Top Artificial Intelligence Jobs

Leads Microsoft’s AI Workforce sales business across state and local government, education, academic medical centers, and software customers. Owns strategy, revenue growth, pipeline, forecasting, competitive execution, and customer adoption. Develops sales leaders and specialist teams, builds executive relationships, orchestrates Microsoft and partner resources, and drives scaled adoption of Copilot, AI agents, and cloud platforms. Establishes operating rhythms, strengthens competitive positioning, and translates AI transformation opportunities into measurable customer and business outcomes.

Owns end-to-end architecture and delivery of AI back-end networks across scale-up and scale-out systems. Leads requirements, silicon and hardware integration, optical and electrical links, protocols, performance modeling, validation, manufacturing readiness, deployment, fleet operations, and reliability. Connects networking architecture with distributed AI workloads and collective communication while coordinating engineering, suppliers, manufacturing, and datacenter teams.

Lead the vision, architecture, and hands-on development of Epiq’s data and AI platforms. Manage a distributed team of 80–100 engineers, establish software engineering standards, and deliver scalable cloud-native services, data pipelines, governance, quality, and AI-ready foundations. Partner with Product, AI/ML, Analytics, and business stakeholders on APIs, event-driven systems, data integration, observability, model operations, GenAI, vector search, and responsible data practices.

Build local AI applications and developer tools across Windows, Linux, and macOS. Develop intuitive workflows for connecting devices, running AI models locally, and deploying AI agents. Collaborate with designers, product managers, and engineers to create unified experiences that leverage accelerated hardware. The role requires strong Go development, extensive experience building APIs, CLIs, or applications, and familiarity with agent-driven software development, inference engines, and local AI platforms.

Build and operate secure, scalable AI and data platforms across multi-cloud environments. Develop production services, APIs, automation, AI agents, self-service capabilities, and observability. Improve platform resilience, reliability, governance, responsible AI controls, and operational efficiency while supporting teams in moving AI use cases into production. Deliver reporting on usage, cost, performance, adoption, and business value, and promote reusable engineering standards and knowledge sharing.

Build and manage B2B sales pipeline for private AI infrastructure and agentic systems. Research accounts, prospect through email, calls, networks, and events, conduct discovery, explain AI solutions, collaborate with engineers on demos and proposals, maintain CRM records, progress deals, and support negotiations and commercial playbook development.

Own the SentinelAgent clinical trial AI platform from early pilots through validated, multi-sponsor deployment. Responsibilities include product requirements, backlog and roadmap management, engineering coordination, regulated integrations, GxP validation, AI governance, privacy and security controls, release gates, auditability, and performance instrumentation. The role requires extensive eClinical product management experience, clinical systems integration literacy, knowledge of global regulations, and experience shipping locked-model AI products in validated environments.

Own end-to-end go-to-market execution, from offer development and positioning through landing pages, campaigns, outbound systems, experimentation, and conversion optimization. Use AI tools to create content, automate workflows, personalize outreach, and accelerate launches. Analyze funnel performance, drive sales and business development, refine product-market fit, build customer relationships, and productize services while scaling new brands or offerings globally.

Bridges business, operations, product, and software teams by eliciting requirements, documenting data flows, shaping technical solutions, and advancing data- and AI-enabled initiatives. Extracts insights from complex datasets, supports architecture decisions, manages dependencies and change control, promotes responsible AI-assisted engineering practices, and drives continuous process improvement. Guides team members while communicating complex technical information clearly to diverse stakeholders.

Lead the technical side of sales cycles across the Americas by qualifying opportunities, conducting technical discovery, developing customer engagement strategies, delivering customized quantum computing demos, and identifying technical opportunities. Explain complex quantum concepts to diverse audiences, provide customer guidance, influence product direction through field feedback, and maintain reusable implementation assets and proof-of-concept templates.

Architects end-to-end HPC and AI solutions for federal customers, serving as the primary technical interface throughout the sales cycle. Responsibilities include product presentations, system architecture, RFI/RFP responses, demonstrations, proofs of concept, consultative selling, partner coordination, account planning, and customer escalation support. The role requires expertise in HPC, GPUs, networking, AI infrastructure, solution configuration, business value modeling, and technical sales, with mentoring responsibilities for other solution architects.

Build production AI and machine learning software for large-scale computing and AI infrastructure. Responsibilities include developing anomaly detection, forecasting, root cause analysis, LLM workflows, agentic systems, and operational analytics using Go, Python, and React. The role spans data engineering, model development, evaluation, deployment, monitoring, and continuous improvement while collaborating with engineering, architecture, product, and customer teams.

