Top Artificial Intelligence Jobs

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.

Lead growth marketing for Dave & Buster's and Main Event, developing multi-channel acquisition and retention strategies, managing budgets and agencies, building a growth team, leveraging analytics/AI for optimization, and driving same-store sales and KPIs.

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, and evaluation. Collaborate with data and infra teams, drive architecture and inference decisions, and provide technical leadership and mentorship.

Lead and build an Applied AI organization that turns PSI's AI and physics research into customer-facing solutions. Hire and manage teams, scope and deliver engagements, integrate research and engineering, stay hands-on technically, and partner with sales to drive revenue while ensuring reusable, trustworthy deliverables.

Lead Core AI to build models, agents, and training systems that enable AI-driven physics discovery. Set roadmap, own research portfolio and infrastructure, hire and coach top talent, stay technical as a player-coach, and ensure results are scientifically trustworthy and production-ready for Applied AI and Engineering teams.

Lead design, prototyping, and scaling of LLM-enabled AI capabilities for drug discovery research. Partner with scientists and engineers to build agentic systems, evaluation frameworks, grounding and provenance infrastructure, and reusable AI building blocks that improve research decision-making and scientific quality.

Senior MLOps & Generative AI Engineer to design, build, and operate scalable ML infrastructure and production GenAI systems. Responsibilities include CI/CD for ML, orchestration, RAG/vector search, LLM orchestration, monitoring, governance, secure integrations with healthcare systems, and optimization of inference and platform performance.

The Principal Scientist will drive AI integration in oncology target discovery, analyze multimodal datasets, and lead cross-functional collaborations to enhance decision-making in drug development.

Build, harden, and deploy generative AI and low-code/no-code workflows to solve field operational needs. Evaluate AI outputs for quality and safety, document and govern solutions, manage incoming requests, and escalate complex builds to engineering while iterating with fast feedback loops.

Work with clients' compliance and risk teams to define data needs, build data pipelines, analyze structured and unstructured data, and develop ML/GenAI solutions (LLM-based systems, predictive models, dashboards). Deploy and fine-tune models, design prompting strategies and agentic systems, ensure regulatory compliance, and support business development and internal knowledge sharing.

Own and expand a portfolio of strategic AI-native accounts by driving ARR growth, prospecting named AI labs, leading complex multi-stakeholder enterprise sales cycles, running POCs with Solutions Consultants, and coordinating cross-functional teams to deliver technical and commercial value.

Lead strategy and implementation of an advanced Quality Management system, AI-driven design tools, and Lean engineering across Heavy‑Duty New Unit teams. Drive KPIs, reduce rework and cycle time, mentor a medium team, champion cultural/process change, and transition engineering workflows to agile, data-driven execution.

Design, develop, and maintain microservices-based, AI-enabled software for safety-critical air-traffic systems. Work in Agile/SAFe teams to implement CI/CD/DevSecOps practices, integrate with databases, messaging, and external APIs, use AI-assisted tools, and support testing, performance tuning, and reliable delivery while meeting Public Trust and U.S. citizenship requirements.