Top Artificial Intelligence Jobs

Lead and expand SEO, GEO, AEO, and AI Search capabilities; define services, delivery frameworks, and AI-enabled operations; drive client strategy, growth, retention, and profitability; establish AI governance, workflows, KPIs, and training; mentor teams and represent the agency as a thought leader.

The AI Platform Engineer will own distributed training and inference operations using Ray on GPUs, manage the lifecycle of machine learning models, optimize performance, and integrate reinforcement learning. Key responsibilities include overseeing the Ray ecosystem, configuring training and inference systems, and ensuring quality and performance metrics are met throughout the model lifecycle.

Design and deliver production AI agent solutions for Veeva Quality Cloud to extract structured data from documents. Lead data flow, model selection and integration, prompt design, orchestration, and collaborate with engineering and UX to automate quality processes. Evaluate AI agents and present capabilities to stakeholders.

Design, build, test, and support automation solutions across business applications using AI, agentic AI, and RPA. Collaborate with product owners and scrum teams to gather requirements, create technical designs, implement code, automate testing and deployments, troubleshoot production issues, document processes, and mentor junior teammates.

Lead design and delivery of production AI Agent features for Veeva QualityDocs and Training. Define data flows, model integration, orchestration, and prompts; collaborate with engineering and UX to automate regulated quality and training processes; evaluate AI agents and present capabilities to stakeholders.

Lead design and delivery of AI agents for Veeva Quality Cloud to convert documents into structured data. Define data flows, orchestration, prompts, and model integrations; collaborate with engineering and UX; evaluate AI agents and present capabilities to stakeholders.

Serve as an AI-fluent implementation consultant for Veeva Vault Safety: advise on AI strategy, design validation and governance frameworks, build monitoring dashboards, run workshops, lead gap analyses and configurations, train customers, and ensure GxP/CSV compliance throughout implementations.

Lead design and delivery of AI Agent features for Veeva QualityDocs and Training. Define data flows, orchestration, prompts, and model integrations. Collaborate with engineering and UX, evaluate AI agents, and communicate value to stakeholders. Hands-on technical product work with travel and Eastern Time zone location requirement.

Design and implement extraction, chunking, vector indexing, and semantic retrieval pipelines for LLMs. Optimize hybrid search, reranking, and RBAC-enabled metadata filtering. Ensure HIPAA/HITRUST-compliant handling of PHI, build evaluation frameworks for retrieval groundedness, and collaborate with architects and developers to deliver context services and hand off validated prototypes.

Lead AI product strategy and roadmap, prioritize and manage the AI backlog, translate business needs into user stories, collaborate with developers and data scientists, ensure HIPAA-compliant and ethical AI, define KPIs, monitor production performance, and drive adoption and iteration.

Design and lead enterprise AI architecture and developer platforms including LLM integration, RAG/agent patterns, vector search, and governance. Enable reusable AI application patterns, ensure HIPAA-compliant PHI handling, collaborate with security and cloud teams, and mentor engineering teams to scale secure, cost-effective AI solutions.

Lead global marketing for Caris AI Insights, defining positioning, messaging, GTM, and commercialization. Drive product launches, sales enablement, thought leadership, customer evidence, competitive analysis, and cross-functional alignment to accelerate adoption of AI-driven oncology intelligence across healthcare, pharma, and research customers.

Design, build, and deploy AI systems across the full lifecycle: data pipelines, model development (traditional ML and generative AI), agent-based applications, API integration, observability, and evaluation. Collaborate with architects and domain experts, support experimentation and production deployments, and assist with project coordination and stakeholder communications.

The Lead Global Solutions Architect designs and supports enterprise technology solutions, collaborating with teams to deliver scalable and secure architectures for both cloud and on-premises environments.

Lead discovery and delivery of AI-enabled clinical workflow capabilities for care teams. Define requirements, run customer experiments and pilots, measure adoption and outcomes, partner with engineering, clinical, UX, analytics, and commercial teams, and ensure responsible, transparent, and regulatory-compliant AI product experiences that improve workflow fit and user satisfaction.

The Senior AI Engineer will implement AI solutions, consult stakeholders, lead projects, and ensure integration of cutting-edge AI technologies to meet business goals.

As a Senior AI Engineer, you'll develop and deploy AI agents, focusing on model fine-tuning, infrastructure, and user interaction across social platforms, ensuring reliability and engagement.

As a Senior AI Engineer, you'll implement AI solutions, facilitate customer workshops, lead development projects, and align strategies with business objectives, utilizing Azure and OpenAI technologies.

Lead architecture and delivery of production AI agent systems: design agent infrastructure, observability, evaluation, progressive rollout, and reliability. Provide hands-on technical leadership, mentor senior engineers, drive RFCs/ADRs, and partner with product to sequence strategic platform work.

Build and operate cloud-native, large-scale data pipelines and scalable services for the Mist platform. Develop AI-driven routing intelligence to detect, troubleshoot, predict, and mitigate network issues in multi-tenant data centers, collaborating across teams and leveraging AI-assisted development tools.

Design, build, evaluate, and operationalize NLP and generative AI solutions including document classification, extraction, summarization, LLMs and agentic systems. Integrate with enterprise data, develop front-end interfaces (JavaScript) and backend APIs/microservices (Python, Flask/FastAPI).

Design, build, and deploy end-to-end web applications that integrate AI/LLMs. Implement frontend UIs, backend APIs, databases, and RAG workflows, containerize and version applications, and enable secure deployments in a DoD environment with attention to AI safety and evaluation.

Lead enterprise AI governance, risk management, and Responsible AI adoption. Oversee AI inventory, policy, risk lifecycle, regulatory compliance, vendor assessments, and integration of governance into business workflows. Partner with Legal, Compliance, Security, PMO, and business units to prioritize AI initiatives, report metrics and ROI, and advise executives and boards.

Design, build, and maintain full-stack web applications with AI/GenAI integration. Develop and deploy ML/LLM models, RAG pipelines, and APIs; implement GitLab CI-based CI/CD; collaborate with data scientists and participate in agile delivery.

The AI Production Designer will manage AI-enabled production workflows, maintain file standards, and coordinate with teams to ensure quality and efficiency.