Top Artificial Intelligence Jobs

Lead technology risk and controls for AI and broader tech environments: identify, quantify, and mitigate compliance and operational risks; provide SME guidance to technologists; use enterprise AI to synthesize evidence and reporting; govern controls, monitor effectiveness, recommend enhancements, and influence executive decision-making.

Own, build, and optimize Spark-based production data pipelines and ML data lake to support recommender systems and personalization. Scale cluster configurations on GCP Dataproc, ensure data quality and accessibility, support ML engineers for model development, resolve performance bottlenecks, and collaborate on platform architecture to deliver reliable data products that drive business value.

Lead design and delivery of reliable, scalable AI/ML platform services. Build production-grade code, reusable APIs/SDKs, observability and resilience tooling, define reliability standards, support on-call incident resolution, and mentor engineers to enable secure, compliant generative AI use cases.

Lead design, procurement, and scaling of on-premise AI infrastructure (compute, storage, networking, accelerators) to support large-scale training and low-latency inference. Own roadmap, run RFIs/RFPs, set infrastructure standards, collaborate with data science/MLOps and cross-functional stakeholders, and influence executive strategy for enterprise AI platform capabilities.

Lead design, procurement, and scaling of Mastercard's on‑premise AI infrastructure to support large‑scale training and low‑latency inference. Define platform standards (compute, storage, networking, cooling), run RFIs/RFPs, partner with data science/MLOps/platform/security/governance, and influence senior leadership on roadmap, vendor selection, and long‑term AI infrastructure strategy.

Lead design and delivery of Mastercard's on-premise AI infrastructure roadmap. Architect and scale compute, storage, networking, and accelerators for large-scale training and low-latency inference. Own RFI/RFP processes, partner with data science, MLOps, security, and enterprise teams, and influence executive stakeholders to drive strategic, compliant, and cost-effective AI platform decisions.

Lead design and delivery of Mastercard's on-premise AI infrastructure roadmap: architect CPU/GPU/accelerator compute, high-performance storage and networking, data center power and cooling, run RFIs/RFPs, align infrastructure with MLOps and model lifecycle needs, collaborate with cross-functional stakeholders, and evaluate emerging AI infrastructure technologies to support large-scale training and low-latency inference.

Lead design, procurement, and scaling of on‑premise AI infrastructure (compute, storage, networking, accelerators). Act as product owner translating data science needs into enterprise platforms, run RFI/RFPs, partner with MLOps, security, and governance, and influence executive stakeholders to enable large‑scale training and low‑latency inference for generative and predictive AI.

Lead design, roadmap, procurement, and scaling of Mastercard's on‑premise AI infrastructure. Architect compute, GPU, storage, and networking for training and low‑latency inference at petabyte scale; run RFIs/RFPs; define data center and cooling requirements; partner with data science, MLOps, security, and governance; influence executives and evaluate emerging AI infrastructure technologies.

Lead design, procurement, and lifecycle management of on-prem AI infrastructure. Act as product owner translating data science needs into scalable compute, storage, networking, and accelerator platforms. Drive RFI/RFPs, architect high-performance systems for large-scale training and low-latency inference, define infrastructure standards, partner with MLOps/data science/security/governance, influence executives, and evaluate emerging AI infrastructure technologies.

The Principal Engineer, AI/ML Architecture will define the technical direction for Coupa's AI platform, focusing on model training, evaluation, safety standards, and mentoring a team of ML and data engineers.

Lead design and delivery of AI-driven product solutions across the commerce platform. Own high- and low-level architecture, implement scalable RAG/LLM systems, select AI infrastructure, mentor teams, and collaborate with product and engineering leadership to execute AI strategy and rapid experimentation.

Lead technical projects and design, build, and deploy production-quality AI/ML systems, pipelines, and LLM-based agent platforms. Explore data, develop AI solutions, advise partners on proposals, select appropriate modeling approaches, establish evaluation and observability frameworks, optimize system performance and cost, and guide proprietary AI/ML infrastructure development. Engage directly with clients across industries while promoting best practices, responsible AI governance, and robust engineering standards.

