Top AI Jobs in NYC, NY

Leads teams designing and deploying scalable AI and machine learning solutions. Responsibilities include data wrangling, software development, data infrastructure and pipeline management, model training, complex analysis, stakeholder collaboration, project execution, and mentoring. The role uses Python, C++, TensorFlow, and Scikit-Learn to transform data into business insights and drive innovation.

Build AI-powered features and products for clinical documentation and medical coding. Develop LLM-driven agentic systems, APIs, services, and production infrastructure across healthcare revenue cycle workflows. Create evaluation frameworks and experimentation loops using statistical and data-driven methods to ensure reliability, auditability, and alignment with coding guidelines and user feedback. Collaborate with product, design, machine learning, clinical, and revenue cycle experts to deliver high-impact SaaS solutions.

Develop and support foundational AI systems, including model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Optimize AI platforms for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish governance standards, lead design reviews, define technical roadmaps, and mentor senior AI engineers while ensuring responsible and compliant AI deployment.

Design, develop, optimize, deploy, and support foundational AI systems, including LLMs, agents, vector search, model evaluation, orchestration pipelines, and responsible AI controls. Lead cost-performance governance, technical design reviews, roadmap development, and optimization of model and hardware utilization. Mentor senior AI engineers while partnering with research, product, program, and engineering teams to deliver scalable production AI platforms.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establish enterprise AI architecture standards, optimize model performance and infrastructure costs, lead hybrid and multi-model orchestration, promote responsible AI, and mentor engineering teams while influencing technical strategy and research-to-production processes.

Conduct applied research on large language models, training optimization, and fine-tuning. Design, train, evaluate, validate, and deploy AI foundation models using PyTorch, AWS, Hugging Face, and Lightning. Lead research initiatives across data science, engineering, and product teams, translating advances into scalable production systems. Candidates should have advanced academic training and experience with large-scale deep learning, model optimization, self-supervised learning, robustness, explainability, or RLHF, plus a record of impactful research and publications.

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, reliability, and hardware utilization. Own complex AI architecture, define reliability objectives, lead technical reviews, collaborate across engineering and research teams, and mentor senior technical staff.

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establish enterprise AI architecture standards, optimize model performance and infrastructure costs, guide hybrid and multi-cloud strategies, and promote responsible AI practices. Lead technical direction, mentor engineering teams, collaborate with cross-functional stakeholders, and advance research techniques into production.

Leads AI engineering strategy and execution across Mastercard, overseeing AI/ML architecture, deployment, monitoring, optimization, governance, and risk management. Partners with product, technology, data science, and business leaders to deliver scalable AI solutions and measurable business impact. Provides technical mentorship, establishes engineering standards, evaluates emerging technologies, and guides enterprise transformation through generative AI and machine learning.

Lead the AI Quality engineering team while building evaluation infrastructure, observability tools, diagnostic systems, datasets, annotation workflows, and experimentation frameworks for production AI agents. Diagnose agent failures, evaluate models and prompts, improve quality, latency, and cost, and partner with AI Core engineering. This is a hands-on player-coach role involving technical direction, team management, and production software development.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Define reliability objectives, lead architecture and technical reviews, ensure security and compliance, contribute to long-term AI platform strategy, and mentor senior technical staff.

Design, develop, deploy, and optimize foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, guardrails, and observability. Lead multi-model orchestration, cost-performance governance, technical design reviews, and AI platform roadmap development. Apply advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and inference costs while mentoring senior technical teams and promoting responsible AI standards.

Develop and deploy advanced machine learning and data science solutions across large numeric and textual datasets. Partner with data scientists, engineers, and product managers to build customer-focused products. Lead model design, training, evaluation, validation, and implementation while researching emerging technologies and translating complex analyses into business outcomes.

Lead the architecture, development, deployment, optimization, governance, and observability of scalable AI platforms and systems. Build foundation model training, LLM inference, agentic workflows, similarity search, guardrails, and evaluation capabilities. Establish enterprise AI safety and transparency standards, drive long-term infrastructure strategy, mentor technical leaders, and guide organization-wide adoption of responsible AI solutions.

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for scalability, cost, latency, throughput, and hardware utilization. Own complex architectures, define reliability objectives, lead technical reviews, collaborate across engineering and product teams, and mentor senior technical staff.

Design, build, deploy, and support enterprise AI systems including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model training and inference for scalability, cost, latency, throughput, and hardware utilization. Establish enterprise AI architecture standards, lead multi-cloud strategies, mentor engineering teams, promote responsible AI, and influence long-term technical roadmaps.

Design, develop, deploy, optimize, and support large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and orchestration pipelines. Lead cost-performance governance, technical design reviews, AI engineering standards, and long-term platform architecture. Apply state-of-the-art optimization techniques for latency, throughput, scalability, hardware utilization, and inference costs while mentoring senior technical staff and ensuring responsible AI practices.

Design, develop, deploy, and support foundational 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 scalable production systems. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term roadmaps, and mentor engineering staff.

Conduct applied research and develop large-scale AI foundation models, including vision-language models, across design, training, evaluation, validation, and deployment. Collaborate with data scientists, engineers, and product managers to deliver AI-powered products. Research optimization, self-supervised learning, robustness, explainability, and fine-tuning methods, while producing technical reports and conference submissions.

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

Leads data science across product and business areas, developing metrics, causal inference methods, forecasting models, and reusable analytics tooling. Partners with product and business leaders to guide roadmaps, evaluate growth, and communicate insights. Applies LLMs and agentic workflows to automate analysis and improve self-service analytics. Requires strong statistical modeling, experimentation, Python or R, SQL, Databricks, cross-functional influence, and mentorship experience.

Lead product strategy, roadmap, OKRs, and execution for an AI product pod. Partner with design, engineering, marketing, and customer success to develop user-facing AI capabilities from discovery through launch and iteration. Conduct user research, define MVPs, evaluate product quality, prioritize initiatives, mentor junior product managers, and improve internal AI-enabled product development practices.

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, evaluation, and observability. Optimize training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architecture, reliability objectives, governance, and technical roadmaps while collaborating cross-functionally and mentoring engineering teams.

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agents, similarity search, guardrails, evaluation, governance, and observability. Optimize AI infrastructure for cost, latency, scalability, throughput, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term AI roadmaps, and mentor engineering talent.

Develop and deploy foundational AI systems, including LLM inference, foundation model training, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish technical standards and governance, lead design reviews, contribute to long-term AI roadmaps, and mentor senior AI engineering staff.