Top Artificial Intelligence Jobs

Serve as the first engineer and technical leader, building Leland Care’s healthcare platform from scratch. Own architecture, data systems, ingestion pipelines, AI intelligence capabilities, agentic coordination systems, and compliance infrastructure. Make high-leverage technical decisions, ship rapidly, and manage outcomes end-to-end in an ambiguous 0-to-1 environment.

Own and close enterprise sales in the US, managing the full cycle from prospecting and pipeline generation through pilots, business cases, negotiation, and contracts. Build relationships with enterprise decision-makers, technical teams, system integrators, and advisory firms. Operate independently without SDR support or inherited pipeline, navigate complex multi-stakeholder evaluations, represent Nebuly at industry events, and help establish early US customer and partner relationships.

Embed AI solutions across legal and corporate affairs to remove workflow bottlenecks, build rapid prototypes for contract analysis and regulatory tracking, enforce data governance, and deliver executive reporting and KPIs to drive legal operations efficiency and risk mitigation.

Partner with HR leaders to define AI strategies, build KPIs, and deliver ROI. Design and deploy generative AI prototypes (e.g., Google AI Studio), integrate across ServiceNow, Jira, and HRIS, enforce data governance and compliance, automate executive reporting and workforce analytics, and translate technical solutions for non-technical HR stakeholders to accelerate AI adoption.

Own product areas end to end, from identifying claims-related problems through shipping solutions and measuring impact. Work closely with claims adjusters, customers, Engineering, Sales, and Claims teams. Identify and validate AI applications, prototype and test solutions with users, use data to guide decisions, and deliver improved customer outcomes. The role requires hands-on product leadership in a fast-scaling, AI-focused insurance claims environment.

Lead the engineering team to develop Tulip's new agent-native app-building product, managing technical architecture and fostering AI-driven development practices.

Leads an Agile Release Train delivering an agentic AI web application across product, engineering, AI/ML, architecture, security, QA, DevOps, and operations teams. Facilitates PI Planning, dependency and risk management, release readiness, production deployment, metrics, and continuous improvement. Coordinates AI experimentation and production delivery while addressing model reliability, safety, latency, cost, integrations, observability, and operational risks. Serves as the execution integrator between technical teams, product leadership, and enterprise stakeholders.

Evaluate emerging dual-use technologies for technical maturity, feasibility, scalability, integration risk, and commercial or government applications. Provide technical input to investment and accelerator decisions, advise on product direction and roadmaps, support technology transition, and participate in reviews with leadership, industry, government, and innovation partners. The role leverages operational perspective, technical judgment, field experience, and mission-driven problem-solving to connect technical capabilities with real-world applications.

Leads Nationwide Technology’s risk-based internal audit and advisory portfolio, covering IT general controls, application controls, cybersecurity, cloud, data, resilience, and emerging technology risks. Manages and develops 4–10 audit professionals, prioritizes engagements, oversees audit testing and reporting, communicates findings to senior leaders and the Audit Committee, and monitors remediation. The role also advances continuous auditing, analytics, automation, and digital audit practices.

Build and maintain internal agentic AI platforms and developer-facing tools that integrate LLMs with engineering workflows. Partner with product and infra teams to design standards for agent deployment, reliability, evals, security, onboarding, and adoption measurement to improve developer productivity.

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Lead full‑stack development of React Native mobile apps and Node.js backends for digital therapeutics. Own architecture, build CI/CD and AWS infrastructure, design multi‑agent AI workflows, mentor engineers, and resolve cross‑cutting production issues.

Lead product strategy and execution for signals exploitation and tracking capabilities. Define roadmaps, shape backlogs with customers and product owners, support architecture and design reviews, and drive development and demonstration of advanced algorithms and software for DoD and Intelligence Community missions. Balance reuse, integrated system offerings, and component supply-chain opportunities while leading engineering teams through the Agile lifecycle and supporting technical marketing and business development.

Own Firecrawl’s commercial and product legal function, negotiating enterprise, data-licensing, vendor, and partnership agreements. Advise sales, BD, product, and engineering on AI, data access, privacy, intellectual property, and regulatory matters. Build contracting templates, playbooks, approval processes, policies, and compliance programs while managing outside counsel and independently handling novel legal issues.

