Top Artificial Intelligence Jobs

Conduct applied AI research and build foundation models from design through training, evaluation, validation, and production implementation. Collaborate with data scientists, engineers, and product managers to deliver AI-powered products at scale. Research emerging methods, optimize large deep learning models, translate technical findings into business outcomes, and contribute to technical reports and conference publications. The role requires advanced expertise in foundation models, deep learning, optimization, NLP, or fine-tuning.

Conduct applied AI research and build foundation models across design, training, evaluation, validation, and production implementation. Collaborate with data scientists, software and machine learning engineers, and product managers to deliver scalable AI products. Research LLMs, optimization, fine-tuning, and agentic AI; publish findings through technical reports and conference submissions; and independently pursue impactful research agendas.

Lead the strategy, vision, and product team for enterprise AI/ML platforms supporting model training, model serving, and generative or agentic AI applications. Partner with platform leaders to build integrated capabilities, improve user experiences for data scientists and developers, drive adoption, and deliver business value. The role requires technical product leadership, AI/ML expertise, cross-functional influence, and people management.

Leads product management for machine learning and AI capabilities, defining customer-focused problem statements, prioritizing infrastructure and model delivery roadmaps, establishing success metrics, and guiding technology design. Partners with data science and engineering teams, facilitates agile delivery, communicates long-term vision, manages risks, recruits talent, and mentors product associates.

Leads data science efforts for foundational AI and automation across a global payments network. Partners with data scientists, engineers, and product managers to develop scalable machine learning engines, AI agents, merchant intelligence, and risk solutions. Serves as an NLP and LLM domain expert, fine-tuning and deploying models using PyTorch, AWS, vector databases, and modern AI technologies. Translates complex ML work into business value while mentoring teams and delivering production-scale platforms and solutions.

Build and operate foundational AI systems, including foundation-model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Lead architecture, optimization, reliability, and deployment of scalable AI platforms; improve GPU/TPU utilization, latency, throughput, and cost. Partner across engineering, research, product, and infrastructure teams, guide technical strategy, conduct deployment reviews, and mentor senior technical staff.

Own the agentic analytics platform for Capital One Shopping’s AI Enablement team. Partner with engineers to build trusted AI products, govern enterprise data and metrics, define agent boundaries, validate system performance, prioritize stakeholder needs, and improve deployment efficiency. The role requires strong product leadership, data platform expertise, SQL proficiency, familiarity with modern data tools and AI agents, executive communication, and experience operating internal platforms or production deployments.

Build and deploy scalable generative AI platform services, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, reliability, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor senior technical staff.

Lead a product team building ML and GenAI infrastructure and model delivery for marketing and acquisitions. Define problem statements, set 3-year roadmaps tied to business outcomes, partner with data scientists and engineers, drive reusable scalable capabilities, influence cross-functional stakeholders, recruit and mentor talent, and measure success with data-driven metrics.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead orchestration, cost-performance governance, technical standards, design reviews, and roadmap development. Apply advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and cost efficiency while mentoring senior technical staff and ensuring responsible AI practices.

Leads the design, development, deployment, and operation of enterprise AI and machine learning solutions, including generative AI, LLMs, conversational AI, predictive analytics, RAG, and agentic systems. Provides technical direction, mentors engineers, establishes responsible AI governance and model lifecycle practices, evaluates cloud AI services, and aligns initiatives with enterprise strategy. Ensures production quality, operational excellence, regulatory compliance, and delivery of healthcare-focused AI platforms.

Leads the vision, architecture, and delivery of AI/ML solutions for prior authorization. Responsibilities include building scalable AI platforms, deploying LLM, RAG, and agentic AI solutions, establishing model governance and monitoring, ensuring healthcare regulatory compliance, and mentoring technical leaders. The role owns delivery from experimentation through production support and drives AI engineering standards, operating models, and strategic roadmaps.

Leads the AI-first engineering vision and roadmap for an enterprise platform. Drives AI-enabled SDLC adoption, generative and agentic AI implementation, intelligent automation, governance, responsible AI controls, model evaluation, security guardrails, and DevSecOps automation. Mentors engineering teams on prompt engineering, RAG, agent frameworks, observability, and AI architecture while serving as a senior technical escalation point for cloud, data, application, and production issues.

