Top Artificial Intelligence Jobs

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production platforms. Define technical standards for HPC, GPUs, Kubernetes, private and hybrid cloud, networking, storage, reliability, security, observability, and automation. Solve complex scalability and performance challenges, guide cross-functional initiatives, evaluate emerging technologies, mentor engineers, and embed governance, compliance, privacy, and responsible AI into secure, resilient infrastructure.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production AI platforms. Define technical standards across HPC, GPUs, Kubernetes, hybrid cloud, networking, storage, reliability, observability, security, and governance. Solve complex performance and scalability challenges, evaluate emerging technologies, lead cross-functional initiatives, and mentor engineers in a highly regulated environment.

Architect and engineer secure, scalable AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production platforms. Lead HPC, GPU, Kubernetes, cloud, networking, storage, reliability, observability, security, and governance initiatives. Shape enterprise architecture and engineering standards, solve complex performance and scalability challenges, evaluate emerging technologies, lead cross-functional technical programs, and mentor engineers.

Leads Mastercard’s global AI Engineering organization within the AI Center of Excellence. Oversees end-to-end delivery and scaling of enterprise AI products, agentic AI solutions, reusable AI capabilities, and intelligent automation. Partners across engineering, infrastructure, data, security, risk, architecture, product, and business teams to ensure secure, reliable, governed, production-ready solutions. Drives adoption, measurable business outcomes, operational excellence, and responsible AI practices while developing global engineering leaders and teams.

Leads Mastercard’s global AI Engineering organization, delivering enterprise AI products, agentic AI solutions, reusable AI capabilities, and intelligent automation from design through production. Oversees engineering execution, production readiness, security, governance, resiliency, observability, and measurable business outcomes. Partners across platform, infrastructure, data, security, risk, architecture, product, and business teams while building a high-performing global organization of AI leaders, architects, and engineers.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production platforms. Responsibilities include HPC and GPU architecture, Kubernetes, private and hybrid cloud, high-performance networking, storage, observability, security, reliability, governance, and automation. The role drives technical standards, performance engineering, cross-functional initiatives, emerging technology evaluation, and engineer mentorship in a regulated environment.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and emerging production capabilities. Define technical standards across HPC, GPUs, Kubernetes, hybrid cloud, networking, storage, reliability, observability, security, and automation. Solve complex scalability and performance challenges, embed governance and compliance, evaluate emerging technologies, lead cross-functional initiatives, and mentor engineering teams.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production platforms. The role covers HPC, GPUs, rack-scale systems, private and hybrid cloud, Kubernetes, networking, storage, observability, security, reliability, governance, and automation. Responsibilities include solving complex scalability and performance challenges, defining engineering standards, evaluating emerging technologies, leading cross-functional initiatives, and mentoring engineers in a regulated environment.

Leads Roche’s global Biometrics product portfolio, setting strategy, architecture, financial objectives, and AI integration vision for clinical data platforms. Oversees cloud-native pipelines, clinical data ingestion, harmonization, advanced analytics, regulatory reporting, validated systems, and GxP compliance. Manages internal teams, vendors, and delivery partners while driving generative AI and machine learning adoption. Coaches product managers and engineers and aligns scientists, technologists, and executives across global operations.

Lead Model Based Systems Engineering implementation across programs by applying MBSE processes, tools, modeling languages, and frameworks. Guide total MBSE development activities, solve complex technical problems, develop modeling strategies, and collaborate with engineering leadership. Contribute to training materials, best practices, and digital engineering transformation initiatives. The role requires systems engineering experience, strong SysML/UML and MagicDraw/Cameo expertise, and an active Secret clearance.

Lead MBSE implementation across engineering programs by applying SysML, UML, UAF, MagicDraw, and Cameo. Guide total model-based systems engineering activities, develop modeling strategies and models, solve technical problems, support digital engineering transformation, and collaborate with program teams and leadership. Contribute to MBSE training materials, best practices, processes, and technical guidance while supporting Agile development and engineering lifecycle activities.

Leads payer onboarding, transitions, ongoing payer operations, licensing, credentialing, audits, site inspections, and regulatory compliance for Babylist Health. Builds scalable processes, monitors payer performance, manages commercial and Medicaid operations, program-manages new business launches, and partners with billing, legal, product, contracting, and customer support. Manages and develops two direct reports while establishing the operational foundation for expanding clinical services.