Own customer support tickets from initial response through resolution, troubleshoot hardware and software issues, coordinate repairs and escalations, analyze support data for recurring problems, and develop proactive self-service systems. The intern will build deep product knowledge across robotics, AI, hardware, software, and the Matic app while creating AI-powered support tools and collaborating with Engineering, Product, Marketing, and Operations.

Lead end-to-end development of a greenfield enterprise platform for software dependency risk management. Build Java services, APIs, data ingestion pipelines, dependency analysis capabilities, and developer-facing features on AWS. Guide engineers and teams in secure AI-assisted development, code quality, testing, governance, and operational stability. Provide technical direction, influence architecture and product decisions, support production systems, and collaborate with product managers and designers to deliver scalable platform capabilities.

Design, build, productionize, and operate supervised and unsupervised ML models, LLM solutions, risk features, and Databricks data pipelines for financial crime monitoring. Develop AI feedback and accuracy measurement, monitor model drift, bias, and performance, and support enterprise-grade ML applications in production. Collaborate with product, engineering, SRE, data science, compliance, and operations teams using agile processes.

Lead engineering for production-grade agentic AI and LLM platforms. Design AWS cloud-native services, RAG solutions, vector storage, agent memory, APIs, and microservices. Establish MLOps, monitoring, governance, security, observability, and responsible AI practices. Provide technical direction, make architecture decisions, review code, mentor engineers, and drive adoption of AI-assisted development standards across teams in a regulated enterprise environment.

Conducts research on algorithms for large language models and agentic AI systems, improving learning efficiency, inference performance, accuracy, latency, and compute cost. Develops theoretical foundations, implements performant software, validates methods at scale, solves problems for internal teams, publishes findings, presents research, and contributes to intellectual property protection.

Design, develop, test, deploy, and support production AI systems, including foundation-model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI training and inference for scalability, latency, throughput, hardware utilization, and cost. Shape the technical vision and roadmap for foundational AI platforms while partnering with engineers, researchers, program managers, and product leaders. Mentor engineering teams and influence senior cross-functional stakeholders.

Designs, develops, tests, deploys, and supports foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Develops optimization techniques to improve scalability, latency, throughput, hardware utilization, and cost. Partners with engineering, research, product, and program teams; contributes to technical vision and roadmaps; and mentors teams while applying emerging AI research to production systems.

Leads FP&A and strategic finance for enterprise AI initiatives, including forecasting, budgeting, financial analysis, funding prioritization, roadmap planning, and executive reporting. Manages and develops a finance team, partners with product, engineering, strategy, and finance leaders, identifies cost drivers and business opportunities, and recommends actions based on analytical insights. Drives projects, process improvements, automation, and cross-functional decision-making.

Own and negotiate Firecrawl’s commercial agreements, including enterprise contracts, data licenses, vendor agreements, NDAs, and partnerships. Advise sales, business development, product, and engineering on data rights, privacy, security, intellectual property, indemnification, and liability. Build legal templates, playbooks, fallback positions, and approval processes while supporting compliance initiatives. Operate autonomously, escalate novel issues appropriately, and provide practical business-focused legal guidance in a fast-moving AI infrastructure company.

Define Order.co’s company-wide data and applied AI architecture, standards, governance, and investment strategy. Build production AI systems spanning data platforms, retrieval, model serving, evaluation, guardrails, and agentic workflows. Partner with the Head of Data, product, engineering, and operations leaders to fund and deliver initiatives that improve company KPIs. Mentor senior contributors, establish reusable engineering patterns, and ensure AI systems meet security, reliability, observability, privacy, and operational requirements.

Designs, deploys, maintains, and troubleshoots HPE NonStop payment infrastructure, secure key managers, Etinet servers, and related subsystems. Uses AIOps and AI-assisted practices for incident triage, anomaly detection, automation, documentation, and reliability improvements. Partners with SRE, observability, and cybersecurity teams to govern AI-enabled monitoring, maintain auditability, execute upgrades, and produce operational knowledge such as runbooks and troubleshooting guides.