Top Artificial Intelligence Jobs

Build backend services, AI agent runtimes, machine learning infrastructure, vector search systems, data acquisition pipelines, model-hosting endpoints, quality measurement workflows, and internal platform tooling. The role also includes security assessments and vulnerability mapping. Candidates should be comfortable solving ambiguous problems independently, developing dataset and synthetic-data pipelines, and maintaining high standards for simple, dependable engineering.

Build and own AI-driven applications across backend and user-facing systems as an early full-stack engineer. Partner with product, design, and engineering teams to architect intelligent solutions, establish engineering practices, shape technical strategy, define success metrics, and drive technical projects from concept through delivery. The role requires AI product development, agent frameworks, LLM APIs, Python, SQL/PostgreSQL, cloud deployments, containerization, CI/CD, and Git experience.

Lead the end-to-end development of agentic AI products for workforce management, from operational research and agent definition through prototyping, evaluation, enterprise deployment, and commercialization. Define human-in-the-loop controls, tools, escalation rules, specifications, and release criteria. Build prototypes, prompts, evaluation sets, and code contributions while diagnosing model and system failures. Partner with enterprise design customers, engineering, AI Lab, executives, and go-to-market teams to deliver reliable, compliant products.

Lead global partner go-to-market strategy for AI consumption within Microsoft’s CSP channel. Translate business goals into partner execution plans, incentives, activation programs, readiness resources, targeting strategies, and field guidance. Collaborate with product marketing, sales, finance, operations, and partners to define KPIs, remove execution blockers, gather partner feedback, and accelerate AI consumption growth among SMB and corporate customers.

The HPC & AI Performance Engineer will execute benchmarking projects, analyze performance, and optimize HPC systems using scientific software and methods.

Source and recruit talent across Sales, Client Success, Shared Services, Product, Engineering, and other functions. Build candidate pipelines using job ads, sourcing tools, and LinkedIn Recruiter; create compelling outreach; track sourcing metrics and conduct A/B testing; communicate progress to hiring managers; and coordinate candidate interviews. The role requires 5+ years of recruiting experience, strong organization, data-driven sourcing, and the ability to attract passive candidates to a growing AI company.

Build and operate production AI agent systems for IT services. Responsibilities include developing integrations, APIs, agent workflows, agent harnesses, backend systems, data pipelines, and model-adjacent infrastructure. The role involves embedding with portfolio teams, translating ambiguous field problems into reliable software, partnering with stakeholders, scaling deployments, and designing evaluations to improve agent quality. This is an in-office position in San Francisco or New York City.

Lead the strategy, architecture, and team responsible for production AI agents and intelligence systems for clinical and operational workflows. Own the Latent Agent harness, evaluation systems, shared AI foundations, reliability, monitoring, and service-level objectives. Guide build-versus-buy and model development decisions, partner with Product and business leaders, and hire and develop a high-performing applied AI organization focused on measurable outcomes.

Build and deploy AI agents that automate business workflows, executive reporting, and team operations. Partner with product teams to shape AI capabilities, transform workflows across the company, and provide data-backed insights to the CEO and executive leadership. Define AI-native operating models involving automated reporting, predictive metrics, LLM-driven communications, and agentic workflows.

Deploys and sustains secure Linux-based infrastructure for AI/ML and LLM applications. Manages containerized environments, cloud deployments, CI/CD workflows, automation, enterprise integrations, and security configurations. Troubleshoots complex production environments and collaborates with software, platform, AI/ML, and cybersecurity teams. Develops operational tooling, technical documentation, deployment procedures, and runbooks while supporting reliable AI platform capabilities in secure enterprise environments.

Develop and integrate AI/ML capabilities for satellite mission planning, space domain awareness, and resilient operations. Responsibilities include end-to-end machine learning, sensor-data classification and tracking, anomaly detection, scalable HPC pipelines, modeling and simulation integration, algorithm optimization, and technical assessments. The role requires collaboration with satellite designers, operations planners, and subject-matter experts in a fully onsite classified environment.

Lead technical engagements with OEM and deployment partners to design, validate, and deploy scalable NVIDIA AI infrastructure. Map customer workloads to NVIDIA hardware and software, co-design architectures, resolve complex technical issues, drive platform adoption, and influence product priorities. Lead AI Factory build-readiness execution, deployment validation, technical enablement, and issue resolution across large-scale GPU infrastructure deployments. Travel up to 50% is required.

Leads AI discovery, prototyping, and enablement across Holland America Line and Seabourn. Translates business problems into AI use cases, builds practical generative AI tools and workflows, evaluates value and risk, and supports governance, production readiness, adoption, and executive decision-making. Develops playbooks, standards, training content, and operating processes while coaching analysts and partnering with IT, security, privacy, data, architecture, and business leaders.

