Top Artificial Intelligence Jobs

Leads company-wide AI transformation by designing distributed AI platforms for data processing, model training, inference, and evaluation. Embeds with autonomous driving teams to deploy AI DevOps and copilot ecosystems, while building LLMOps/MLOps automation, optimizing GPU clusters, reducing inference latency, and establishing long-term AI infrastructure strategy. The role requires hands-on systems engineering, enterprise AI platform experience, cross-functional technical influence, and deep distributed LLM training and inference expertise.

Lead strategy and technical direction for generative and multimodal vision systems. Build end-to-end model workflows, improve realism, controllability, and robustness, drive evaluation and production-readiness, partner with engineering to deploy optimized inference, and mentor scientists while owning a major technical pillar from research to production.

Lead the execution and governance of an enterprise AI portfolio for Wind Engineering: maintain portfolio visibility, track lifecycle stages, manage risks and dependencies, build dashboards and reporting, coordinate cross-functional stakeholders, and apply Lean/continuous improvement to scale AI initiatives from concept to production.

Define and maintain enterprise AI reference architectures, reusable solution patterns, and MLOps/LLMOps standards. Guide subsystem designs for scalability and reuse, update engineering standard work and QMS for AI, partner with Digital/IT on platforms and integrations, mentor Embedded AI Architects, and serve as technical escalation for high-risk AI initiatives across Wind Engineering.

Build internal software and AI-powered systems that connect Product, Engineering, Sales, Marketing, Programs, and Operations. Responsibilities include integrating SaaS tools, APIs, databases, LLMs, and AI agents; automating workflows; creating systems that translate product specifications into engineering work; maintaining synchronized documentation; supporting controlled government proposal workflows; and improving how employees and agents collaborate.

Leads enterprise technology solutioning across business development, presales, architecture, and delivery governance. Builds CXO relationships, translates business challenges into scalable AI and application modernization solutions, develops business cases and roadmaps, supports RFP/RFI responses, and guides complex multimillion-dollar pursuits. The role requires enterprise architecture expertise, cloud and integration knowledge, financial modeling, innovation leadership, and experience closing deals worth $15 million or more.

Embed into engineering intake to standardize cloud cost estimation, translate technical requirements into predictive cost models, track ROI for AI initiatives, and recommend cost-avoidance strategies while building reporting and value-capture frameworks across project lifecycles.

Partner with business leaders to define AI strategy, set KPIs, and deliver executive reporting; prototype AI solutions (e.g., Google AI Studio); remove operational obstacles across ServiceNow, Jira, and automated workflows; enforce data governance and privacy to enable enterprise AI adoption and measurable ROI.

Drive deployment of AI solutions for field services: prepare data governance, integrate knowledge-base chatbots, enable wearable AI and AR onboarding, build agentic "AI manager" workflows, and optimize prompts and automation to improve technician productivity and service quality.

Design, build, and deploy enterprise-grade autonomous AI services and multi-agent systems. Implement MCP and A2A integrations, architect hybrid control flows and state machines, integrate sandboxed tool execution with strict egress controls, and apply SRE principles for observability, fallback mechanisms, and reliability to move generative AI from POC to production.

Lead and grow a team of Agentic AI engineers to transition multi-agent and generative AI POCs into scalable, secure, production-ready services. Define technical roadmap, guide architecture for scalability and cost, champion agent interoperability standards, and establish CI/CD, observability, and delivery practices for LLM-powered applications that drive business impact.

Design, build, and operate secure LLM platforms and RAG pipelines for security analytics and automation. Develop agentic workflows, continuous monitoring, governance, and adversarial testing to mitigate AI risks and integrate protections into security tooling and cloud-native ML pipelines.

Lead the global transition from traditional SDLC to AI-enhanced development by executing the enterprise roadmap, embedding AI governance and security into SDLC gates, establishing KPI dashboards, scaling training and AI Champions, and managing people and culture risks to drive measurable AI adoption and ROI.