Manage AI and data science projects from planning through implementation, coordinating technical and non-technical teams, dependencies, Agile ceremonies, requirements, budgets, timelines, scope, and stakeholder expectations. Translate AI capabilities into actionable plans, navigate ambiguity, and drive accountability across multiple teams and priorities.

Lead Wafer’s talent function by sourcing, evaluating, and closing exceptional engineers and operators. Own priority searches end to end, partner with founders and hiring managers, improve recruiting processes and data quality in Ashby, manage recruiting partners, and build the talent team and systems needed for company growth.

Lead design and deliver AI-native, cloud-native software components; develop secure high-quality production code; drive adoption of AI-assisted engineering practices, CI/CD automation, SDKs and reusable frameworks; mentor engineers and lead cross-team communities of practice.

Own the product strategy and production delivery of shared AI services and infrastructure across 2K. Drive opportunity intake, prioritization, build-versus-buy decisions, roadmap communication, governance, adoption, reliability, cost, latency, and output quality. Partner with engineering, data science, security, trust and safety, and senior leadership to deliver scalable AI capabilities with auditable compliance and measurable business value.

Drives revenue and profitability for Cisco AI infrastructure products by managing pricing, promotions, positioning, vendor relationships, SKUs, e-commerce merchandising, forecasts, and marketing plans. Serves as the product portfolio subject matter expert, identifies emerging technologies, manages vendor co-op funding, analyzes market opportunities, and develops annual business plans while coordinating with segment marketing and internal stakeholders.

Own partner-facing product capabilities from customer discovery and experimentation through launch, measurement, and iteration. Develop product strategy, requirements, success metrics, and AI-powered experiences including agents, recommendations, automation, and intelligent workflows. Collaborate with Engineering, Design, Data Science, Marketing, Sales, Support, and partner teams to evaluate tradeoffs, improve usability and trust, and deliver measurable customer and business outcomes.

Lead the technical direction, architecture, and development of Compass, an LLM-powered agent platform. Design multi-agent systems, MCP and A2A integrations, orchestration, evaluation, and observability infrastructure. Drive cross-team technical projects, production support, engineering quality, mentoring, and adoption of AI-assisted development practices. Partner with Product, QA, Design, Operations, and Engineering stakeholders to deliver reliable agentic systems.

Build and deploy production-grade AI applications across front-end, back-end, and data workflows. Translate customer problems into product requirements, integrate AI models, optimize application performance and reliability, evaluate AI outputs, and iterate using customer feedback. The role supports impactful projects for government, healthcare, and industrial customers, with ownership across the full product lifecycle.

Manage strategic partnerships with cloud providers, AI labs, inference providers, and data partners. Responsibilities include commercial negotiations, partner business reviews, co-selling, marketplace activation, joint marketing, co-development, pipeline reporting, and building scalable partnership processes. The role requires coordinating across Sales, Product, Research, Marketing, and Finance while influencing revenue through partner channels and supporting executive-level engagement.

Supports manufacturing performance improvement through process optimization, automation, reliability initiatives, Lean manufacturing, analytics, and continuous improvement. Analyzes OEE, downtime, quality, waste, labor, and capacity data; leads root-cause investigations and improvement projects; supports SCADA, MES, CMMS, Power BI, and other manufacturing systems; participates in commissioning and validation; develops dashboards and documentation; and coaches frontline teams on Lean practices.

Leads enterprise transformation across strategic business groups and corporate functions. Translates strategic priorities into executable roadmaps, establishes governance and performance mechanisms, removes execution barriers, and drives adoption and measurable value realization. Partners with executives and matrixed teams to improve processes, operating models, productivity, and organizational effectiveness. Builds sustainable transformation capabilities through change leadership, stakeholder engagement, digital enablement, analytics, automation, and AI.

Leads executive operations, strategic planning, leadership forums, reporting, VIP visitor programs, and cross-functional initiatives. Develops workflows, dashboards, governance resources, training, and change-management strategies to improve organizational alignment, scalability, and execution. Synthesizes complex strategic and operational information into executive-ready narratives and deliverables while coordinating stakeholders and managing sensitive information.