Top Artificial Intelligence Jobs

Lead competitive strategy for Microsoft's AI and cloud infrastructure products, evaluating accelerators, CPUs, memory, networking, storage, and rack-scale systems. Analyze technology roadmaps, performance, power, cost, supply, utilization, and total cost of ownership. Partner with architecture, engineering, finance, sourcing, and product teams to develop data-driven recommendations, competitive assessments, dashboards, and executive-level insights influencing Azure product roadmaps and investment decisions.

Develop and optimize GPU kernels, compilers, runtimes, and distributed inference systems for large language model serving. Improve latency, throughput, reliability, and hardware efficiency through profiling and low-level performance optimization. Integrate scalable improvements into production systems, collaborate across model, infrastructure, compiler, and hardware teams, and mentor engineers. Principal-level contributors additionally lead cross-stack initiatives and create reusable performance capabilities across models and hardware generations.

Design and develop high-scale distributed AI infrastructure services supporting model training and inference on Azure Machine Learning. Build backend services using C#, Go, Rust, and C++ on Kubernetes and Docker. Operate reliable, secure, low-latency systems with 24/7 availability, provide operational ownership, collaborate with engineers and researchers, and provide technical leadership.

Design, build, and operate scalable cloud platform services for deploying, monitoring, governing, and managing AI models and workloads. Lead architecture, reliability, security, observability, automation, and developer self-service initiatives using distributed systems and SRE practices. Collaborate across engineering, product, and research teams, analyze production performance, drive continuous improvement, and mentor engineers while influencing technical strategy.

Lead a development pod to deliver AI-enabled eGRC solutions on ServiceNow IRM/GRC and NowAssist. Maintain prioritized backlog, translate complex business requirements into product increments, align stakeholders, manage delivery, monitor performance, and ensure regulatory and risk alignment while promoting Agile practices and continuous improvement.

Conduct intelligence analysis and technology assessments focused on remote sensing, electronic warfare, radar, and emerging military technologies. Identify and prioritize promising technologies, evaluate foreign research using open and classified sources, synthesize findings into reports, and identify relevant conferences and research actors. Collaborate with government, military, contractor, and technical personnel on science, technology, and innovation problems.

Develop graduate-level Applied AI course materials, including lessons, labs, case studies, assignments, rubrics, projects, readings, templates, and glossaries. Design practical exercises using LLMs, RAG, AI agents, APIs, automation, and evaluation tools. Collaborate with learning designers and university faculty, revise content based on academic review, advise on Applied AI topics, and maintain current coverage of evolving tools and regulations.

Leads enterprise data and AI engineering initiatives by developing Databricks applications, scalable ETL pipelines, AI and machine learning solutions, and MLOps capabilities. Integrates Snowflake, Azure, ADLS, APIs, and other platforms while ensuring security, governance, reliability, and responsible AI practices. Partners with business and technical stakeholders to deliver production-grade solutions and troubleshoot complex data and AI workflows.

Leads the HPAC product management organization, owning product strategy, portfolio roadmap, lifecycle, pricing, profitability, and market growth. Builds and develops a global product management team, establishes scalable processes, drives product launches and documentation, and aligns engineering, marketing, sales, and executive stakeholders. The role focuses on high-performance power solutions for AI, cloud, data center, server, telecom, networking, and emerging computing markets.

Leads global strategy, business development, customer engagement, product portfolio direction, and revenue growth for Renesas’ hyperscaler and emerging AI segment. Builds relationships with cloud providers and AI companies, identifies market opportunities, drives design wins, translates customer needs into product roadmaps, and coordinates Sales, Applications, Engineering, Marketing, Product Management, and Operations teams. Presents strategy and business outlook to senior leadership while influencing global teams across functions and regions.

Two-year early-career rotational program with three cross-functional assignments focused on data engineering and analytics. Participants create analytical reports, work with machine learning and artificial intelligence, lead intensive business projects, and develop technical, interpersonal, and leadership skills. The program provides exposure to the full data lifecycle, business strategy, and continuous improvement, with potential placement into a post-program role.

Leads global organizational development and learning strategy, partnering with executives, business leaders, and HR to design leadership, technical, management, succession, talent, and performance programs. Oversees program planning, delivery, evaluation, and continuous improvement while using AI, emerging technologies, and analytics to strengthen workforce capabilities and inform talent decisions. Responsibilities include executive coaching, change management, career development, cultural alignment, and organizational effectiveness initiatives.

Build production LLM systems and agentic workflows for enterprise process automation. Develop context-learning libraries, autonomous knowledge-management agents, continuous learning loops, evaluation and confidence systems, and scalable AI orchestration. The role combines applied research with production engineering, requiring hypothesis-driven experimentation, reliability improvements, and contributions to AI strategy and product capabilities.