Build and ship LLM- and agent-based prototypes and production solutions. Develop traditional ML where appropriate, prioritize reuse and scalability, collaborate with product, platform, and frontend partners, and support business development with technical credibility.

Support AI capability and adoption through structured prompt experimentation, custom GPT testing, workflow prototyping, documentation of exemplars, and coaching colleagues to scale responsible AI use across the firm.

Partner with enterprise finance customers to analyze workflows, identify AI and automation opportunities, define solution requirements, and support implementation. Design deployment plans, integrations, governance, model and prompt evaluations, change management, testing, and rollouts. Synthesize customer insights into scalable product opportunities, communicate impact to executives, and collaborate with Product, Engineering, and go-to-market teams.

Build and integrate LLM-based agents, coding tools, model infrastructure, and role-specific AI interfaces for engineers, quants, and traders. Develop inference, configuration, integrations, tracing, and access controls; improve system reliability and performance; prototype applications for research, simulation, and trading; and collaborate with users to prioritize solutions.

Leads enterprise AI strategy, portfolio management, governance, lifecycle oversight, financial management, and value measurement. Advises teams on AI technology selection, including foundation models, agents, RAG architectures, orchestration platforms, and evaluation tools. Establishes responsible AI processes, approval workflows, model governance frameworks, executive dashboards, and investment prioritization methods. Partners with FinOps, Procurement, Enterprise Architecture, Technology, and business leaders to optimize AI spending, manage risk, drive responsible adoption, and realize measurable business value through influence and cross-functional leadership.

Lead AI engineering efforts from model development through production deployment. Optimize inference latency, throughput, GPU utilization, quantization, and serving costs; design evaluation systems and data flywheels using user feedback. Apply LLMs and multimodal models to video understanding, curation, ranking, personalization, and editing. Set technical roadmaps, review architecture, mentor engineers, manage a small AI team, and balance quality, reliability, latency, and infrastructure cost.

Lead development and execution of workforce strategies for advanced manufacturing. Build industry and education partnerships, design training and credential programs, integrate emerging technologies, manage grants and business development, and lead teams to implement and evaluate workforce initiatives that strengthen the manufacturing talent pipeline.

Support defense business development and acquisition planning by conducting market and opportunity research, analyzing contract vehicles and timelines, assisting proposal and capture activities, and supporting early-stage program management tasks (schedule, cost, risk). Requires translating military acquisition experience to a commercial startup environment, producing briefings and decision materials, and maintaining security-clearance eligibility with up to 20% travel.

Design and produce interactive client presentations in DIGIDECK, including slide layouts, infographics, visualizations, motion graphics, animations, and custom creative elements. Use Claude and other AI tools for ideation, brief analysis, copy refinement, content synthesis, and workflow acceleration. Collaborate with brand strategy teams, maintain design systems and brand standards, manage feedback and multiple timelines, and deliver polished presentation experiences using Adobe Creative Suite and cloud-based presentation tools.

Build and own full-stack product features for an AI-powered biopharma documentation platform. Develop React and TypeScript interfaces, backend services, APIs, data models, and production deployments. Design scalable systems for complex enterprise workflows, create AI-native scientific and regulatory content experiences, collaborate cross-functionally, and make pragmatic architectural decisions in a fast-paced startup environment.

Lead development, synchronization, and staffing of Offensive Cyberspace Operations (OCO) plans supporting USCYBERCOM. Drive targeting, integrate OCO/DCO, draft OPLANS/OPORDs/FRAGOs, support Joint planning teams, coordinate intelligence and capabilities, and provide situational awareness and analytic products. Communicate complex technical concepts and work across DoD/IC partners to support cyber warfare operations.

Drives feature-level strategy, requirements, roadmap planning, execution, and deployment for C3 Generative AI products. Collaborates with engineering, data science, marketing, sales, and customers to translate technical and market needs into product specifications and software capabilities. Conducts customer scoping sessions, demos, and analyst presentations, while supporting marketing collateral and feature releases.