Top Artificial Intelligence Jobs

Lead the design and deployment of AI platforms, agentic systems, model gateways, RAG pipelines, and AI-focused DevOps infrastructure. Build self-service GCP AI APIs, SDKs, and Terraform modules; manage model routing, observability, security, deployment, and automation using GKE, Cloud SQL, Cloud Build, and related services. The role requires extensive platform engineering, cloud infrastructure, IaC, MLOps, and enterprise AI architecture experience.

Participate in a 12-week internship collaborating with business and AI teams to identify use cases, prototype and deploy AI solutions using low-code frameworks, large language models, and orchestration tools. Support AI literacy materials, demonstrations, and internal showcases that promote adoption. Translate technical concepts into business value while developing analytical, problem-solving, communication, and collaboration skills.

Lead the architecture and delivery of AI/ML initiatives supporting U.S. Air Force and Army predictive logistics and condition-based maintenance programs. Coordinate engineering teams, translate mission needs into technical solutions, guide tradeoff decisions, mentor technical leaders, and ensure AI/ML systems improve fleet maintenance, aircraft availability, and military readiness. The role requires Databricks expertise, technical leadership, stakeholder communication, and an active Secret clearance.

Monitors and reconciles DenialsAI account data to verify case creation, workflow progression, user activity, and appeal output. Investigates pipeline discrepancies, builds operational reports and dashboards, tracks resolution and coverage metrics, maintains KPI scorecards, defines monitoring thresholds, and validates data accuracy. Collaborates with engineering, operations, client services, and external stakeholders while complying with HIPAA and information security requirements.

Operate and improve an on-premises Kubernetes-based AI and large language model platform across development, QA, and production. Responsibilities include infrastructure deployment, maintenance, troubleshooting, access provisioning, CI/CD support, Ansible automation, monitoring, incident response, documentation, and collaboration with infrastructure, security, and engineering teams.

Designs, deploys, and scales enterprise AI agents, RAG systems, automations, and integrations across ServiceNow and Microsoft ecosystems. Builds solutions with Claude, Copilot Studio, Azure AI Foundry, Azure AI Search, and related orchestration tools. Responsibilities include full-stack development, customer-facing implementation, solution architecture, evaluation, responsible AI controls, troubleshooting, and reusable platform development. The role requires extensive travel and collaboration with technical and business stakeholders.

Owns AI governance submissions from intake through cross-functional review, risk escalation, approval, and compliant implementation. Coordinates Legal, Privacy, Security, Procurement, Cloud, Identity, ServiceNow, and enterprise technology stakeholders; tracks schedules, blockers, documentation, and onboarding requirements. Ensures approved AI products are registered in CSDM, completes post-implementation checks, and improves governance processes, playbooks, and enterprise integrations.

Researches AI tools, platforms, and vendors; assesses privacy, security, transparency, contractual, regulatory, and reputational risks; gathers evidence; prepares governance submissions; validates policy alignment; maintains program tracking; and improves AI governance processes in collaboration with legal, security, privacy, procurement, technology, and business stakeholders.

Freelance contributors record spontaneous Xhosa conversations, participate in speech and AI interaction tasks, annotate recordings, and provide metadata. Work is remote, flexible, and task-based for approximately 2–4 hours daily. Candidates need native-level Xhosa fluency, advanced English, attention to detail, reliable internet, a quiet recording space, and a microphone or headset. Prior speech recording or data annotation experience is advantageous but not required.

Designs and implements manufacturing processes for aerospace engines, supporting design for manufacturability, assembly, and testing. Develops work instructions, process flows, tooling designs, and inspection plans; evaluates machining, welding, brazing, additive manufacturing, and assembly methods. Collaborates with engineering, quality, supply chain, and production teams, performs root-cause analysis and corrective actions, and supports FMEAs, risk assessments, equipment validation, quality compliance, and process optimization using AI and manufacturing engineering tools.

Leads the design, implementation, and optimization of rocket engine manufacturing, assembly, integration, and test processes. Develops work instructions, tooling, fixtures, and production equipment; supports DFM/DFA/DFT, quality systems, root cause analysis, corrective actions, FMEAs, and process validation. Collaborates with engineering, quality, supply chain, and production teams to improve reliability, throughput, cost, and cycle time while meeting aerospace safety and quality standards.

Design and build Python APIs, agentic AI frameworks, LLM-powered clinical documentation systems, and EHR integrations. Evaluate and improve AI output quality for real-world clinical workflows, collaborate with physicians and customers to resolve documentation issues, and contribute across the platform’s full stack. The role requires strong Python, LLM, API, testing, and communication skills, with Google Cloud Platform and Firebase experience preferred.

