Top Artificial Intelligence Jobs

Develop scalable AI services spanning cloud and edge environments. Build APIs, microservices, data pipelines, and agent orchestration runtimes supporting LLM tool use, memory, and multi-step reasoning. Collaborate with researchers and infrastructure teams to deliver AI solutions, optimize real-time inference, and implement security and privacy practices. The role requires backend development experience and proficiency in Python, Java, and C++, with familiarity with databases, streaming, distributed AI, and on-device frameworks preferred.

Own and evolve supply chain data products; design scalable data models and architectures; establish governance, quality, metadata, and master data practices; and enable reporting, analytics, machine learning, and AI use cases. Partner with stakeholders, data engineers, data scientists, and product owners to translate business needs into data strategies and AI-ready solutions. Promote data product maturity, self-service analytics, architecture excellence, and business adoption.

Leads AI runtime architecture, performance optimization, benchmarking, and model execution strategies. Develops scalable software platforms, automates deployment, establishes engineering standards, and drives software architecture roadmaps. Leads cross-functional engineering teams, translates business needs into applications, supports Agile delivery, manages complex projects, and mentors engineers. The role emphasizes Linux and Windows, cloud platforms, containers, Kubernetes, programming, edge AI, telemetry, and firmware integration.

Develop and optimize machine learning software for embedded edge devices, integrating inference runtimes, AI accelerators, firmware, drivers, sensors, and host applications. Convert models into production-ready implementations optimized for latency, memory, power, and throughput. Responsibilities include quantization, pruning, benchmarking, profiling, debugging, validation, documentation, and cross-functional platform integration across PCs and connected devices.

Leads HP’s enterprise integration, API, platform-native AI, and AI governance strategy at global scale. Defines architecture vision, API-as-a-product standards, AI-ready frameworks, integration patterns, and platform AI adoption across SAP, ServiceNow, Adobe, and MuleSoft. Guides governance, security, privacy, emerging technology evaluations, and enterprise AI initiatives while influencing executives, mentoring architects and engineers, and driving scalable digital transformation capabilities.

MBA Summer Associates join consulting teams for 8–12 weeks, contributing to real client projects and developing hands-on strategy consulting experience. Responsibilities include strategic analysis, creative problem-solving, teamwork, and applying AI-based solutions and data competencies to complex business challenges. Strong performers may receive full-time offers.

Leads analytics, governance, cost optimization, and business value realization for enterprise AI platforms. Responsibilities include defining platform standards, analyzing usage and token consumption, forecasting spend and capacity, developing executive dashboards, detecting anomalies, optimizing prompts and models, and establishing budget guardrails. Partners with security, legal, finance, procurement, engineering, and business teams while mentoring staff and influencing executive decisions on AI adoption and investment.

Owns the strategy, roadmap, requirements, and delivery of enterprise Edge AI inference capabilities for LLMs and agentic workflows. Leads customer discovery, solution definition, deployment lifecycle requirements, enterprise governance, ecosystem partnerships, and go-to-market collaboration. Translates customer and workload needs into software requirements, acceptance criteria, and integrated solutions across local, on-premises, and hybrid environments. Tracks adoption, reliability, workflow effectiveness, deployment cost, and customer value.

Leads product operations and portfolio strategy for HP’s Edge and AI solutions. Establishes planning cadences, discovery frameworks, portfolio reviews, and outcome-tracking practices. Coordinates Product Management, Engineering, Design, Marketing, Sales, and field teams to align priorities, identify dependencies, synthesize customer and deployment insights, and support evidence-based decisions. Influences product direction without owning individual product roadmaps and prepares executive-ready recommendations on trade-offs, risks, and opportunities.

Leads North America Print transformation initiatives by prioritizing an enterprise backlog, redesigning workflows, reducing manual work, and driving AI, automation, and Copilot adoption. Uses data analysis, dashboards, and measurement frameworks to improve planning and executive decision-making. Manages complex cross-functional projects, aligns stakeholders, advises executives, tracks outcomes, and builds organizational capability to sustain improvements.

Lead the Product Owner organization for agentic AI experiences on HP PCs and edge devices. Define personas, experience strategy, product priorities, acceptance criteria, and adoption measures. Guide trade-offs across hardware, operating systems, AI runtimes, connected services, privacy, performance, and power. Develop product leaders, manage budgets and capacity, oversee agile delivery, evaluate shipped experiences, and communicate product decisions to executives.

Leads HP’s engineering organization delivering production-ready agentic AI experiences across PCs and edge devices. Responsibilities include roadmap execution, agentic workflow engineering, OS and hardware integration, scenario-based evaluation, automated testing, release management, risk mitigation, field issue resolution, and global leadership of engineering managers and technical teams.

Leads positioning, messaging, go-to-market strategy, launches, sales enablement, market intelligence, and thought leadership for HP AI solutions serving higher education and research. Translates complex AI infrastructure and software technologies into customer and business outcomes, influences product roadmaps, collaborates with sales, partners, and product teams, and measures pipeline, adoption, and revenue impact. Mentors marketing staff and represents HP with customers, analysts, and industry stakeholders.

