Top Artificial Intelligence Jobs

Manage channel marketing programs, dealer initiatives, marketplace operations, loyalty programs, and cross-functional projects across Jacuzzi Group brands. Analyze KPIs and program data, maintain dashboards, support sales enablement, coordinate vendors, manage invoices and budgets, and provide data-driven recommendations. The role partners with Sales, Marketing, Finance, IT, dealers, and vendors to improve program adoption and effectiveness.

Lead architecture and hands-on development of Cisco’s cloud-native data platform, including distributed pipelines, lakehouse systems, backend services, APIs, and real-time analytics infrastructure. Establish standards for data modeling, streaming, observability, governance, and CI/CD. Mentor data engineers, conduct technical reviews, coordinate cross-team dependencies, and deliver AI-ready data foundations supporting predictive and generative AI applications.

Leads architecture and delivery across device, mobile app, and cloud systems. Sets technical direction, drives cross-team initiatives, writes production code, guides migrations, resolves complex issues, and improves reliability, observability, security, cost, and developer workflows. Mentors senior and Staff engineers, establishes scalable technical decision practices, and partners with data science, AI, and platform teams on production integrations.

Leads the strategy, development, optimization, and expansion of growth products supporting customer acquisition, lead generation, conversion, monetization, and marketplace efficiency. Partners with Engineering, Marketing, Analytics, Revenue Operations, and Partnerships to identify opportunities, build business cases, execute experiments, establish KPIs, and deliver measurable revenue and performance improvements through products, automation, AI, and emerging technologies.

Leads strategic customer relationships and global cross-functional programs for AI and data center infrastructure customers. Translates customer requirements into product specifications, business cases, supply chain plans, and product roadmaps. Oversees RFQ through NPI, qualification, certification, and mass production while managing commercial negotiations, capacity, risk, revenue, margin, and ROI. Coordinates engineering, manufacturing, quality, procurement, supply chain, and finance teams across multiple countries without direct authority.

Build and deploy production agentic workflows that transform recurring business processes across company functions. Responsibilities include mapping workflows, evaluating AI opportunities, developing agents on a shared framework, defining approval gates, improving reliability and evaluation, preparing data and systems for agent use, training functional teams, and documenting reusable implementation patterns. The role requires close collaboration with non-engineers, senior stakeholders, and domain experts, plus strong ownership through handoff and ongoing platform improvement.

Leads enterprise architecture across data, analytics, AI/ML, Generative AI, cloud platforms, integration, and automation. Defines target-state architectures, technology roadmaps, governance standards, and architecture guardrails; evaluates platforms and vendors; guides modernization and cloud transformation; and advises senior leadership. Partners across business, engineering, cybersecurity, and infrastructure teams to deliver scalable solutions involving AI platforms, agent frameworks, RAG, knowledge platforms, APIs, and event-driven architectures. Mentors technical teams and drives responsible AI, security, privacy, and data governance.

Conduct research and develop machine learning, generative AI, stochastic simulation, and optimization solutions for clinical trial operations. Build predictive models, optimization engines, LLM workflows, and data pipelines supporting enrollment forecasting, cost optimization, site selection, document processing, and risk management. Validate models, promote responsible AI and data quality, contribute reusable code and documentation, and communicate findings to technical and clinical stakeholders.

Defines LTK’s AI product vision and strategy across agent infrastructure, LLM evaluation, machine learning models, and knowledge graphs. Partners with ML, data science, engineering, and product teams to establish model requirements, evaluation frameworks, launch criteria, and responsible AI practices. Owns trade-offs involving quality, reliability, latency, cost, privacy, transparency, and human oversight while influencing senior leaders and driving AI-powered products for creators, brands, and consumers.

Leads the architecture, development, and deployment of physical AI systems for autonomous robots and embodied agents. Designs perception, planning, control, navigation, manipulation, sensor fusion, and multimodal AI capabilities. Develops simulation and reinforcement learning pipelines, translates research into production systems, mentors engineering teams, establishes technical standards, and partners with mission and business stakeholders on strategy and emerging technologies.

Builds firmwide AI infrastructure and production-grade generative AI applications, including agentic workflows using RAG, semantic search, planning, and structured outputs. The role develops evaluation harnesses, guardrails, approval workflows, tool-use controls, source attribution, and kill switches while optimizing reasoning quality, retrieval accuracy, latency, cost, safety, and user experience. It also evaluates foundational and open-weight models for enterprise use.

Build AI-powered developer infrastructure for the core servicing platform, including coding-tool harnesses, scaffolding, context retrieval systems, evaluation frameworks, and workflow automation. Integrate AI assistance into CI/CD, code review, testing, and developer feedback loops. Establish technical direction, solve cross-team engineering problems, create reusable tooling, and enable platform teams to adopt AI-assisted development at scale.

