Top Artificial Intelligence Jobs

Lead AI transformation initiatives for strategic Fortune 50–100 customers. Build executive relationships, define AI adoption frameworks, orchestrate cross-functional programs, and demonstrate measurable ROI and P&L impact. Manage customer portfolios, drive value realization, resolve risks and escalations, identify expansion opportunities, and develop scalable transformation playbooks and communications.

Own customer activation and platform success across a portfolio by translating business objectives into AI workflows, adoption strategies, champion programs, dashboards, workshops, and training. Build stakeholder and power-user communities, deliver customized educational experiences, connect usage to measurable outcomes and ROI, and continuously improve enablement through feedback and adoption data. Partner with transformation and curriculum teams while advising customers on scaling AI programs.

Own customer activation and deployment success across a portfolio by translating business objectives into AI workflows, adoption strategies, champion communities, workshops, training, and measurable outcomes. Build stakeholder relationships, demonstrate ROI, customize educational experiences, and collaborate cross-functionally to improve materials and customer engagement. The hybrid role is based in San Francisco or Seattle and may require 25% domestic and international travel.

Build secure, scalable generative AI services, applications, APIs, microservices, and agentic workflows for enterprise customers. Own system architecture from proposal through deployment, develop React and TypeScript interfaces, and collaborate with product, data science, and DevOps teams. Implement continuous deployment, monitoring, logging, security, and reliability practices while leveraging cloud platforms, containers, vector databases, and modern AI development tools. Mentor engineers and take ownership of resilient production code.

Partner with Fortune 500 customers to identify AI use cases, architect and implement custom applications, templates, and integrations, and guide enterprise adoption. The role uses Python, APIs, generative AI, and Knowledge Graph capabilities while translating complex technical concepts for business and executive audiences. It also develops scalable integration processes, documentation, and workflows; provides customer feedback to Product and Engineering; and drives successful outcomes and expansion within strategic accounts.

Partner with enterprise customers to identify AI use cases and deliver tailored applications, templates, and integrations using WRITER’s platform, APIs, and Knowledge Graph. Translate complex AI capabilities into practical recommendations, collaborate with Product and Engineering, improve technical implementation processes, and drive customer adoption, expansion, and measurable business outcomes.

Partner with enterprise customers to identify AI use cases, architect and implement tailored applications, templates, and integrations using WRITER’s platform, APIs, and Knowledge Graph. Translate complex AI concepts into actionable recommendations, collaborate with Product and Engineering, develop scalable integration processes, and drive customer adoption, time-to-value, and measurable business outcomes.

Lead research on large language models and agentic systems from hypothesis through production deployment. Design post-training experiments using SFT, RLHF, RLAIF, DPO, and GRPO; develop evaluation benchmarks, synthetic data pipelines, and adversarial datasets; shape model architecture and training roadmaps; publish at leading venues; and mentor researchers and engineers.

Build and deploy scalable AI applications, intelligent agents, and AI-powered features for enterprise customers. Collaborate with research, data science, and product teams to translate AI research into production solutions. Evaluate emerging AI technologies, develop LLM-based systems, and promote responsible AI through bias detection, explainability, and safety alignment.

Design, develop, test, and support AI-enabled software applications using full-stack technologies, LLMs, AI agents, retrieval systems, APIs, databases, and cloud architectures. Collaborate with engineering and business teams, research emerging AI technologies, prototype solutions, and contribute to testing, documentation, automation, and continuous improvement.

Participate in a five-and-a-half-month applied AI and analytics co-op building production-ready solutions for real business problems. Responsibilities include data gathering and preparation, AI prompt and workflow design, dashboard and model development, trend analysis, workflow automation, agentic prototype development, data-quality improvement, and presenting recommendations to stakeholders. Co-ops work onsite at least three days weekly and collaborate with technical and business leaders across SharkNinja.

Build and operate highly available, cloud-native Voice AI systems, including deployments, observability, monitoring, disaster recovery, and continuous integration. Develop scalable software, integrate LLMs with voice and real-time communication platforms, automate testing and delivery, and collaborate with product owners. Mentor colleagues and promote knowledge sharing across telecommunications and AI engineering disciplines.

Uses AI to support retail sales by preparing prospect briefs, call notes, emails, recaps, proposals, CRM updates, prompts, templates, and team how-to guides. Tests AI tools, verifies information, measures time savings and quality, maintains a prompt library, coaches teammates, and helps protect confidential client data. Works onsite with Account Executives under the mentorship of the Director of Retail Sales.

