Top Artificial Intelligence Jobs

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.

Lead vision and strategic architecture for enterprise AI and analytics on Everpure's all-flash platform. Define data/metadata orchestration, build ecosystem partnerships, drive technical competitive strategy, and act as spokesperson to executive leadership and customers to enable AI-ready, governed data pipelines across edge, core, and cloud.

Lead design and delivery of enterprise analytics and AI governance frameworks, operating models, CoEs, and maturity assessments. Advise on Responsible AI, generative AI governance, adoption strategies, change management, and stakeholder alignment. Create reusable methodologies, run workshops, assess maturity, and mentor consultants to scale trusted analytics, automation, and AI across large organizations.

Design and build the central AI orchestrator (Maestro) and distributed agents; own containerized deployment, observability, LLMops, evaluation pipelines, RAG optimization, and technical leadership for agentic production systems.

Lead and mentor an AI engineering squad to design scalable AI architectures and deliver performant solutions. Partner with Product to prioritize roadmap, track metrics (usability, conversion, retention, F1), optimize development processes, drive technical decisions, and produce documentation to support long-term success.

Design, build, and deploy agentic multi-agent AI systems and RAG pipelines for document intelligence and procurement workflows. Integrate and evaluate LLMs, implement orchestration frameworks, build human-in-the-loop approvals, and collaborate on production deployment, observability, and secure architectures for government acquisition environments.

Manage and administer the marketing technology stack (HubSpot, integrations), ensure data governance and campaign quality, execute and optimize demand/ABM campaigns, operationalize lead scoring and intent models, train and support marketing teams, and partner cross-functionally with RevOps, Sales Ops, Data and IT to scale marketing operations.

As an AI Data Engineer, you will build and maintain data pipelines and AI models to enhance GTM strategies, improving decision-making and operational efficiency.

Lead enterprise AI adoption by evaluating vendors and models, designing reusable agentic frameworks and reference architectures, developing prototypes and POCs, and enabling business units to deploy secure, scalable LLM-, RAG-, and agent-based solutions across cloud and microservice environments.

Lead identification, evaluation, and implementation of emerging technologies (AI, automation, robotics) and process improvements across logistics and corporate functions. Build business cases, run pilots, coordinate cross-functional teams, assess operational and financial impact, support governance and enterprise readiness, and measure post-implementation outcomes.

Lead development and deployment of AI/ML solutions for target discovery, translational research, and biomarker identification. Partner with scientists and data engineers to build scalable workflows for multimodal biological data, promote reproducibility and model validation, mentor colleagues, and recommend emerging AI applications to accelerate discovery.

Lead design, prototyping, and operationalization of AI/ML solutions for intelligence mission workflows. Develop LLM integrations, scalable architectures, and analytics to automate discovery, accelerate insights, and transition capabilities to production while shaping technical strategy and best practices.

Own go-to-market strategy and positioning for NVIDIA desktop AI products. Lead cross-functional teams through planning, launch, and post-launch optimization. Coordinate OEM and channel messaging, train regional sales, collaborate with product/sales/marketing, and drive analyst/influencer relations to grow pipeline and sell-through.

Provide onsite and remote technical support for NVIDIA Ethernet and AI infrastructure, troubleshoot Linux-based systems, debug networking and interoperability issues, collaborate with engineering and marketing, document support methodologies, and act as primary customer contact, spending at least one week per month onsite.

Architect and ship production AI systems and agent workflows with strong focus on explainability, auditability, and compliance for financial services. Build Python/FastAPI tooling, distributed event-driven backends, evaluation and monitoring pipelines, and occasional React/TypeScript UIs. Drive schema, API, and system architecture decisions and establish validation and quality-assurance patterns for AI outputs.

Lead AI product strategy for investor workflows, building AI-native products (agents, copilots, MCPs, APIs) that turn proprietary data into reliable, production-grade investor tools. Partner with customers, engineering, design, and research to run experiments, define quality/trust frameworks, and ship 0→1 products that automate and augment investment decision-making.

Design, build, and operationalize scalable AI/ML infrastructure and data pipelines. Implement model lifecycle management (MCP), integrate agent frameworks (e.g., LangChain), manage vector DBs and feature stores, enable HITL workflows, maintain data architectures, mentor junior engineers, and present work internally and externally.

Lead operational management of the Enterprise AI Center of Excellence: run governance and intake processes, manage the AI portfolio and roadmap, coordinate cross-functional delivery, embed responsible AI guardrails, build enablement assets, drive change management and stakeholder engagement, and report ROI and status to executive leadership.

Design, build, and scale AI-powered operational platforms integrating LLMs, monitoring, CI/CD, ticketing, and cloud services. Develop APIs, data pipelines, front-end apps (TypeScript/React/Next.js), backend systems, evaluation/observability tools, and reusable frameworks. Collaborate across SRE, Product, and Infrastructure to deliver production-grade, automated AI workflows and improve system reliability and performance.

Lead and build Console's enterprise customer enablement function end-to-end: onboarding, training, documentation, self-serve resources, and metrics. Partner with Product, Engineering, Sales, and Forward Deployed Engineering to design scalable learning experiences, measure effectiveness, and iterate to drive adoption and retention for enterprise AI agents.

Design, build, and maintain scalable full-stack systems for underwriting experience, modernizing customer correspondence and automating manual processes using AI. Collaborate cross-functionally, participate in design and code reviews, define product/architecture, and apply DevOps and cloud practices to deliver production-ready solutions.

Build and maintain backend, CI/CD, and data integrations for the NOS platform. Ramp on Palantir Foundry and AI/data tooling, write production code and tests, and collaborate with engineers and data scientists under senior review to deliver scoped features.

Customer-facing specialist who leads product demos, conducts workflow discovery, tailors use-case presentations, and collaborates with Sales, Product, and Engineering to drive AI adoption in fashion design, merchandising, and product development.

Design and evaluate AI-enhanced instructional materials, lead Agentic AI implementations for adaptive learning, collaborate with faculty and technologists, run needs analyses and effectiveness evaluations, and deliver faculty training on AI in education.