Top Artificial Intelligence Jobs

Architect and optimize timing and power implementation flows for ASIC/FPGA designs. Lead STA and UPF-based power flows, library generation/characterization, automation infrastructure, and apply ML/AI to improve flow efficiency, debug productivity, and signoff QoR while collaborating across PD, STA, library, and CAD teams.

Design, train, test, deploy, and maintain AI/ML models and agentic workflows (LLMs, RAG). Build secure cloud data foundations on AWS, implement MLOps/DevSecOps (CI/CD, containerization, automated testing), design event-driven integrations, and collaborate with cross-functional teams to deliver AI-enabled solutions for Air Force mission needs. Secret clearance required.

Design, train, test, deploy, and maintain AI models and agentic workflows. Build secure data foundations on enterprise cloud (AWS). Implement MLOps/DevSecOps, CI/CD, containerization, event-driven integrations, and model evaluation. Collaborate with data engineers, data scientists, architects, and product owners to deliver AI-enabled solutions for the Air Force.

Build and own an agentic creative pipeline that generates, vets, and optimizes thousands of image and video ad variants daily: extend LLM critic feedback to multimodal assets, build fast cost-efficient video pipelines, route generative models by cost/quality, train and deploy brand-style checkpoints, implement policy pre-flight checks, and ship event-triggered ad pipelines with A/B validation in production.

Design, train, test, deploy, and maintain AI/ML models and agentic workflows. Collaborate with engineers and product teams to build secure cloud data foundations, event-driven integrations, and MLOps/DevSecOps pipelines supporting Air Force mission needs.

Build, train, test, deploy, and maintain AI models (LLMs, RAG, agentic workflows). Collaborate with engineers and product owners to implement MLOps/DevSecOps, cloud data foundations, event-driven integrations, and secure AI-enabled applications to support Air Force mission needs.

Design, train, test, deploy, and maintain AI/ML models and agentic workflows. Build secure cloud data foundations (AWS), implement MLOps/DevSecOps (CI/CD, containers, automated testing), design event-driven integrations, and collaborate with engineers, data scientists, and product owners to deliver AI-enabled mission solutions for the Air Force. Requires Secret clearance and a relevant bachelor’s degree.

Lead cross-functional engineering workstreams to design software, integration, data, testing, and infrastructure architectures. Define and encode engineering best practices into AI coding agents, design AI-assisted workflows, select AI tools, implement CI/CD, automated and chaos testing, and lead performance, integration, and data strategies. Provide technical leadership, mentorship, and measurable quality reporting at enterprise scale.

Design reusable, secure, and scalable AI and automation solution patterns, build reference implementations and proofs-of-value, provide technical advisory, embed responsible AI practices, create standards and reusable assets, and enable distributed teams from intake through production while partnering across architecture, data, security, and operations.

Build and operate integrations and automations using iPaaS and APIs, ship production AI/LLM agents and workflows, implement identity-driven provisioning (SSO/SCIM/OAuth), and deliver monitored, reliable automations with runbooks while partnering across teams to scale systems.

Lead developer-focused growth for DeepL's headless surfaces (MCP, CLI). Drive discoverability, instrument activation funnel to paid usage, create production-quality developer artifacts and content, build ecosystem presence, and feed actionable developer insights into product and engineering.

Design, develop, test, and maintain cloud-native, API-driven AI/ML software solutions. Lead technical delivery, design scalable architectures, implement AWS-based deployments, productionize ML/GenAI applications, and collaborate with stakeholders to translate business needs into secure, maintainable systems.

Lead development of model-driven pricing intelligence, supplier performance signals, and automated financial reconciliation across flights, hotels, cars, and ancillaries. Define roadmaps, productionize predictive models, implement monitoring/drift detection, and deliver cross-functional, measurable systems that ensure revenue integrity and marketplace economics.

Lead design and maintenance of data models, dashboards, and reporting for travel commercial supply. Run deep-dive analyses, define KPIs, build forecasting and planning frameworks, map and improve commercial workflows, and implement AI-driven solutions. Partner closely with Product and Engineering to translate commercial problems into technical briefs and drive adoption of operational improvements.

Lead and grow an AI-focused engineering organization building full‑stack products that integrate LLMs and RAG. Drive architecture, development (.NET/Java/React), databases, CI/CD, testing, scalability, and security. Mentor engineers, collaborate with product and UX, and deploy production LLM systems using vector DBs, LangChain/LangGraph, Docker/Kubernetes, and cloud/on‑prem infrastructure.

Embed onsite with mid-market CEOs to own executive relationships and deliver finance-verified AI transformation outcomes. Drive fixed-scope first wins, translate problems to build-ready specs, manage end-to-end rollouts, and codify methods to scale deployments.

Design, build, and maintain end-to-end agentic intelligence systems that ingest social, on-chain, and ads data; create pipelines, semantic layers, and multi-step AI agents; ship full-stack apps and dashboards; collaborate with marketing and client teams to operationalize structured intelligence and measurable impact.

Design, test, and deploy AI/agentic solutions that extract structured data from healthcare documents and web workflows. Collaborate with operations and engineering to translate workflows into technical specs, create reproducible playbooks, evaluate model accuracy, and define metrics to drive automation rollout.

Build and operate AI agent runtimes and enterprise integrations enabling AI solutions to securely access systems and data. Design connectors to ERP/MES/CRM/databases, expose APIs, implement observability, and collaborate with architecture, data, security, and business teams to operationalize scalable, secure AI deployments.

Design, build, and operate scalable, secure AI infrastructure and MLOps platforms. Deploy containerized GPU workloads, manage APIs and data integrations, implement IaC and CI/CD pipelines, and collaborate with enterprise architecture, cybersecurity, and data teams to enable AI agent execution and model lifecycle management across manufacturing and corporate systems.

Lead and maintain enterprise AI governance frameworks, perform AI risk assessments and gap analyses, define controls for AI development and deployment, ensure regulatory and standards compliance (NIST/ISO), collaborate with cross-functional stakeholders, produce executive-level materials, and support AI training and awareness.

Lead and scale Abby Care's privacy and AI governance programs: own HIPAA and state privacy compliance, BAA program, AI use-case review and monitoring, incident response, vendor and outside counsel management, cross-functional legal partnership with Product/Engineering/Clinical, and hire and lead the privacy/AI team.

Design, build, and maintain automated test suites, evaluation scenarios, metrics, and datasets for AI-powered features. Automate QA workflows with Python and CI/CD, test APIs and ML model outputs, define quality strategy and acceptance criteria, create CI/CD quality gates, mentor teams, participate in code reviews, and document findings.

Full-cycle AE role selling Encord's data platform to robotics, autonomous vehicle, and Physical AI teams. Own pipeline through close, build technical and executive relationships, shape ICP, partner with Product and CS, and hit pilots and revenue targets in an early-stage GTM environment.

Build and maintain data pipelines, prepare and curate data for ML/GenAI, support presales solution design and BOM/capacity planning, integrate enterprise systems, develop dashboards for deployment tracking and performance, and document standards and automation for scalable AI-enabled infrastructure delivery.