Top Artificial Intelligence Jobs

The Senior AI Engineer will focus on building AI-driven pipelines, semantic technologies, and enterprise ontologies while mentoring and ensuring best practices in a hands-on role.

Lead product strategy for GenAI safety across Pinterest: build proactive safety frameworks, run threat modeling and red-teaming, translate policy into product requirements, define safety metrics, manage incident response, and collaborate cross-functionally to ensure equitable, scalable AI safeguards.

The role involves managing the AI product lifecycle, building LLM-based workflows, defining product requirements, monitoring performance, and collaborating with teams to ensure effective AI solutions.

Embed with lending and finance desks to map workflows, identify AI redesign opportunities, build and iterate prototypes (RAG/agent-based), transition validated solutions to production, contribute reusable skills to a firm-wide catalog, and partner with tech, legal, compliance, and desk leadership to ensure adoption, governance, and ongoing performance monitoring.

Lead and implement AI-assisted engineering workflows within software development, focusing on scalable AI integration, CI/CD automation, and team enablement.

Design, research, and deliver LLM and GenAI solutions for healthcare SaaS at scale: architect systems, handle healthcare data, implement PEFT and RAG pipelines, ensure responsible AI, mentor teams, and translate technical outcomes to business impact.

Lead discovery and modernization of expert-driven manual processes using LLMs, AI agents, and automation. Embed with domain experts to extract tacit knowledge, produce formal process models, design multi-step agent workflows with HITL escalation, replace legacy tools, and build evaluation and audit frameworks to ensure safe, verifiable production deployment.

Design, develop, and maintain scalable AI/ML systems and end-to-end pipelines (data preprocessing, model training, prompt engineering). Collaborate cross-functionally, optimize model performance, ensure fairness and security, monitor production systems, troubleshoot issues, mentor junior engineers, and recommend improvements to data infrastructure and tooling.

The Experience Architect focuses on building Agentforce-powered experiences, driving solution architecture, and executing strategies for generative AI technologies in a customer-facing role.

Lead engineering of the full embodied systems stack for real-world robots, owning onboard inference, data collection, teleoperation, and on-robot RL systems. Set architecture and technical direction, deliver reliable real-time deployments, close the loop between data, models and actions, and mentor engineers to scale embodied AI in industrial environments.

The role involves building and maintaining AI infrastructure, optimizing training performance, and developing tools for robot training and data workflows.

The Officer, Model Risk Management validates bank-wide models, ensuring compliance with regulations. Responsibilities include developing testing plans, assessing model performance, reviewing documentation, and collaborating with stakeholders for effective model governance.

Lead a team to design, build, and operate scalable, secure platform and MLOps capabilities on GCP and hybrid cloud. Drive DevSecOps, CI/CD, telemetry, and platform modernization to accelerate AI/ML and software delivery while managing delivery, budgets, vendors, and stakeholder alignment.

Lead clients in designing and deploying Microsoft Azure AI solutions. Oversee multiple projects, engage in client discussions, and support sales efforts.

Lead research and thought leadership on data management and AI readiness, produce actionable insights, advise clients and sales, present at events, mentor analysts, and improve research processes to drive client outcomes.

Act as a technical partner to internal teams to design and implement LLM-powered solutions. Build prototypes and POCs, advise on system architectures, support end-to-end deployment, and keep teams current on LLM best practices and tools while prioritizing safety and responsible use.

Partner with internal teams to design and implement LLM-powered applications and architectures. Build prototypes and proofs-of-concept, advise on system-on-system design, support end-to-end deployment, and keep teams current on evolving LLM techniques and best practices while prioritizing safe, responsible deployment.

Work with internal teams to design and implement LLM-powered solutions: build prototypes, advise on system architecture and safe deployment, support end-to-end implementation, and keep current on LLM techniques and tools.

Lead research on AI integration in manufacturing, develop actionable insights for clients, analyze market trends, and provide expert guidance on digitalization and AI strategies.

Partner with internal teams to design and implement LLM-powered applications. Build prototypes and system architectures, advise on prompt design, retrieval and agent systems, and support end-to-end deployment while prioritizing safety, testing, and responsible use of AI technologies.

Partner with internal teams to design and implement LLM-powered applications. Build prototypes, advise on system architectures, support end-to-end integration, and promote safe, responsible LLM deployment.

Lead architecture and integration of hyperscale server and rack solutions, driving technical strategy with customers and suppliers. Provide technical leadership across engineering, design and business teams, resolve performance and operational issues, and steer new product development from concept through delivery with emphasis on firmware, BMC, security, and high-speed system design.

Act as a technical partner to internal teams to design and implement LLM-powered applications. Build prototypes and proofs-of-concept, advise on system-of-system architecture, support end-to-end deployment, and keep teams current on LLM techniques, safety, and best practices while coordinating cross-functional stakeholders.

Lead the vision and execution of AI and automation strategies across multiple sectors, ensuring integration and compliance with regulatory standards while steering teams and optimizing workflows for operational efficiency.

Support internal teams to design, prototype, and deploy LLM-powered applications. Act as the technical lead across Sales, Product, and Engineering, advising on system architecture, testing approaches, and responsible LLM integration while keeping current with evolving LLM techniques and tools.