Lead the design, development, and deployment of enterprise Generative AI solutions. Architect LLM, agentic AI, RAG, orchestration, and cloud-native workflows across AWS, GCP, and Snowflake. Establish prompt governance, guardrails, evaluation frameworks, benchmarking, observability, and optimization practices. Partner with stakeholders to prototype AI solutions and convert them into scalable, secure, production-ready applications.

Own the roadmap and evolution of AI and automation products, including creative management, internal AI agents, and advertising intelligence tools. Lead user discovery, prioritize requirements, manage backlogs, coordinate engineering delivery, measure outcomes, and drive adoption through onboarding and training. Partner with marketing, creative, operations, data, product, engineering, and leadership teams across the US and Europe to deliver measurable workflow and campaign improvements.

Leads AI-enabled product data strategy and digital commerce operations, including data governance, taxonomy, catalog lifecycle management, publishing readiness, promotions, store listings, fulfillment accuracy, and return reduction. Manages two item data specialists while partnering with merchandising, marketing, technology, fulfillment, analytics, and store teams. Establishes standards, metrics, workflows, documentation, and continuous improvement initiatives that improve catalog quality, automation readiness, operational efficiency, and customer experience.

Design, build, deploy, and support enterprise generative AI products using LLMs, RAG, agentic workflows, Python, Azure, and cloud-native technologies. Develop APIs, vector search solutions, evaluation and observability systems, and production services on AKS. Translate business needs into scalable product capabilities while balancing quality, latency, cost, security, and reliability. Collaborate with product, architecture, infrastructure, database, operations, and engineering teams on delivery, troubleshooting, incident response, and ongoing enhancements.

Leads the design, development, and delivery of production AI systems, including agentic workflows, RAG architectures, backend services, evaluations, and orchestration infrastructure. Provides hands-on technical leadership, architectural direction, mentorship, code reviews, and engineering standards improvements. Partners with product and engineering stakeholders to translate ambiguous requirements into reliable, observable, scalable, and maintainable AI solutions.

Leads BILL’s AI and platform product partnership strategy, managing relationships with frontier AI companies and software vendors. Sources and structures deals, negotiates agreements, drives integrations and connector approvals, develops go-to-market and monetization plans, and coordinates execution across product, engineering, sales, marketing, finance, and executive leadership. Builds partnership roadmaps, evaluates opportunities, models deal economics, and creates scalable processes for a growing ecosystem.

Leads large-scale enterprise application development, modernization, and sustainment programs. Oversees Agile teams, program governance, schedules, budgets, risks, resources, stakeholder communications, cloud modernization, DevSecOps, AI, user adoption, and cybersecurity compliance. The role coordinates technical and business stakeholders, manages Appian and Google Cloud initiatives, drives continuous improvement, and ensures delivery quality, security, performance, and alignment with government objectives.

Own the Central region enterprise advertising territory, developing new business and expanding existing relationships with brands and agencies, especially in Retail, QSR, and CPG. Manage the full sales cycle, build executive relationships, create strategic proposals, maintain pipeline and forecasts, navigate complex buying processes, and achieve revenue goals. Collaborate cross-functionally to deliver successful campaigns, provide strategic marketing advice, and share customer insights that inform product and go-to-market strategy.

Leads the design, implementation, and production deployment of AI-powered solutions across technology teams. Responsibilities include architecture, LLM and agent workflow development, observability, telemetry, evaluation frameworks, guardrails, governance, reliability, cloud integration, and operational readiness. The role evaluates AI technologies, defines reusable patterns, partners with engineering and business stakeholders, supports scalable AI infrastructure, communicates technical decisions, and mentors engineers.

Engineer agentic AI and automation integrations connecting planning systems with laboratory instruments, robotic platforms, ELN/LIMS, and data pipelines. Build multi-agent workflows with orchestration, state management, tool integration, error recovery, and human oversight. Deploy containerized services using Docker, Kubernetes, Argo, GitOps, and CI/CD. Collaborate with scientists, automation engineers, and NVIDIA researchers to transition prototypes into reliable autonomous experimentation systems for drug discovery.

Lead infrastructure and platform engineering teams, oversee capacity, schedules, risks, governance, and delivery quality. Design secure, scalable AWS architectures and external integrations; translate business needs into technical roadmaps and implementation plans. Establish standards for data integration, configuration, compliance, and auditability. Guide Agentic Audit technology evolution, evaluate emerging technologies, conduct technical and code reviews, and support platform deployments in a DevSecOps environment.

Designs conceptual, logical, and physical data models for R&D research and clinical domains. Translates scientific and business requirements into reusable, scalable, AI-ready data structures that support analytics, machine learning, data integration, and standardized workflows. Partners with domain experts, data engineers, architects, data scientists, and governance teams to apply enterprise standards, schemas, ontologies, metadata, quality rules, and lineage. Promotes shared data assets and ensures models are implementable, interoperable, and aligned with evolving R&D needs.