Lead Lyra Health’s global accounting organization across accounting operations, consolidated reporting, treasury, and financial controls. Build scalable processes, systems, talent, and public-company-ready infrastructure, including SOX compliance and SEC-quality reporting. Drive accounting transformation through standardization, automation, AI, and continuous improvement while strengthening cash management and financial reporting. Partner with senior leadership, develop accounting talent, and ensure compliance with U.S. GAAP and evolving accounting standards.

Senior Product Manager responsible for defining the vision, strategy, requirements, roadmap, and execution of edge AI management capabilities. The role coordinates engineering, data science, infrastructure, sales, marketing, and customers to manage model, data, and configuration deployment across distributed edge environments. Responsibilities include product acceptance testing, documentation, release communications, go-to-market support, market analysis, and KPI-driven prioritization. Candidates need strong AI/ML fluency, distributed systems or edge experience, technical product management expertise, and eligibility for a Secret or higher security clearance.

Lead the architecture, development, deployment, and operation of production-grade AI platforms and applications. Build scalable cloud-native services integrating generative AI, LLMs, intelligent document processing, APIs, microservices, data pipelines, and evaluation frameworks. Provide hands-on technical leadership, mentor engineers and data scientists, establish engineering standards, and ensure AI systems are secure, reliable, observable, compliant, and cost-efficient.

Build and operate enterprise AI platforms across Azure and GCP, including Kubernetes clusters, Terraform infrastructure, CI/CD and GitOps pipelines, GPU workloads, model gateways, vector databases, tracing, and evaluation services. Develop reference agents and LLM application templates, support application teams integrating workloads, troubleshoot platform reliability, automate operations, and create runbooks. The role requires Chicago office attendance three days weekly and up to 20% domestic travel.

Operate and improve a self-hosted AI platform across customer Kubernetes environments. Responsibilities include Helm and GitOps deployments, stateful service reliability, incident response, SLOs, capacity planning, Terraform infrastructure across major clouds, CI/CD automation, security controls, troubleshooting, and customer support. The role also develops runbooks, documentation, and automation while supporting private, air-gapped, and regulated environments.

Lead the architecture and delivery of an enterprise AI platform supporting LLM and agentic workloads. Design multi-tenant Kubernetes infrastructure across Azure and GCP, GPU capacity, Terraform modules, CI/CD, GitOps, security controls, model-serving services, and governance. Mentor a platform engineer, lead architecture reviews, advise client stakeholders, and establish operational standards for production AI infrastructure.

Lead the architecture and delivery of an enterprise AI platform supporting LLM and agent workloads. Design multi-tenant Kubernetes infrastructure across Azure and GCP, GPU capacity, Terraform modules, CI/CD and GitOps workflows, security controls, governance, and shared AI services. Mentor a platform engineer, guide technical decisions, conduct architecture reviews, and advise client stakeholders.

Build and maintain Databricks-based batch and streaming data pipelines for an enterprise AI platform. Responsibilities include lakehouse modeling, governance, data quality, unstructured document ingestion, chunking, embeddings, vector search, and LLM evaluation datasets. The role deploys cloud infrastructure on Azure or GCP, applies testing and CI/CD practices, and partners with AI and platform engineers to deliver secure, reliable data for retrieval-augmented generation systems.

Build and operate Databricks-based batch and streaming pipelines for analytics and enterprise AI workloads. Design lakehouse data models, governance, access controls, quality monitoring, and reliable cloud infrastructure. Develop ingestion and chunking pipelines for unstructured documents, embeddings, vector search, and LLM evaluation datasets supporting RAG systems. Collaborate with AI and platform engineers while applying software engineering practices such as version control, testing, and CI/CD.

Build and operate an enterprise AI platform across Azure and GCP, including Kubernetes clusters, Terraform modules, CI/CD and GitOps pipelines. Deploy and monitor LLM platform services such as model gateways, vector databases, tracing, and evaluation systems. Support GPU workloads, automate operations, create reference agents and LLM application templates, and help application teams integrate and troubleshoot production workloads.

Operate and improve a self-hosted AI platform across customer Kubernetes environments. Responsibilities include deployments, upgrades, stateful service reliability, incident response, SLOs, capacity planning, Terraform infrastructure, CI/CD, security controls, automation, troubleshooting, and customer support. The role also develops Helm charts, runbooks, and documentation while supporting private, air-gapped, and regulated environments across AWS, GCP, and Azure.

Develop and validate AI-assisted power system study workflows for generation and large-load interconnection. Automate model preparation, simulations, contingency and power-flow analysis, data checks, results extraction, and reporting using Python and power-system tools. Validate automated outputs against engineering expectations, planning criteria, reference cases, and manual studies. Collaborate with software, AI, product, and power systems teams while documenting assumptions, methods, limitations, and results.