Top AI Engineer Jobs in NYC, NY

Design and ship production LLM features for a SaaS product: build customer intelligence from recordings and text, prioritize testimonials using metadata, create AI-powered surveys/interviews, iterate via research, prototyping, deployment, and evaluation. Own technical decisions and deliver measurable outcomes working closely with engineering leadership and customers.

Leads the design and delivery of secure, scalable distributed systems and AI-enabled applications. Responsibilities include architectural leadership, Java or Scala development, cloud engineering, CI/CD, infrastructure automation, API integration, retrieval-augmented generation, agentic workflows, responsible AI adoption, mentoring, and establishing engineering quality and security standards.

Build and operate the backend for Bilt’s AI-powered Concierge across chat, SMS, email, and iMessage. Responsibilities include agent tools and workflows, multi-agent orchestration, model routing, distributed systems, messaging, authentication, performance optimization, evaluation harnesses, and proactive engagement features. The role requires production backend experience, LLM familiarity, cloud service operation, strong distributed-systems fundamentals, product judgment, AI coding-agent usage, and clear technical writing.

Leads global teams developing production AI, GenAI, RAG, agentic workflow, and MLOps solutions for health systems and health plans. Translates clinical, financial, and operational challenges into HIPAA-compliant use cases involving clinical decision support, population health, revenue cycle, research, and optimization. Oversees healthcare data science engagements, model governance, executive stakeholder communications, client delivery, and team development.

Build and deploy AI systems for startup customers, from initial implementation through ongoing adoption. Identify high-impact AI use cases, develop LLM applications and integrations, collaborate with customer engineering teams, and translate field insights into product roadmap improvements. The role requires strong Python or TypeScript skills, production AI experience, customer-facing communication, autonomy, and comfort working in ambiguous environments.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agents, vector search, guardrails, evaluation, governance, and observability. Optimize models and infrastructure for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish AI cost-performance governance, lead technical design reviews, shape foundational AI roadmaps, and mentor senior AI engineering leaders.

Build and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architecture, reliability SLOs, and technical roadmaps while collaborating across engineering, research, infrastructure, and product teams. Lead deployment reviews for security and compliance and mentor senior technical staff.

Leads an AI engineering team building scalable, responsible generative AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and governance. Establishes technical strategy, optimizes production AI systems for cost and performance, manages cloud deployments, translates enterprise goals into roadmaps, and develops engineering talent.

Architect and engineer secure, scalable enterprise AI infrastructure for LLM training and inference, machine learning, agentic AI, and production platforms. Lead HPC, GPU, Kubernetes, cloud, networking, storage, reliability, observability, security, and automation strategies. Resolve complex performance and scalability challenges, establish engineering standards, evaluate emerging technologies, lead cross-functional initiatives, and mentor engineers in a regulated environment.

Leads the architecture and engineering of secure, scalable enterprise AI infrastructure. Designs platforms for LLM training and inference, machine learning, agentic AI, and production workloads using HPC, GPUs, Kubernetes, hybrid cloud, networking, storage, and automation. Establishes standards for reliability, security, observability, governance, and compliance; resolves complex performance challenges; evaluates emerging technologies; leads cross-functional initiatives; and mentors engineers.

Leads an AI engineering team developing scalable, responsible AI systems, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Oversees architecture, deployment, optimization, build-versus-buy decisions, and long-term technical strategy. Translates enterprise goals into measurable roadmaps, operationalizes responsible AI standards, collaborates cross-functionally, and mentors engineering talent while advancing production AI capabilities.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI infrastructure for cost, latency, throughput, reliability, and hardware utilization. Define system architectures and SLOs, lead technical reviews, ensure security and compliance, contribute to long-term AI platform strategy, and mentor engineering colleagues.

