Top Artificial Intelligence Jobs

Lead the design and development of an enterprise agentic AI platform for wholesale credit risk model development. Partner with quantitative analysts and stakeholders to define use cases, architect secure RAG and orchestration capabilities, build integrations with data sources, APIs, and LLMs, and establish CI/CD, testing, monitoring, and governance frameworks. Lead and mentor software engineers while overseeing architecture, agile delivery, timelines, and platform milestones.

Leads the University’s enterprise cyber threat intelligence program and serves as the senior expert in AI-enabled cybersecurity operations and automation. Develops threat detection, hunting, incident response, vulnerability prioritization, monitoring, dashboards, playbooks, and automated security workflows. Provides technical leadership during major incidents, establishes responsible AI governance and security controls, researches emerging threats and technologies, and advises cybersecurity, clinical, research, infrastructure, and institutional stakeholders.

Leads Trace3’s AI strategy for a priority industry vertical and assigned anchor accounts. Develops vertical AI narratives, executive relationships, roadmaps, business cases, governance models, solution architectures, accelerators, and reference architectures. Shapes and advances AI pursuits, guides use-case discovery and proofs of concept, ensures delivery fidelity, manages escalations, and measures business value. The role combines enterprise AI consulting, account leadership, executive engagement, opportunity development, and transformation leadership across areas such as financial services, healthcare, government, manufacturing, or retail.

Conduct foundational research in agentic reinforcement learning, post-training, evaluation, environments, and scaling. Design rigorous experiments, implement methods in deep-learning frameworks, build datasets and evaluations, analyze model behavior, and translate findings into production systems. Collaborate with research, systems, backend, and product engineers while communicating results through technical documentation, reviews, publications, and open-source work.

Leads the hands-on design, development, and delivery of applied AI and machine learning products for software engineering workflows. Directs technical discovery, prototyping, model evaluation, architecture, MLOps/LLMOps, and production deployment while managing and mentoring an engineering team. Partners with engineering, product, and business stakeholders to define scalable, secure, responsible AI solutions and measure developer and business impact.

Build and deploy multi-agent AI systems that modernize applications and infrastructure across Goldman Sachs. Responsibilities include agent orchestration, human-in-the-loop learning, AI evaluation frameworks, observability, API and tooling integrations, and translating business needs into agentic workflows. The role requires production experience with LLM-based agents, distributed cloud systems, AI benchmarks, and agentic applications in areas such as data analysis, planning, optimization, or infrastructure observability.

Build and deploy production-grade multi-agent systems for firmwide application and infrastructure modernization. Responsibilities include agent orchestration, autonomous planning and execution, human-in-the-loop feedback systems, AI evaluation frameworks, observability, API and CI/CD integrations, and translating business needs into agentic workflows. The role requires strong software engineering and distributed cloud systems expertise, direct experience operating LLM-based agents, and experience applying agentic patterns to data analysis, observability, optimization, or planning.

The Principal leads AI platform initiatives, driving strategy and technical execution. This role includes product management, cross-functional program leadership, and governance in AI adoption across the organization.

Designs and builds enterprise AI agents, Power Platform applications, and automation solutions with business stakeholders. Establishes architecture standards, reusable components, ALM practices, and platform guardrails; owns Dataverse architecture for Dynamics CRM and Dynamics 365 F&O. Leads agent grounding and integration architecture using RAG, MCP, A2A, knowledge sources, enterprise search, connectors, and APIs. Provides technical direction, code reviews, quality standards, and mentorship to a distributed India Center of Excellence.

Leads a portfolio of enterprise applied AI initiatives from strategy and prioritization through production deployment and continuous improvement. Oversees generative AI, agentic AI, machine learning, architecture, evaluation, governance, observability, and responsible AI practices. Manages multidisciplinary teams, establishes reusable platforms and technical standards, guides cloud and technology decisions, and partners with executives, business teams, security, compliance, vendors, and technical stakeholders to deliver reliable, secure, measurable AI capabilities.

Designs and implements AI and machine learning capabilities for cyber and intelligence workflows. The role translates mission-user processes into LLM-powered, agentic, or hybrid solutions; integrates AI with APIs, enterprise data, and mission platforms; and ensures reliable, secure production deployment in classified environments. Collaboration with analysts, operators, developers, and leadership is central. Required qualifications include Python, TS/SCI with polygraph, and relevant AI/ML or software engineering experience.

Manages Sinclair’s AI opportunity pipeline by discovering operational problems, evaluating and prioritizing AI opportunities, developing business cases, and preparing project briefs for AI Product and Delivery. Builds cross-functional relationships, supports the ChatGPT Champions Program, enables employee adoption, tracks usage and impact, prepares leadership reporting, and remains connected throughout project delivery.

