Top Artificial Intelligence Jobs

Develop AI compiler solutions, optimize GPU programming, build training pipelines, collaborate on integrating AI policies, and conduct rigorous performance benchmarking.

Design and build agent infrastructure and orchestration for AI-driven products; implement observability, tracing, evals, guardrails, and tooling to deploy, monitor, and improve LLM-based systems in production.

Lead end-to-end SEO strategy (technical, on-page, content, conversion) using SEO analytics to generate performance insights. Manage large-scale datasets and combine internal and external sources to identify actionable search and content opportunities. Build AI-driven forecasting, ranking, and content optimization models using Python, SQL, and NLP. Drive technical SEO improvements (schema, structured data, crawl diagnostics) and support content strategy. Requires EST/CST availability and biannual domestic travel to Georgia.

Senior technical subject-matter expert responsible for Security Posture Management: define operating model, lead initiatives, assess and design controls, monitor KPIs/KRIs, report risks, drive remediation, and advise cross-functional teams to ensure compliance with regulatory and security frameworks.

As a Senior ML Engineer, you'll design, develop, and deploy ML models for product features, collaborate with teams, and drive research initiatives.

Design, implement, and support enterprise AI infrastructure and pipelines; optimize workflows via automation and IaC; integrate and evaluate GenAI/MLOps tools; ensure security compliance; monitor performance for scalability, reliability, and cost efficiency; collaborate with engineers, IT, and stakeholders. SES.4 adds system lifecycle leadership and mentoring.

Design, build, and maintain automations across MSP tools (Power Automate, Zapier, ConnectWise) using PowerShell/Python and APIs. Identify inefficiencies, integrate platforms, monitor stability, document workflows, and partner with teams to prioritize high-impact automation to reduce repetitive work and staffing costs.

Design, build, deploy, and maintain AI-integrated full-stack web applications, chatbots, and dashboards. Collaborate with ML and backend teams to ensure secure, scalable, high-availability production systems and monitor model outputs and training analytics.

Support AI and digital transformation projects by collaborating with teams and senior data engineers/scientists to analyze and preprocess large datasets, help develop and deploy AI models, contribute to R&D, and build AI-powered products across smart factory, manufacturing, supply chain, and finance. Onsite/hybrid in Cary, NC.

Lead MUFG's global AI red team to drive adversarial testing, threat modeling, and technical strategy for AI systems. Provide technical leadership, collaborate with stakeholders, and operate in a hybrid on-site schedule to improve AI security posture across the organization.

Design, build, test, and deploy AI-powered workflows and agent instructions (Rafio, Claude) by interviewing staff, mapping processes, iterating on prompts/configurations, debugging workflows, documenting usage, and contributing to AI governance to drive adoption across business functions.

Lead and grow the Platform & AI Infrastructure team, set product and technical strategy, write and review code, build shared services (authn/authz, RBAC, FGAC, audit trails), and design and operate reliable AI/LLM infrastructure. Mentor engineers, hire, and embed AI where appropriate to accelerate the product while maintaining compliance.

The Revenue Operations Manager leads the design and implementation of data models and operational processes, leveraging AI to drive revenue growth and enhance efficiency in commercial strategies.

As Revenue Operations Manager, you will drive analytics and operations, enhance workflows, leverage AI tools, and improve revenue processes for the Travel Commercial organization.

Design, implement, validate, and deploy advanced machine learning and statistical models across the data science pipeline. Collaborate with product and engineering, mentor peers, translate research into production, and deliver high-impact business insights using large-scale and distributed data platforms.

This role involves developing strategies for electrical hardware design, consulting on new product development, mentoring junior staff, and leading innovative projects in HPC and AI systems.

Lead architecture and development of large-scale HPC and AI infrastructure for image processing and ML workloads. Design scalable distributed systems, drive GPU-accelerated strategies, partner cross-functionally, establish engineering best practices (CI/CD, observability, reliability), mentor engineers, and shape long-term platform strategy.

Lead discovery, analyze business processes, translate needs into functional specs and user stories, support solution design and Agile delivery, manage QA/UAT and functional configuration, define KPIs and data requirements, and align stakeholders across enterprise SaaS transformation programs.

The AI Assistant Product Manager is responsible for discovery, incubation, and shaping AI product propositions at Amadeus, focusing on delivering exceptional travel experiences through AI Assistant platforms.

Design, build, and deploy end-to-end AI systems powering product features and internal tools. Develop agent pipelines, prompt chains, orchestration, evaluation and monitoring frameworks, and integrate AI into backend services and interfaces while collaborating with product, data, and platform teams.

Lead strategy, roadmap, and execution for AI-powered products. Conduct market and customer research, define product requirements and user stories, manage full product lifecycle, collaborate with engineering/data science/design, use Agile practices, analyze product performance, and drive customer-centric product decisions.

Lead and grow a global Forward Deployed Engineering team to build a next-generation AI agent platform; drive AI adoption across the software lifecycle; manage delivery, hiring, and performance; partner with product and engineering to design cloud-native Azure solutions using Python and C#; collaborate with customers to validate use cases and convert feedback into reusable platform features.

Lead technical product strategy and roadmap for large-scale Conversational AI platform capabilities. Prioritize features, collaborate with engineering, AI, and customer teams, optimize scalability, latency, and AI accuracy, engage enterprise customers, define KPIs, and drive go-to-market and platform performance improvements using LLMs and NLP technologies.

Lead full‑stack development of web and mobile applications, contributing to architecture and best practices. Build backend services (Python, Node/Express), frontend apps (React, Vue, TypeScript/ES6), databases (MySQL/Postgres, MongoDB, DynamoDB), REST APIs and microservices. Employ AI coding tools, testing (unit/integration), CI/CD and cloud/serverless (AWS). Provide consulting, scale/operate solutions, and work in agile teams with emphasis on code quality, performance, and client relationships.

Drive adoption of generative AI and legal technology across practice groups by partnering with attorneys, designing AI-enabled workflows, running pilots, delivering training, monitoring usage, and translating feedback into improvements.