Lead and mentor an AI engineering team to design, architect, and deploy production-ready multi-agent, LLM-powered systems. Define technical strategy, set architecture and security standards, implement hybrid control flows/state machines, integrate agent-to-tool and agent-to-agent protocols, and collaborate with product and business teams to translate requirements into scalable microservices.

Serve as the bridge between AI models and field operations to implement AI-driven tools (chatbots, wearables, AR, agentic workflows). Prepare AI-ready data, enforce governance, deploy and iterate knowledge-base chatbots, design AR onboarding, create an "AI manager" for technicians, and build prompt libraries and automation to optimize workflows and improve field technician efficiency and service quality.

Lead AI product strategy for enterprise shared services, prioritize and govern AI requests, and drive deployment of generative AI/LLM and ML solutions. Manage cross-functional teams to convert manual processes into automated workflows, build reusable AI components and datasets, and deliver measurable operational efficiency and cost reductions across internal systems.

Lead AI product strategy and delivery for marketing and field engagement in US Pharma. Build and scale generative and agentic AI solutions, partner with data science and engineering, manage vendor/platform relationships, ensure regulatory and privacy compliance, and drive adoption across commercial teams.

Own the roadmap and end-to-end product strategy for Moloco’s internal AI platform. Define governed access to models, data, and knowledge; enable agents, tools, retrieval, reusable skills, and evaluation; and provide secure hosting, observability, permissions, spend controls, and safety guardrails. Partner with Engineering, Infrastructure, IT, and go-to-market teams while serving technical and non-technical builders. Translate ambiguous, cross-functional needs into an adoptable platform that informs both internal workflows and customer-facing products.

Manage AI project planning and execution within scope, timeline, budget, quality, compliance, and financial guardrails. Coordinate staffing with line and resource managers, track execution and compliance metrics, report status to stakeholders, manage risks and escalations, conduct quality reviews, and mentor project management team members. The role requires cross-functional leadership, problem solving, stakeholder influence, and adherence to company standards of business conduct.

Build and maintain automated testing frameworks for distributed AI training and inference workloads. Develop end-to-end integration and stress-testing plans covering distributed systems, GPU kernels, AI frameworks, and hardware-software boundaries. Collaborate with systems engineers to troubleshoot failures in large-scale clusters, using telemetry and profiling tools to identify root causes and performance bottlenecks. The role also supports containerized test environments and CI/CD workflows.

Support customer onboarding, workflow discovery, implementations, testing, and ongoing service needs. Configure and launch workflows in the Upwell platform, investigate operational issues, document processes, coordinate with internal teams, and translate customer needs into product requirements. The role is customer-facing and involves managing multiple projects, improving processes, and using AI-enabled automation. No prior freight, logistics, AI, or software engineering experience is required.

Builds developer and customer relationships across the Jetson ecosystem, guides partners through embedded Linux and Jetson software integration, promotes NVIDIA AI libraries and edge AI capabilities, supports production deployments, and communicates developer feedback to engineering and product teams to shape platform roadmaps, reference designs, positioning, and adoption.

Lead product vision and strategy for an end-to-end AI-powered software factory. Integrate planning, coding, review, and verification across GitLab Duo Agent Platform and product areas. Collaborate with engineering, design, and GTM teams, identify gaps and dependencies, present recommendations to executives, and drive execution to deliver cohesive developer workflows.

Lead Squarespace's partnership strategy for AI discoverability and the Acuity Scheduling ecosystem. Build and manage strategic partner relationships, influence product roadmaps, drive ecosystem integrations with AI platforms, define KPIs and commercial terms, and coordinate cross-functional teams to scale partner initiatives and measure business impact.

Build and lead development of an agentic AI platform for developer productivity: design, implement, test, and ship features; use and advocate AI tooling; experiment with AI approaches; mentor engineers; ensure quality, scalability, and cross-team collaboration.