Evaluate LLM and automation outputs for accuracy, consistency, and reliability; create annotations and training datasets; research ambiguous cases; support product recognition and classification models; and use AI tools to improve operational efficiency. The role also involves maintaining model and catalog quality through mapping and classification work.

Develop and deploy production-grade AI/ML solutions on Microsoft Azure. Build secure data pipelines, prepare and validate datasets, support NLP and machine learning workloads, and implement MLOps practices including CI/CD, monitoring, troubleshooting, and model lifecycle management. Collaborate with engineers, analysts, and stakeholders in Agile teams while producing architecture, testing, security, and operational documentation for regulated government environments.

Lead AI enablement for investment teams by translating LLM and generative AI capabilities into practical workflows, training and coaching investors, creating reusable prompt libraries and playbooks, guiding agentic designs, partnering with engineers and product teams, and ensuring responsible, secure adoption with measurable outcomes.

Build and deploy AI agents, automations, and tools that transform organizational processes. Embed with teams to map workflows, deliver time-boxed solutions using Claude and related platforms, measure business value, document and hand off production-ready systems, troubleshoot during stabilization, and create reusable patterns. Partner with AI Platform Operations, IT, risk, and staff while ensuring responsible handling of sensitive data and teaching teams to maintain solutions.

Leads end-to-end development of an AWS-based healthcare analytics platform across frontend, backend, data infrastructure, and AI capabilities. Sets architecture and engineering standards, writes and reviews code, mentors engineers, and partners with product, data engineering, and data science teams. Develops ETL pipelines, production machine learning services, generative AI features, and scalable analytics products while maintaining security, governance, privacy, and healthcare data quality standards.

Drive Pinterest’s programmatic advertising revenue through full-funnel sales strategies for major clients. Build executive and agency relationships, negotiate partnerships, advise on programmatic buying and measurement, oversee campaign setup and optimization, and deliver data-driven growth recommendations. Collaborate with Sales, Product, Engineering, and Operations teams while using AI for account research, campaign insights, forecasting, scenario planning, QA, and workflow automation.

Integrate and fine-tune existing AI models within production software, evaluate model performance, build evaluation frameworks, configure observability, develop Python AI features, and troubleshoot real-world performance. Collaborate with data science, engineering, the CTO, and cross-functional teams to improve products and identify AI innovation opportunities. This role focuses on applying existing models rather than designing new machine learning models.

Lead client-facing AI transformation and enablement engagements for mid-market companies. Design and deliver practical sessions that drive executive confidence, behavior change, and measurable AI adoption. Own client relationships between sessions, provide follow-up support and strategic guidance, stay current on AI tools, and identify adoption patterns that improve the company’s methodology. The role is full-time and remote, with travel to client sites required for some engagements.

Own strategic alliance and client relationships from discovery through deployment and expansion. Lead complex AI simulation engagements, joint selling, proposals, partner enablement, Centers of Excellence, governance, playbooks, and repeatable go-to-market systems. Collaborate with engineering, product, deployment, and commercial teams to translate partner needs into scalable solutions. Represent the company with senior executives and help partners embed the technology across their organizations and client offerings.

Supports an Agency MBS and CMO trading desk through market analysis, valuation assessment, relative value idea generation, and risk evaluation. Builds datasets, models, data tools, and automation workflows using Python and Excel/VBA. Partners with risk, modeling, and technology teams to improve desk analytics and infrastructure. Applies AI tools to research and workflow optimization, and communicates market views, opportunities, and findings to traders, salespeople, and clients.

Owns the university’s learner-facing digital experience across CRM, SIS, portals, and AI tools from initial contact through graduation. Responsibilities include business analysis, system configuration, data integrity, reporting, UX decisions, issue triage, requirements gathering, and cross-functional enhancement delivery. The role manages projects, supervises employees, maintains documentation, monitors system performance, and improves recruitment and enrollment processes through Salesforce, analytics, and AI-driven workflows.

Build and operate customer-facing AI agent experiences for loyalty programs, including LLM workflows, tool calling, guardrails, evaluations, monitoring, personalization, and streaming chat. Integrate frontend, backend, APIs, data services, and AI orchestration layers. Partner cross-functionally to ship measurable products, maintain quality, protect sensitive data, and support features in production.

Owns the architecture and technical direction of an AI-enabled enterprise platform built with secure, cloud-native microservices. Leads technical execution, architecture, coding, code reviews, mentoring, release management, production support, and integration design. Partners with product and engineering stakeholders to translate requirements into milestones and deliverables. Designs AI application patterns, APIs, data flows, identity integrations, CI/CD practices, and secure deployments to Azure Kubernetes Service in Azure Government.