Top Artificial Intelligence Jobs

The Data Engineer will develop and maintain AI context data capabilities, create data pipelines, ensure operational excellence, and follow compliance protocols. Responsibilities include data ingestion, retrieval quality improvements, and semantic layer implementation for AI integration within healthcare organizations.

Lead large-scale data, analytics, and AI programs: define governance, roadmaps, and delivery; manage cross-functional teams and stakeholders; mitigate risks and dependencies; align business and technology; oversee testing, change management, and adoption to deliver measurable data-driven value across global markets.

The Director & Group Head of Applied AI will lead AI strategy in drug discovery, collaborating with teams to deploy machine learning solutions and drive innovation in biomedical research.

Lead enterprise data modernization and AI adoption as a technical thought leader. Influence senior stakeholders, design cloud-native data and GenAI solutions (Azure, Databricks), drive MLOps/LLMOps and RAG architectures, ensure Responsible AI, mentor technical teams, and represent the company externally.

Lead planning, coordination, and delivery of AI, FinOps, and CloudOps initiatives. Build roadmaps, business cases, ROI models, and executive materials; run intake/prioritization, vendor evaluations, cost-optimization actions, governance, OKR tracking, and risk/dependency management to drive cross-functional programs to decision-ready completion.

As a Software Engineer - AI, you will design, develop, and deploy AI solutions for legal applications, working with clients and technical teams to enhance legal workflows and improve AI outputs.

The Lead AI Forward Engineer designs AI solutions, guiding implementations for scalability and compliance, while mentoring teams and shaping technology strategy.

Lead the go-to-market build and execution for a new AI training business: define strategy, source and close deals, partner with product/marketing/delivery/legal, and hire/manage a small commercial team. Additionally, drive outbound programs and strategic commercial projects across the sales organization, turning analysis into implementable revenue growth initiatives.

Lead the finance automation and AI program for the Chief Accounting Office: assess processes, build business cases, define roadmaps, design and implement automation (RPA, Alteryx, Power Automate, scripts), integrate with data/APIs/ERP, establish governance, and train finance teams to adopt scalable AI/automation solutions.

Design, implement, and maintain AI-driven tools and workflows (initially Microsoft 365 Copilot, Copilot Studio, Power Platform). Translate business needs into secure, governed AI solutions, manage data sources and access controls, build automations, document designs, implement guardrails, and train users to drive adoption.

Senior full-stack engineer to design and deliver enterprise web applications for banking using AI coding agents. Own end-to-end systems (Angular frontends, Java/Spring Boot microservices, relational/NoSQL data), integrate agentic workflows into CI/CD, validate AI-generated code, and coach junior engineers. Must apply banking domain knowledge and drive secure, scalable, production-ready solutions.

Lead assessment and implementation of AI across library operations and curricula, develop and coordinate OER/Zero Cost Degree initiatives, provide faculty development and consultation, manage OER grants and staff, and teach information literacy with emphasis on responsible AI practices.

Design, test, and iterate agentic AI workflows to evaluate model reasoning and tool use. Produce test cases, playbooks, and domain-driven feedback; partner with research and product teams to improve model capabilities and document best practices for enterprise AI adoption and enablement.

Lead the Partner Deal Structuring team to run regional deal forums, approve non-standard partner deals, integrate AI into processes, manage commercial frameworks and reporting, train stakeholders, and ensure compliant execution of partner and redistribution deals to maximize revenue and retention.

Join as a founding full-stack engineer to build an AI-native enterprise platform (agents, connectors, RAG, workflows). Own end-to-end product areas, design reusable primitives, ensure production readiness, work directly with customers, and shape roadmap and engineering practices.

Lead design, development, deployment, and maintenance of large-scale ML systems and pipelines. Provide senior technical consulting, resolve complex ML and governance issues, plan end-to-end model integrations, and document implementations for performance optimization.

Design, build, and evaluate managed AI agents embedded with business teams: translate processes into requirements, implement safe reliable agents, define evaluations and human-in-the-loop checkpoints, build reusable patterns, pilot and lead acceptance testing, and partner with data scientists and stakeholders.

Support NGA modernization by designing and implementing AI integration strategies across GOTS and COTS, embedding with analytic teams to accelerate workflows, delivering training and briefings, assessing commercial AI platforms for secure operational use, building adoption roadmaps, and advising leadership on technology investment and hybrid solutions while measuring analytic performance and ROI.

Lead design, build, and operation of enterprise agentic AI and RAG solutions for a regulated aerospace manufacturing environment. Implement vector stores, embedding pipelines, secure multi-agent orchestration, event-driven integrations, and production-grade CI/CD and governance to automate and audit digital thread workflows while ensuring compliance with export controls.

Owner and coordinator for GTM technology portfolio, optimizing seller productivity through SaaS tool administration, generative AI and automation prototyping, workflow analysis, requirements gathering, vendor coordination, change management, training, and metrics tracking to drive adoption and measurable business impact across Sales, Marketing, Revenue Operations, and partners.

Own Tread's operating model, forecasting, unit economics, cash planning, and revenue operations. Build investor materials, automate finance workflows with AI, integrate billing/ERP/CRM systems, run financial cadence, and partner cross-functionally to scale revenue and profitability.

Research, design, and implement foundation models for machine-generated and graph data; develop distributed training and inference pipelines; collaborate with engineering and product teams to productionize models; evaluate new AI/ML techniques and drive MLOps, monitoring, and model optimization to improve scalability and reliability.

Design, build, and scale AI-enabled cloud infrastructure including RAG pipelines, vector DBs, and multi-agent flows. Implement IaC (Terraform/ARM), Kubernetes (AKS/EKS), containerization, automation (Python/PowerShell), cloud security controls, and collaborate with cross-functional teams to deploy production LLM workloads.

Lead cross-functional programs to deploy AI servers from prototype to volume production. Manage roadmaps, suppliers, ODMs, procurement, manufacturing, risk, budgets, and stakeholder communication to ensure on-time, production-ready delivery of compute infrastructure.

Lead product strategy and P&L for hyperscale AI connectivity products (Dark Fiber, Wave, eLine). Translate AI workload and GPU networking needs into scalable connectivity solutions, drive GTM and pricing, enable sales for large enterprise and hyperscaler deals, and align cross-functional teams on KPI-driven roadmaps.