Leads revenue strategy and execution for an industrial AI company. Owns strategic enterprise sales, revenue targets, forecasting, partnerships, customer expansion, and executive relationships. Builds a repeatable go-to-market engine, recruits and develops senior sellers, creates sales narratives and collateral, converts pilots into long-term contracts, and represents the company with industrial, energy, aerospace, defense, and Fortune 500 customers. The role is hands-on, requiring direct deal leadership while scaling the commercial organization.

Develops and governs FP&A analytics architecture, reporting standards, data models, dashboards, and automated reporting solutions. Extracts and prepares data from multiple systems, ensures data quality, and implements predictive analytics and AI-driven insights. Partners with Finance and IT to translate business requirements into scalable BI solutions, improve planning and reporting processes, and support performance management and decision-making.

Design scalable, production-grade architectures for agentic AI solutions, including orchestration, workflow execution, integrations, observability, evaluation, and responsible AI controls. Translate business requirements into technical designs, define reusable patterns, guide implementation, assess tradeoffs, and support release readiness, production monitoring, incident response, and post-release improvements. Collaborate across product, engineering, platform, QA, security, infrastructure, operations, and support teams.

Designs scalable, reliable architectures for production AI agents, including orchestration, workflow execution, integrations, evaluation, observability, security, and responsible AI controls. Translates business requirements into technical designs, defines reusable agent patterns, supports implementation and release readiness, and partners with engineering, platform, QA, operations, and product teams on monitoring, incident response, and continuous improvement.

Build and evaluate secure, robust machine learning systems for government sponsors. Responsibilities include researching AI vulnerabilities, developing adversarial ML defenses, designing prototypes, experimenting with ML frameworks and algorithms, testing system performance and security, transitioning capabilities to sponsors, collaborating across technical teams, and mentoring engineers. The role requires onsite work in Pittsburgh or Arlington, up to 25% travel, and an active Department of War security clearance.

Designs and delivers enterprise data and AI capabilities from concept through production. Responsibilities include defining target architectures, evaluating build-versus-buy decisions, building hands-on data and AI solutions, reviewing code and models, guiding platform adoption, and advising executive stakeholders. The role covers machine learning, generative AI, RAG, agent architectures, semantic layers, MLOps, model governance, cloud data platforms, and production readiness in a regulated environment.

Design and operate a production-grade multi-agent AI platform using Gemini models, Google ADK, Pub/Sub, Redis, Firestore, Neo4j, and evaluation tools. Build orchestration, multimodal integrations, RAG, adaptive learning loops, artifact generation, guardrails, and performance optimization. Collaborate across backend, frontend, prompt engineering, and product teams while scaling AI tutoring capabilities and monitoring quality, latency, safety, and model drift.

Own enterprise sales cycles targeting frontier AI labs, Big Tech, and enterprise AI adopters. Acquire strategic accounts, expand existing relationships, develop territory plans, manage complex RFPs, negotiate enterprise agreements, and coordinate internal teams across delivery, legal, finance, and client partnerships. Build technical and executive relationships, conduct customer meetings and business reviews, maintain CRM discipline, and deliver accurate forecasts and account growth.

Architects and operates production-grade agentic AI systems for enterprise identity security. Responsibilities include designing multi-agent orchestration, tool use, planning, memory, retrieval-augmented generation, model integration, evaluation, safety guardrails, observability, and human-in-the-loop controls. The role provides technical leadership through architecture ownership, code reviews, mentoring, and establishing scalable production AI practices.

Build and deploy production AI tools, agents, internal platforms, workflow automations, custom demos, and technical assets for Stripe’s Solutions Architecture organization. Collaborate with Solutions Architects, Product, Engineering, and leadership to identify productivity opportunities, translate ambiguous needs into deliverables, and scale successful workflows. Debug backend systems, integrate AI capabilities, document solutions, and maintain infrastructure across multiple codebases.

Serve as a post-sales technical advisor for healthcare clients using conversational AI solutions. Lead scoping, architectural recommendations, deployments, configuration, testing, troubleshooting, and maintenance of AI agents. Coordinate with Engineering, Product, Customer Success, and Sales while aligning technical solutions with business goals. Manage client projects, anticipate deployment issues, and advise customers on best practices and product capabilities.

Designs, builds, and productionizes AI-powered applications using LLMs, agentic architectures, retrieval patterns, model APIs, and enterprise AI platforms. Partners with product and engineering teams throughout the development lifecycle, from prototyping through deployment and operations. Develops reusable APIs, SDKs, frameworks, and reference implementations; evaluates emerging AI technologies; resolves complex production issues; and provides technical mentorship and architectural guidance.

Build and lead full-stack web applications and enterprise search capabilities using GCP and Vertex AI Search. Responsibilities include frontend and backend development, API and cloud service design, search indexing and relevance optimization, experimentation, performance tuning, testing automation, observability, reliability, and technical mentorship. The role partners with product, UX, marketing, data science, and ML teams to deliver personalized discovery experiences across GM digital products.