Leads enterprise AI strategy, roadmap development, and delivery of AI-powered solutions. Manages AI scientists and engineers, translates business needs into technical requirements, oversees AI project portfolios, and drives adoption, governance, education, and measurable business impact. Collaborates with executives, stakeholders, data scientists, and engineering teams to develop scalable analytics, machine learning, and generative AI solutions.

Lead architecture and delivery of scalable AI infrastructure, including model serving, feature stores, training systems, and ML observability. Partner with ML, data, and AI teams to improve workflows and reliability. Manage AWS CPU/GPU resources, establish monitoring standards, shape technical strategy, and mentor engineers. The role requires deep expertise in distributed systems, production ML workflows, search systems, vector databases, and modern ML infrastructure tooling.

Develops and validates advanced ultrasonic nondestructive testing methods for detecting defects in specialty alloys. Responsibilities include designing experiments, analyzing ultrasonic signals and large datasets, creating signal-processing algorithms, establishing detection thresholds, evaluating inspection equipment, and supporting technology transfer into production. The role collaborates with manufacturing, quality, metallurgy, and engineering teams; produces technical documentation; supports patents and publications; and requires Carpenter UT Level 3 certification within two years.

Build and deploy production-grade generative AI systems for enterprise clients in financial services and healthcare. Embed with customers to understand their workflows, design solutions, write production code, create evaluation systems, and own delivery from strategy through deployment. Work with Claude, AWS, agentic workflows, Cowork, and industry blueprints while advising senior stakeholders, supporting pre-sales, and improving AI delivery frameworks.

Supports the Nittany AI Challenge by troubleshooting AI tools, mentoring student interns, assisting with workshops and events, improving program workflows, and tracking participation and milestones for impact reporting. The role requires regular on-campus engagement at University Park, occasional evening and weekend hours, and offers an opportunity to pursue a master’s degree.

Lead enterprise sales for Komodor’s agentic operations platform, prospecting and closing new business with F1000 organizations. Build and manage an enterprise pipeline, exceed bookings targets, develop customer relationships, and communicate the value of complex infrastructure SaaS solutions to SRE, DevOps, and platform engineering teams. Collaborate with Sales Engineering, Customer Success, Solutions Architects, Product, and Marketing while helping define and scale the company’s early enterprise sales motion.

Lead client-specific software solutions from pre-sales discovery through implementation and value realization. Manage project plans, scope, timelines, budgets, resources, profitability, and client expectations. Partner with sales on discovery, solution design, effort estimation, and Statements of Work. Facilitate workshops and create user stories, wireframes, architecture diagrams, prototypes, demos, and decks using modern AI tools. Guide cross-functional engineering, design, and development teams to deliver scalable, high-quality outcomes.

Establish and lead the People Analytics function, building trusted workforce data foundations, governance, privacy standards, analytical products, workforce planning capabilities, and AI-enabled decision support. Partner across HR, Finance, IT, and business leadership to deliver executive insights, improve workforce decisions, and scale analytics operations. Recruit and develop the analytics team while ensuring responsible data use, global-local compliance, stakeholder adoption, and alignment with organizational priorities.

Develop and deploy production-grade 3D LiDAR and computer-vision perception systems for mobile robots. Own algorithms from sensor characterization through fleet deployment, while building AI agents and automated pipelines for data labeling, dataset curation, edge-case triage, and regression validation. Collaborate across controls and software teams, lead architecture and testing efforts, mentor engineers, and improve collision avoidance and autonomous behaviors across diverse operating environments.

Leads strategy, roadmaps, delivery, adoption, and value realization for Data and AI products. Partners with business stakeholders and technical teams to define opportunities, prioritize requirements, scale prototypes into production, drive user engagement, measure outcomes, and continuously improve products. Requires product management, analytics, data, AI, stakeholder management, and technical fluency.

Leads enterprise laboratory operations across Blood Bank, Clinical Pathology, and Anatomic Pathology. Oversees staff, budgets, resources, compliance, accreditation, quality, patient safety, risk management, and regulatory readiness. Develops strategic plans, manages laboratory technologies and workflow improvements, partners with clinical and administrative leaders, and drives automation, artificial intelligence, analytics, and continuous improvement to enhance performance and patient experience.

Build, optimize, and maintain scalable ETL pipelines and data warehousing solutions using Azure technologies. Integrate complex datasets, manage database quality and security, automate processes, document data architectures, and collaborate with data scientists, analysts, and stakeholders. The role also requires experience with SQL, programming languages, artificial intelligence, machine learning, and related analytical methods.

Lead enterprise technology strategy and modernization for a rapidly growing, distributed organization. Responsibilities include architecture, cloud, infrastructure, cybersecurity, data, applications, integrations, vendor management, technology governance, business continuity, and digital innovation. The executive will align technology investments with business objectives, lead transformation using Agile and potentially Scrum, establish KPIs, manage significant budgets, and build a high-performing technology organization supporting operational efficiency, customer experience, risk management, and scalable growth.