Designs, develops, tests, and optimizes AI-powered workflows for legal practice groups. Creates prompts, workflow templates, knowledge resources, evaluation methods, and quality standards; translates attorney needs into testable AI solutions; monitors performance metrics and user feedback; supports pilots, training, adoption, vendor collaboration, and roadmap development. The role requires strong generative AI, prompt engineering, workflow analysis, quality assurance, and stakeholder communication skills in a legal technology environment.

Develop and maintain enterprise AI/ML solutions using Generative AI, RAG, and agentic frameworks on AWS. Design scalable architectures with Terraform, build CI/CD pipelines using GitLab, automate deployments with Python and Bash, and develop React-based internal tools and APIs. The role also involves troubleshooting, reliability, governance, security, compliance, project collaboration, technical mentorship, and occasional production support or travel.

Designs, develops, deploys, and optimizes production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. The role establishes AI cost-performance standards, leads technical design reviews, contributes to platform roadmaps, optimizes model and hardware utilization, and mentors senior AI engineers while delivering scalable and responsible AI capabilities.

Develop and operate large-scale generative AI systems, including foundation models, LLM inference, agentic workflows, guardrails, model evaluation, vector search, memory, governance, and observability. Lead architecture and optimization for cost, latency, throughput, reliability, and hardware utilization. Define AI service objectives, ensure security and compliance, guide technical roadmaps, and mentor engineering teams.

Designs, develops, deploys, and supports production AI systems, including foundation models, LLM inference, agentic workflows, guardrails, evaluation, and observability. Owns complex AI architecture, reliability objectives, governance, and optimization for cost, latency, throughput, and hardware utilization. Partners across engineering, research, product, and program teams; leads technical reviews; contributes to long-term platform strategy; and mentors senior technical associates.

Designs, develops, deploys, and supports foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, and multi-model orchestration. Optimizes AI systems for cost, latency, throughput, scalability, and hardware utilization. Leads governance reviews and design councils, contributes to technical roadmaps, establishes responsible AI standards, and mentors senior engineering leaders.

Build and operate scalable generative AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluations, governance, and observability. Optimize AI systems for cost, latency, throughput, and hardware utilization; architect multi-model pipelines; establish performance governance and engineering standards; contribute to long-term platform strategy; and mentor senior AI engineers.

Design, develop, deploy, and support large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model performance, scalability, latency, throughput, hardware utilization, and cost. Lead technical reviews and governance, define AI platform roadmaps, integrate heterogeneous AI systems, and mentor senior engineering talent.

Design, develop, deploy, and support large-scale generative AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Establish governance and engineering standards, lead design reviews, contribute to platform strategy, and mentor senior AI engineers.

Build AI-powered features and scalable agent infrastructure for Hex’s data analytics platform. Partner with research and product engineering teams on context, model prompting, experimentation tooling, and platform integration. Lead major product initiatives, develop performant web experiences, maintain high standards for testing and correctness, and mentor engineers. The role involves full-stack development across React, TypeScript, GraphQL, backend services, cloud infrastructure, and data systems.

Build and deploy AI capabilities for Hex’s Notebook Agent and context engine. Responsibilities include running experiments, fine-tuning models, developing AI infrastructure and experimentation tooling, architecting AI-enabled systems, and shipping production experiences for data scientists and analysts. The role partners closely with product teams, spans greenfield initiatives, and offers opportunities for technical and team leadership.

Leads the strategy, design, and deployment of AI and machine learning solutions for tax-focused client engagements. Builds Python-based systems for text analytics, extraction, and sentiment analysis; oversees data preparation, feature engineering, model tuning, and platform integration. The Director develops client relationships, drives business development, mentors teams, promotes responsible AI adoption, and ensures governance and professional standards across initiatives.

Lead design and build of New York Life's enterprise AI platform and agentic solutions. Work across cloud-native infrastructure, model and agent lifecycle, RAG knowledge systems, multi-agent orchestration, MLOps/LLMOps, governance, CI/CD, observability, and production operations. Partner with security, legal, and architecture to embed responsible AI and deliver production-ready, compliant AI systems.