Top Artificial Intelligence Jobs

Design, develop, and maintain engineering solutions to validate, monitor, and operate GPU clusters for ML researchers. Research and apply AIOps/Agentic AI to reduce operational toil, enable self-service reliability and performance improvements, and participate in on-call support for platform systems.

Lead end-to-end technical research into how AI models perceive and rank brands. Build and own research stack, design experiments, produce clean datasets and actionable insights, author credible content with Editorial, create scalable research playbooks, and partner with Product and Engineering on platform performance.

The Staff AI Research Engineer will architect AI systems, optimize retrieval methods, collaborate across teams, and innovate AI solutions for sales efficiency.

Work with EPD and Sales to design AI-native financial modeling workflows, create standardized templates, perform high-rigor QA on AI outputs, and pressure-test edge cases to ensure investor-grade financial models and automation reliability.

Build and maintain enterprise-scale ML/LLM infrastructure on Azure: create automated, secure CI/CD pipelines, provision resources with Terraform, enable real-time inference and RAG pipelines, and collaborate with data science and product teams to productionize generative AI solutions.

Client-facing AI engineer who develops and deploys generative AI/LLM solutions, collaborates with data scientists and engineers, leads cross-functional projects, and delivers technical solutions and presentations to clients.

Develop and maintain machine learning models and pipelines as part of a cross-functional data team. Build/operate ML platforms, use data processing frameworks, apply software engineering practices, obtain Security+ and support classified (Top Secret/SCI) environments while working onsite in Oahu with travel up to 30%.

Design and prototype LLM- and agent-based workflows (RAG, tool-calling), build reusable connectors and agent templates, integrate data science models, define clean API boundaries, implement lightweight evaluation/logging, and collaborate with data science and leadership to operationalize AI systems.

Design, deploy, and operate production AI systems for enterprise customers. Build compound workflows combining models, agents, retrieval, and evaluation. Measure and iterate on live systems, integrate with customer data and APIs, and collaborate with stakeholders to ensure reliability and business impact.

Design, build, and operate end-to-end AI systems in enterprise environments. Own architecture, integrations, security, observability, and operational patterns; deliver reference implementations and resolve high-impact production issues while partnering closely with customer engineering and security teams.

Design, build, and own the end-to-end model platform: training/fine-tuning pipelines, RL workflows, data ingestion/curation, reproducible experiments, scalable inference services, and production deployment.

Lead AI integration and full-stack development across the platform, rapidly prototype and ship production-quality features using Node.js, React, TypeScript, Ruby, GraphQL and Postgres, ensure secure handling of healthcare data, set technical standards, and mentor engineering team members.

Design, build, and deploy generative AI applications and agentic workflows using LLMs. Develop orchestration, tool-use, memory, and SDKs; partner with stakeholders to create AI copilots; ensure scalability, governance, and security with platform and MLOps teams.

Build, deploy, and productionize generative AI applications and agentic systems, including CI/CD pipelines, monitoring, and governance. Integrate AI solutions with enterprise systems, prototype and scale copilots and multi-agent workflows, and optimize pipelines for performance, cost, and scalability.

Lead design and implementation of defensible Generative AI and ML solutions for eDiscovery. Consult with clients, optimize models, ensure AI auditability/QC, train internal teams, and support KPI reporting across litigation workflows.

Apply AI/ML and data-driven techniques to accelerate NAND flash controller design verification. Build models and tooling for failure triage, anomaly detection, and root-cause analysis, integrate ML solutions with verification pipelines, and collaborate with design, DV, firmware, and infrastructure teams to improve debug efficiency and coverage closure.

Develop production-ready code in Python and TypeScript for AI applications, optimize AI performance, integrate enterprise data, and ensure security through testing.

Lead Thermo Fisher's enterprise AI-driven search and discoverability strategy. Manage multi-disciplinary teams, set governance and standards for generative search, oversee technical search health, direct AI-ready content, build visibility monitoring and reporting, partner across Product, Digital, IT, Analytics, and Media, and manage external vendors to improve visibility and accuracy across search and LLM-driven experiences.

Design, build, and implement conversational AI solutions (IVR, speech analytics, quality management). Gather requirements, perform no-code development, analyze performance metrics, document processes, enable partners, and recommend process improvements to drive scalable customer-focused solutions.

Lead technical strategy and delivery for a 12-month AI Platform SRE transformation: align executives, define SRE operating model, guide architecture and tooling, track reliability and automation metrics, and oversee phased vendor-to-client transitions.

Lead and grow the AI Platform team to deliver shared AI/ML services, infrastructure, and tooling. Define platform vision with the architect, enforce standards for reliability, security, and observability, and collaborate with cross-functional teams to enable secure, self‑service developer capabilities.

Build production-ready intelligent automations and agentic AI solutions. Full-stack development using TypeScript or Python, LangChain, vector DBs, RAG, and tool-calling. Collaborate with clients, ensure maintainable, tested, scalable deployments, CI/CD, observability, and model fine-tuning or deployment.

Manage AI intake and triage via ServiceNow, support AI risk/impact screenings and inventory, develop change management and training materials, monitor operational metrics, and collaborate with legal, data governance, and technology stakeholders to operationalize AI governance.

Lead AI & Commerce Foundations for a B2B e-commerce platform: own platform architecture (auth, APIs, payments, loyalty), define LLM integration strategy, prioritize partner-facing features, run zero-to-one delivery, collaborate with engineers and data scientists, and use data and partner feedback to drive product decisions and rapid shipping.

Lead the AI & Commerce Foundations team to define AI strategy and own core platform architecture (auth, APIs, loyalty, payments). Integrate LLMs to create partner value, prioritize and ship partner-facing B2B features, run experiments, and collaborate with engineers, data scientists, and partner success to deliver MVPs and iterate based on data and customer feedback.