreams, review evidence and controls, communicate trade-offs, mentor teams, and maintain delivery and professional standards.

Owns the architecture, engineering, operation, and roadmap of a secure AI research platform. Builds scalable CPU/GPU cloud infrastructure, self-service research environments, platform automation, CI/CD, orchestration, and infrastructure-as-code. Partners with AI researchers to enable LLM, agentic AI, retrieval, inference, and benchmarking workloads, while maintaining reliability, observability, security, cost controls, incident response, and operational readiness in a regulated enterprise.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead architecture, cost-performance governance, technical reviews, and long-term roadmap development. Apply advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and inference costs while ensuring responsible AI standards. Mentor senior technical leaders and collaborate with engineers, scientists, technical program managers, and product managers.

Design and deliver enterprise-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Set architecture and optimization strategies for scalable, cost-efficient, responsible AI platforms. Lead model routing, caching, orchestration, and cloud deployments while mentoring engineers, influencing stakeholders, and advancing AI research into production.

Designs, develops, deploys, and optimizes enterprise AI systems, including foundation models, LLM inference, agentic workflows, similarity search, model routing, and observability. Establishes AI architecture standards, improves system cost and performance, champions responsible AI, and leads technical direction, mentorship, and cross-functional collaboration across Capital One.

Design, develop, deploy, and support scalable 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. Define AI architecture, reliability objectives, and technical roadmaps while ensuring security, compliance, and ethical alignment. Lead technical reviews and mentor engineering teams across complex AI product initiatives.

Build evaluation infrastructure, observability tooling, diagnostic systems, datasets, annotation workflows, and experiments for production AI agents. Lead a hands-on AI Quality engineering team, improve agent quality, latency, and cost, and partner with AI Core engineering. The role requires developing evaluation pipelines, scorers, dashboards, CI/CD systems, and model-testing frameworks while managing team priorities and technical direction.

Design, build, deploy, and scale production AI agents and platforms for legal workflows. Develop and evaluate agent solutions, optimize performance and cost, redesign business processes, and promote responsible AI adoption. Collaborate with Legal, Enterprise AI, Data, Security, Privacy, and Engineering teams to deliver secure, reliable, scalable solutions. Contribute frameworks, training, governance guidance, and innovative applications using modern LLM and AI techniques.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead AI architecture, cost-performance governance, optimization, technical design reviews, and responsible AI standards. Partner with engineering, research, product, and program teams while mentoring senior technical staff and shaping the roadmap for foundational AI platforms.

Lead the architecture and development of enterprise AI platforms, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, safety, and transparency. Set long-term technical strategy, drive organization-wide standards, lead platform initiatives, partner cross-functionally, and mentor senior engineering and research leaders.

Build and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, GPU/TPU utilization, latency, throughput, and cost. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to foundational AI roadmaps, and mentor engineers on scalable design and research-to-production practices.

Lead the design, development, and operation of an enterprise GenAI serving platform. Build high-throughput LLM inference, gateway, routing, GPU optimization, quantization, distributed serving, and observability capabilities. Establish reliability practices, SLOs, performance benchmarks, cost controls, and secure production standards. Drive adoption of agentic AI-enabled engineering workflows with governance, auditability, and human validation while influencing senior stakeholders and enabling reusable platform components across teams.

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.

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.

Hands-on technical leader who designs, builds, and deploys production-grade AI automation and agentic workflows. Responsibilities include rapid prototyping, RAG and document-intelligence systems, API integrations, orchestration, monitoring, reusable AI assets, and mentoring a small engineering team to deliver frequent releases and measurable business impact.

Design, build, and operate production AI automations and agentic workflows for Toast’s customer care systems. Integrate LLMs with Salesforce Service Cloud, Amazon Connect, and workflow platforms; develop autonomous actions with human oversight; test, monitor, and improve system reliability, accuracy, latency, and cost. Collaborate with software engineers, Salesforce developers, and care operations stakeholders to translate workflows into scalable intelligent solutions.

Supports AI engineering initiatives at Vantor, contributing to spatial intelligence solutions and helping develop technologies that enable customers to understand current and future conditions. The posting provides limited role-specific responsibilities but emphasizes problem-solving, innovation, collaboration, and mission-focused work. Applicants must qualify as a U.S. Person under applicable export control and ITAR requirements.

Lead design and build of an AI-native marketing platform: integrations, multi-agent orchestration, UIs, tooling, analytics, CI/CD, auth/multi-tenancy, and production AI agents to automate marketing workflows and drive adoption across teams.

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.

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.