Leads AI/ML infrastructure networking initiatives by benchmarking switches, validating ASIC and fabric features, and guiding workload deployments. Develops AI-driven network automation using Python, Ansible, SONiC, Junos, and related tools. Tests protocols, congestion behavior, traffic distribution, resiliency, and high-performance network operations. Documents architectures, methodologies, configurations, results, and recommendations for scalable AI/ML network fabrics.

Develop AI data infrastructure, metadata and lineage systems, foundation models, agentic AI platforms, and knowledge-infused learning capabilities. Build and evaluate NLP, LLM, time-series, diffusion, and computer vision systems while improving reproducibility, governance, reasoning, scalability, and trustworthy AI outcomes. Partner with software teams and external collaborators to translate innovative research into production implementations.

Lead AI solutions development in finance using statistical methods and machine learning. Mentor, implement models, ensure compliance, and collaborate for effective tool integration.

Partners with AI engineering leaders to improve organizational effectiveness, leadership capability, workforce readiness, and change management. Coaches leaders, advises on organizational design, talent, succession, culture, and high-stakes decisions. Uses HR data, diagnostics, talent processes, and AI insights to develop scalable people solutions, strengthen leadership teams, and support strategy execution.

Leads strategic enterprise engagements to identify AI and Microsoft 365 Copilot use cases, demonstrate technical solutions, quantify business value, and accelerate adoption. Delivers workshops, sales presentations, and proofs of concept; addresses technical challenges; guides sales teams through competitive deal cycles; collaborates with partners and internal teams; and shares customer feedback to influence product, marketing, and support strategies.

Supports AI Engineers in designing, developing, testing, validating, documenting, and deploying AI-driven solutions using Microsoft's Azure AI ecosystem. The intern will help build agentic and automation solutions, collaborate with business stakeholders to identify automation opportunities, contribute to production testing, and maintain internal knowledge resources. The role offers hands-on experience with frontier AI technologies and enterprise production systems.

Lead design and implementation of security frameworks for AI systems, performing threat modeling, adversarial defense, secure MLOps, architecture reviews (including MCP and agentic systems), authoring standards and reference architectures, managing large AI security projects, and mentoring cross-functional teams to strengthen AI security posture.

Leads enterprise AI platform governance and operations, overseeing Microsoft AI and collaboration platforms, governance frameworks, controls, lifecycle management, integrations, monitoring, auditability, licensing, budgets, and cost optimization. Translates cybersecurity, privacy, data governance, legal, and compliance requirements into platform controls. Manages platform engineers and vendors, evaluates changes and business requests, and communicates operational, risk, adoption, and performance insights to stakeholders.

Lead the architecture, development, and production deployment of advanced AI systems for risk management and compliance. Design agentic frameworks, LangGraph workflows, distributed memory infrastructure, model-serving platforms, PEFT optimization, and AI observability solutions. Mentor engineers, establish technical strategy, collaborate across teams, and evaluate emerging GenAI, LLM, and NLP technologies. The role also requires integrating AI APIs and microservices with banking applications while ensuring scalability, reliability, compliance, and operational excellence.

Design and implement compiler transformations, distributed runtimes, pipeline parallelism, sharding, and communication-computation overlap for large-scale deep learning training on GPU clusters. Develop JAX and XLA integrations, optimize compiler and runtime performance, support open-source JaxPP, collaborate with framework and hardware teams, and debug large training workloads.

Serve as a strategic advisor to divisional leaders, shaping AI transformation and process optimization portfolios. Identify opportunities, develop business cases and ROI frameworks, evaluate emerging AI technologies, lead proof-of-concepts, establish governance, coordinate cross-functional teams, and create transformation roadmaps. The role also involves executive communication, stakeholder influence, training, workshops, and translating AI capabilities into measurable business outcomes across complex organizations.

Designs, builds, debugs, and optimizes scalable AI systems, including inference infrastructure, APIs, distributed services, and platform components. The role delivers secure, reliable, high-performance AI experiences across audio, image, video, and safety applications. Responsibilities include full-stack development, cross-team collaboration, architectural innovation, rapid issue resolution, and end-to-end feature delivery for systems serving millions of customers.

Leads Cloudflare’s growth sales segment targeting AI and startup accounts. Owns revenue acquisition and expansion, recruits and develops a high-performing sales team, positions Cloudflare’s infrastructure and developer products, converts self-serve usage into enterprise agreements, and builds executive relationships with startups, founders, and venture capital partners.