Top Artificial Intelligence Jobs

Design and build enterprise data warehouse, scalable data pipelines, integrations (OneStream, Workday, Oracle, Databricks), and AI/agent solutions (RAG, vector stores). Partner with Finance/HR/Legal/IT to translate workflows into automated, secure, maintainable enterprise data and AI services, and support analytics and reporting.

Lead GPU and HPC cluster architecture and deployment for cloud partners: design end-to-end GPU/NVLink infrastructures, perform performance modeling, validate and debug cluster configurations, support NPI deployments, and serve as technical liaison between engineering, product, and field teams.

Build and optimize local LLM inference and agent runtimes for Windows and GeForce RTX GPUs. Profile and reduce latency/memory, integrate CUDA/TensorRT pipelines, implement sandboxed security and privacy routers, and harden multi-agent orchestration for constrained consumer hardware while collaborating across research, driver, and open-source teams.

The Marketing AI Engineer enhances marketing with AI tools, manages application integrations, tracks AI utilization goals, and fosters team enablement in a 4-month contract role.

Apply LLM-based tools and traditional analytics to operational data: ingest and curate data, automate recurring analyses, tune prompts/agents, deliver decision-support reporting, and convert ad-hoc requests into self-service tools for operations stakeholders.

Build and scale AI-enabled Customer Success workflows and automations across lifecycle, health, renewal, and engagement. Prototype tools, connect data systems, establish prompt/QA processes, gather CSM feedback, and measure impact to improve productivity and consistency.

Design, build, deploy, and operate production AI systems for Operations, including agents, evaluation pipelines, and shared frameworks. Develop advanced algorithms (optimization, forecasting, CV), design APIs/SDKs, ensure reliability/latency/cost targets, mentor junior engineers, and partner with IT/security for production readiness.

Lead Trustly's internal AI strategy and team to evaluate, adopt, and scale foundation models, agentic tooling, and LLM orchestration. Establish governance, partner across functions to prioritize AI features and automations, drive adoption and measurable ROI, and evangelize AI capabilities company-wide.

Design, build, and maintain Trustly's internal AI platform and agentic workflow tooling. Partner with product and engineering teams to embed LLM-powered capabilities, lead prototyping and model evaluations, establish evaluation standards for quality and safety, and create reusable libraries, patterns, and enablement resources to drive internal AI adoption.

Lead design and implementation of AI-ready data platforms and scalable batch/streaming pipelines to support Generative AI and RAG solutions. Own architecture, reliability, governance, observability, and operationalization of production GenAI data systems across cloud environments. Mentor engineers and collaborate cross-functionally to integrate AI data products into enterprise workflows.

Lead and deliver large-scale AI and automation projects from planning through execution. Translate business goals into actionable plans, manage cross-functional stakeholders, risks, and dependencies, drive AI evaluation programs, improve workflows and SOPs, track project metrics, partner with technical teams on automation, and mentor project coordinators.

Lead Applied AI strategy and execution across Employee Benefits and Operations, overseeing ML, generative and agentic AI, RAG, and predictive analytics lifecycles. Drive governance, model risk management, evaluation, deployment, cross-LOB prioritization, talent architecture, and regulatory readiness to improve enrollment, billing, service, and operational efficiency.

Lead and coordinate technical AI and digital transformation projects across data, model, and platform domains. Translate architecture into project plans, manage cross-functional dependencies, enforce CI/CD and observability, oversee testing and compliance (regulatory and security), create runbooks and incident response, and ensure audit-ready evidence and controlled production cutovers.

Lead architecture and hands-on implementation of large-scale Generative AI and agentic systems. Build production-ready RAG, embedding fine-tuning, vector search, and MLOps pipelines on Google Cloud. Advise clients in pre-sales, mentor engineers, and ensure reliability, latency, cost, and responsible-AI controls for high-stakes deployments.

Senior leader responsible for domain and product leadership within a Supply Chain portfolio, leading teams to deliver AI-enabled analytics and decision-centric products (OSA, upstream intelligence, final mile). Drive execution, solution architecture for agentic workflows, coaching, cross-functional alignment, and measurable business impact through applied AI and analytics.

Design AI integrations for government systems, collaborate on SDKs and APIs, and ensure ethical AI deployment while maintaining high standards for code quality.

Design, develop, and deploy AI/Copilot solutions using various Microsoft and AWS technologies, integrating AI search capabilities and ensuring solutions meet security and compliance standards.

Design, build, and deploy AI integrations between frontier models and government systems. Develop SDKs, APIs, developer tools, and production-grade features; partner with stakeholders to prototype, iterate, and ensure safe, transparent, and ethical AI deployment for federal agencies.

Lead design and engineering of enterprise AI-powered automation using n8n as an orchestration platform. Build scalable, secure API-first and event-driven integrations, AI-enabled workflows and agents, CI/CD and governance, observability, and reusable automation frameworks. Provide technical leadership, mentor engineers, and drive platform standards and operational excellence.

Define and drive enterprise AI automation architecture and governance for scalable, secure, API-first and event-driven workflows. Create reusable automation patterns, influence cross-functional roadmaps, mentor engineers, and ensure measurable operational and business outcomes.

Translate complex business workflows into implementation-ready requirements for AI-enabled automation and agentic workflows. Map current/future-state processes, define APIs, triggers, data mappings, acceptance criteria, and governance. Collaborate with AI Automation Engineers, architects, and stakeholders to ensure scalable, auditable, and measurable AI automation solutions and support delivery, testing, and continuous improvement.

Senior product-minded engineer owning end-to-end SaaS product areas: discovery, design, implementation, testing, and iteration. Translate ambiguous goals into workflows and technical plans, make architecture decisions, build polished UX, use and validate AI-assisted development tools, communicate clearly, and drive work independently with cross-functional partners.

Develop, train, validate, and document ML and LLM-based models on clinical datasets, ensure GxP-compliant data pipelines and QA, build Databricks/AWS-based pipelines, create dashboards, collaborate with cross-functional clinical teams, and contribute to scientific publications.

Lead and grow the engineering team owning AI Runtime (AIR) for managed GPU training. Define product and technical roadmap, drive architecture for distributed training at scale, build reliability and observability for multi-node long-running jobs, collaborate across product, platform, research, and customers, and hire and mentor engineering talent.

Build end-to-end AI solutions for GTM teams: define problems, design prompts, deploy and monitor LLM-powered applications, create workflow automations (e.g., Slackbots), partner with stakeholders to validate and drive adoption, and contribute requirements and reusable patterns to platform and prompt libraries.