Leads the design, implementation, maintenance, and optimization of scalable AI software systems for HP commercial PCs. Oversees operating system and AI application configuration, testing, performance monitoring, troubleshooting, and integration. Gathers infrastructure requirements, leads software systems engineering teams, manages development partners, reviews designs for quality and compliance, introduces new technologies, and mentors less-experienced engineers.

Lead customer-facing Edge AI solution engagements from discovery and architecture through POCs, production deployment, and scale. Design solutions using accelerated computing, AI inference, computer vision, containers, networking, and hybrid cloud. Own POC execution, productionization planning, technical troubleshooting, customer presentations, architecture documentation, and sales support. Collaborate with sales, engineering, services, partners, and enterprise customers to convert Edge AI opportunities into measurable business outcomes.

Advises enterprise clients, designs tailored Edge AI and cloud solutions, and supports strategic sales efforts with technical expertise. Responsibilities include solution architecture, scalability planning, product demonstrations, sales enablement content, market research, client feedback, complex problem-solving, and mentoring junior team members. The role partners closely with sales and internal teams to position offerings, address customer challenges, and improve products and sales strategy.

Leads customer-facing Edge AI solution engagements from discovery and architecture through POCs, production deployment, and scale. Designs solutions using accelerated computing, AI inference, computer vision, containers, networking, and hybrid cloud. Owns POC execution, productionization planning, troubleshooting, technical presentations, proposals, and bills of materials. Partners with sales, engineering, services, ISVs, and customers to translate business needs into secure, scalable architectures and measurable outcomes.

Designs and delivers secure, scalable Azure infrastructure and cloud transformation solutions for customers. Leads architecture sessions, migrations, modernization, proofs of concept, and production deployments across infrastructure, applications, data, SAP, hybrid environments, and AI workloads. Partners with technical and sales teams to identify opportunities, resolve blockers, optimize customer environments, develop reusable intellectual property, and drive adoption, resiliency, performance, and business value.

Develops, integrates, tests, and deploys autonomy, artificial intelligence, and machine learning algorithms for mission systems. Responsibilities include perception, planning, controls, model training, software development, requirements translation, human-machine interfaces, troubleshooting, debugging, and lifecycle support. The role requires applied machine learning experience, advanced education or equivalent experience, and proficiency with AI/ML frameworks and cloud tools.

Designs, builds, and evaluates complete AI systems for Pearson’s K-12 assessment platform. Responsibilities include predictive machine learning, statistical methods, retrieval, knowledge graphs, generative and agentic AI, deterministic services, human review, and system integration. The role develops evaluation datasets and regression suites, analyzes failure modes, supports model adaptation, monitors deployed systems, and collaborates with engineers, psychometricians, learning scientists, and content experts to create transparent, reliable, and auditable AI capabilities.

Design and build Pearson’s knowledge architecture for AI-powered K-12 assessment and learning systems. Responsibilities include developing knowledge graphs, ontologies, schemas, data pipelines, APIs, retrieval and context architectures, and hybrid AI integrations. The role establishes provenance, versioning, traceability, quality measures, and responsible AI practices while partnering with engineers, AI scientists, psychometricians, learning scientists, and content experts. It requires autonomous innovation, rapid prototyping, rigorous evaluation, and translating educational and assessment expertise into computational representations.

Leads enterprise AI deployments embedded with client organizations, from architecture and platform integration through production deployment, governance, adoption, scalability, and ROI. Designs agentic AI solutions, LLM workflows, APIs, orchestration, RAG systems, and enterprise integrations. Advises CTO, CFO, and CISO stakeholders, leads executive workshops, defines AI roadmaps and standards, and develops reusable deployment frameworks while managing engineering teams and growing the forward-deployed engineering practice.

Leads complex, AI-driven sales transformation projects from planning through implementation. Coordinates multiple workstreams, manages risks, dependencies, budgets, and stakeholders, develops financial forecasts and analytical frameworks, uses AI-enabled tools for insights, and supports organizational change and long-term adoption.

Build and maintain large-scale machine learning infrastructure, data pipelines, analytics platforms, and distributed systems in AWS. Develop and scale model training and deployment frameworks, implement ETL and real-time data processing, and create CI/CD and observability solutions. Research reinforcement learning algorithms, including Monte Carlo Tree Search, and validate multi-agent models using simulation platforms. Partner with engineering and business teams, advise management on technical strategy, coordinate complex projects, and mentor junior engineers.

Own and scale paid search and emerging AI advertising across Google Ads, Microsoft Advertising, and ChatGPT. Responsibilities include campaign strategy and execution, budget allocation, keyword and bidding optimization, landing page development, A/B testing, conversion-rate optimization, performance measurement, attribution, and pipeline growth. The role partners with Product Marketing, Web, Design, Marketing Operations, and Demand Generation while connecting advertising performance to qualified leads, opportunities, revenue, and downstream pipeline.