Top Artificial Intelligence Jobs

Lead strategic engagement with AI platform and tools ecosystem, enhance partner product direction, technical onboarding, and ecosystem growth with NVIDIA's technology.

Develop and support Rescale's HPC platform, integrate AI into CAE workflows, solve customer HPC/AI problems, build AI software, collaborate with engineering teams, and engage directly with customers.

Lead design and delivery of Ripple's AI platform (GSmart). Own roadmap, enable product teams, operate production AI systems, hire and mentor engineers, contribute to architecture and full-stack code.

Lead integration of AI/ML (LLMs, RAG, generative workflows) into a cloud-native full‑stack platform. Architect serverless backends, optimize data pipelines, modernize legacy PHP APIs, maintain CI/CD, mentor engineers, and support secure, defense-constrained deployments.

Lead strategy and execution of AI, Generative AI, and data science solutions across R&D. Build and mentor a team, architect scalable regulated AI/ML systems (including LLMs and RWD workflows), ensure governance and compliance (GxP/HIPAA), and partner cross-functionally to deliver production AI capabilities across clinical development, pharmacovigilance, and other R&D functions.

Lead AI solution strategy, discovery, and design for client engagements. Qualify use cases, assess data readiness, build prototypes, develop business cases, support proposals, and ensure delivery handoff while partnering with cross-functional teams to drive account growth.

Lead UX Operations for AI Enablement by defining frameworks, workflows, and governance to design, test, and scale AI-enabled and agentic experiences. Run pilot programs, measure adoption, partner with L&D to deliver training, and translate learnings into playbooks and operating models that ensure human-agent collaboration, accessibility, ethics, and quality across product development.

Lead strategy and implementation of an advanced Quality Management system, AI-driven design tools, and Lean engineering across Heavy‑Duty New Unit teams. Drive KPIs, reduce rework and cycle time, mentor a medium team, champion cultural/process change, and transition engineering workflows to agile, data-driven execution.

Lead product strategy and roadmap for AI Ready Data capabilities—data ingestion, transformation, enrichment, synthetic data, and tooling for retrieval, fine-tuning, evaluation, and agent workflows. Write product specs, partner with engineering, research, design, and GTM, translate experiments into scalable features, work with customers to surface requirements, and define success metrics to monitor adoption and performance.

Six-month, on-site AI/ML co-op to develop, deploy, and monitor production ML and time-series forecasting models. Responsibilities include data sourcing/cleaning, building ML pipelines, model evaluation and validation, drift detection, retraining triggers, and maintaining audit-ready production models using ML tooling.

12-week, on-site summer fellowship for MS/PhD students to formulate problems, build simulations, and develop RL/optimization/forecasting models for construction scheduling, capital allocation, and security operations. Conduct rigorous experiments, maintain reproducible code, and collaborate on production paths. U.S. Persons only; no visa sponsorship. Housing and relocation provided for non-local fellows.

Lead product strategy and roadmap for an enterprise AI development platform enabling data ingestion, model fine‑tuning, orchestration, governance, and observability. Drive secure, compliant ML lifecycles and tooling so builders can train, deploy, monitor, and debug large-scale models and multi-step agentic workflows for global customers.

Design and iterate multi-channel conversational experiences (chatbots/voice) including intents, entities, prompts, fallbacks, and testing. Create conversation artifacts, define channel-specific behaviors, enforce style and safety guidelines, support platform implementations, use analytics to optimize flows, collaborate with UX/engineering/security, and maintain operational reliability and observability.

Lead global strategy and execution of AI-driven media governance, ensuring transparency, brand safety, and investment integrity. Build policies, oversight protocols, dashboards, and contractual terms for agencies and platforms while embedding compliance, AI literacy, and automated governance operations across markets.

Lead product strategy and roadmap for AI-powered laboratory analytics. Define use cases, engage customers, drive discovery and pilots, partner with engineering and data science, measure adoption and business impact, and support go-to-market and cross-functional alignment.

Lead technical strategy, design and deploy scalable AI/ML systems. Lead end-to-end model development, productionize pipelines and infrastructure, troubleshoot models, measure impact, and mentor engineering teams.

Lead firmwide AI tool portfolio and enablement: prioritize AI initiatives, design prompts and workflows, productionize consultant-built prototypes, manage stakeholder communications, ensure governance and compliance, deliver training, and measure adoption and impact to drive AI use across consulting teams.

Integrate AI capabilities (LLMs, agent orchestration, RAG, KGs, SLMs) into production systems. Implement reliable, secure inference workflows, develop evaluation/monitoring and human-in-the-loop validation, collaborate cross-functionally, and scale autonomous agentic AI solutions. U.S. citizenship and travel up to 25% required.

Design and implement AI-driven software to improve enterprise productivity; collaborate with cross-functional teams to integrate models into production, optimize performance, and maintain scalable ML systems.

Support MLOps and DevOps initiatives across the AI Enablement team by developing microservices, building dashboards and automation for deployment and monitoring, collaborating with ML engineers and data scientists, and researching new tools to improve model development pipelines.

Design, build, and maintain production-grade AI automation workflows and LLM integrations (n8n, Python, APIs). Lead client delivery, translate business requirements into reliable systems, create reusable frameworks, and drive internal AI adoption and governance (including Microsoft 365 Copilot).

Build and maintain automation workflows and AI-assisted pipelines using n8n, APIs, webhooks, and LLM tooling. Integrate disparate systems (CRMs, ERPs, ticketing), support client delivery, create internal tools and dashboards, document solutions, and contribute reusable templates and enablement materials for the AI & Automation practice.

Design and deploy production ML/AI architectures on the Databricks platform, lead Generative AI (LLM, RAG, agent) implementations, optimize and scale MLOps pipelines, provide technical pre-sales support, and translate customer feedback into product improvements while mentoring peers and meeting delivery milestones.

Lead AI strategy and delivery: define technical direction, prototype and ship production AI features, build shared AI platform components (evaluation, retrieval, safety, observability), mentor senior engineers, and drive measurable business outcomes for early education customers.

Serve as the primary finance business partner for AI/ML and Infrastructure teams, building driver-based models, rolling forecasts, KPI dashboards, and cost models for compute, GPU, LLM API, data storage, and pipelines. Support planning, fundraising diligence, cash runway scenarios, headcount and capacity planning, and FP&A tooling integrations while partnering with Accounting, IT, and Procurement to improve cost allocation and reporting.