Leads AI discovery, prototyping, enablement, governance, and operating-model development across Holland America Line and Seabourn. Manages AI analysts, translates business challenges into practical AI use cases, builds prototypes such as agents, copilots, automations, and decision-support tools, and evaluates value, risk, scalability, and adoption. The role also develops playbooks, training content, standards, and portfolio recommendations while partnering with IT, security, privacy, architecture, PMO, and business leaders to move experiments toward responsible implementation.

Leads product definition and development for next-generation AI hardware supporting Azure training and inference workloads. Identifies customer needs, market opportunities, and competitive gaps; translates them into product requirements, roadmaps, investment proposals, and hardware strategies. Drives technical programs from concept through execution while collaborating with architecture, engineering, business teams, customers, and senior leadership. Requires expertise in AI systems, GPU architecture, networking, data centers, reliability, and cloud infrastructure.

Own the architecture and operation of a secure, scalable GCP platform for production AI agents. Design networking, identity, service perimeters, secrets management, Terraform modules, CI/CD pipelines, LLM access, observability, and cost controls. Lead incident response, postmortems, provider relationships, and platform governance while enabling product teams to deploy independently and safely.

Partners with sales executives to provide technical pre-sales support, lead proof-of-concept processes, deliver customer presentations, architect cloud security solutions, and help close opportunities. Maintains product expertise, supports events and technical working sessions, updates CRM records, collaborates with sales and engineering, and contributes to revenue targets. Requires extensive sales engineering and cloud security experience, strong presentation skills, and familiarity with major cloud platforms and security solutions.

Leads AI discovery, prototyping, enablement, governance, and operating-model development across Holland America Line and Seabourn. Translates business problems into AI use cases, builds practical tools and workflows, evaluates value and risk, develops playbooks and training, and partners with technology, security, privacy, data, and business teams to support responsible adoption and production handoff. Manages AI analysts and advises leadership on strategy, portfolio priorities, emerging trends, and scalable practices.

Own the model portfolio for a large-scale consumer AI platform, treating models as product lines. Define model tiering, launch processes, configuration standards, SLAs, deprecation policies, and reliability practices. Track provider changes, pricing, releases, inference metrics, evaluations, usage, and customer feedback to shape product strategy and roadmap recommendations. Partner cross-functionally with Engineering, API, BI, Product, and Marketing to make strategic decisions and deliver them.

Owns the strategy, roadmap, execution, launch, adoption, and continuous improvement of an AI security product domain within Cisco AI Defense. Partners with customers, engineering, design, security research, marketing, sales, and leadership to translate enterprise security and AI needs into scalable SaaS capabilities. Defines outcomes, prioritizes investments, guides technical trade-offs, and drives customer value across the product lifecycle.

Create and structure AI literacy curriculum, research instructional frameworks, develop course materials and assessments, publish content through LearnPress, and refine lessons using participant feedback. The intern will work remotely with the Founder and Web Manager Intern for 5–10 hours weekly over three months. No prior curriculum design experience is required; reliability, curiosity, and authorization to volunteer in the United States are required.

Designs, develops, and maintains production-ready AI/ML applications for intelligence missions, focusing on LLM integration, agentic workflows, and RAG pipelines. Builds Python services, REST APIs, backend components, database workflows, and enterprise integrations. Optimizes AI systems for performance, scalability, reliability, and usability while evaluating model effectiveness and applying software engineering best practices. Requires collaboration with technical stakeholders to deliver mission-focused capabilities.

Lead Verizon’s AI infrastructure networking product growth strategy from market identification through commercialization and revenue expansion. Develop ecosystem partnerships with hyperscalers, GPU providers, data center operators, colocation providers, OEMs, and infrastructure partners. Build pricing models, business cases, capacity commitments, and multi-year agreements for optical, fiber, programmable networking, and data center connectivity services. Collaborate with engineering, finance, legal, sales, and product teams while tracking pipeline, contracts, revenue conversion, and partner performance.

Design and build a scalable agentic AI framework integrating MCP, A2A communication, machine learning, data pipelines, APIs, and orchestration technologies. Develop SDKs and tooling, support secure multi-tenant deployments, evaluate emerging AI and LLM technologies, and mentor engineers. The role requires production-grade AI and data platform experience, strong Python skills, distributed systems knowledge, API development, Kubernetes, Docker, security, IAM, and enterprise AI architecture expertise.

Provides data privacy counseling to clients, including drafting retention policies, privacy and data processing agreements, incident response plans, breach notifications, and AI-related guidance. Assesses and mitigates data risks, supports breach response matters, and monitors privacy, AI, and cybersecurity regulations. Requires legal research and writing skills, CIPP certification, relevant privacy experience, and admission to a state bar.