Leads global supply chain process transformation, SAP ERP implementation, process harmonization, governance, KPI management, compliance, stakeholder alignment, change management, and continuous improvement across planning, logistics, warehousing, and inventory operations.

Leads the end-to-end development and productionization of AI/ML solutions for trade surveillance and regulatory compliance. Responsibilities include model development, LLM and generative AI experimentation, MLOps pipelines, data engineering collaboration, API integration, monitoring, responsible AI governance, and technical mentorship. The role guides projects from requirements through implementation and delivery while partnering with data science and software engineering teams.

Lead design, development, deployment, and support of secure, scalable cloud-native Java/Spring Boot microservices on AWS. Build REST APIs, event-driven integrations with Kafka, promote CI/CD, observability, secure coding, and enterprise AI-assisted engineering practices; troubleshoot and mentor engineers.

Develops semiconductor yield improvement methods by analyzing fab, defect, electrical, and process data. The role identifies root-cause yield limiters using statistical analysis, machine learning, multivariate algorithms, and coding; creates yield models, measurement recipes, and computational tools; supports product introductions, design-manufacturing collaboration, process changes, and leading-edge logic-node production. Responsibilities also include experimentation, equipment and software debugging, technical documentation, and cross-functional collaboration with design teams, vendors, and manufacturing organizations.

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.

Lead and mentor AI/ML engineers building production-scale machine learning and LLM systems for autonomous medical coding. Define engineering strategy, architect scalable inference and data-processing platforms, establish evaluation and monitoring frameworks, and ensure accuracy, security, privacy, explainability, and regulatory compliance. Collaborate with data science, clinical, product, software, legal, and compliance teams to integrate AI capabilities into healthcare workflows and SaaS products.

Advise cross-functional teams on legal and regulatory matters, including privacy, AI, disputes, and operational risk. Draft, review, and negotiate technology, vendor, professional services, and strategic partnership agreements. Partner with legal operations to improve workflows, automate processes, and create scalable guidance. Collaborate with finance, technology, and operations teams on third-party risk and regulatory compliance while providing practical, business-oriented counsel to senior leaders.

Lead product development for an enterprise agentic AI platform and ServiceNow catalog interpreter from concept through launch. Define product specifications, technologies, developer interfaces, user experiences, success metrics, and roadmap priorities. Partner with engineering, machine learning, design, customer success, and deployment teams; analyze product usage data; support developer ecosystems and hackathons; and build relationships with enterprise CIOs and IT leaders.

Own the Tool Studio product area for an agentic AI platform, driving development from concept through launch. Define product strategy, specifications, success metrics, developer interfaces, and end-user experiences. Partner with engineering, machine learning, design, deployment, and customers to prioritize roadmaps, improve integrations, support adoption, and build the developer ecosystem. Use product usage data, market research, and customer feedback to guide decisions while building trusted relationships with enterprise technology leaders.

Lead the development of data, analytics, automation, and AI-enabled decision-support capabilities for infrastructure portfolio management and strategic execution. Build dashboards, reusable analytics patterns, predictive models, generative AI solutions, and reporting standards that improve investment governance, risk identification, and execution outcomes. Partner with technology, product, risk, and controls stakeholders to ensure responsible AI use, strong data lineage, human oversight, and scalable insights for senior executives.

Senior Counsel will serve as the General Counsel’s first hire, leading commercial contracting, technology agreements, fintech partnerships, and Latin American legal matters. The role independently negotiates complex deals, advises executives on regulated products, handles privacy and financial-services issues, and builds scalable contract review processes using AI legal technology. Candidates must be US-qualified attorneys with Latin American legal credentials, fintech or financial-services experience, Spanish fluency, and at least seven years of legal experience.

Develop technical AI safety standards and governance proposals, analyze frontier AI legislation, engage standards bodies and safety communities, and write policy memos for leadership. The role translates complex AI safety topics—including alignment, compute governance, model weight security, evaluation, and risk reporting—into practical legislation and policy frameworks.

Leads DigitalOcean’s Applied Research function for agentic AI, setting the research agenda across model routing, memory, observability, and reinforcement learning. Builds and manages a research team, connects research investments to product outcomes, and partners with engineering to ship production capabilities that improve reliability, cost, latency, and task success. Represents the company externally through publications, talks, open-source work, and research collaborations.