Top Artificial Intelligence Jobs

Design and prototype AI-powered solutions that transform business processes. Partner with business and technology leaders to identify automation opportunities, translate requirements, develop proof-of-concepts and minimum viable products, and define performance metrics. Serve as a technical liaison, document solutions, train users, support adoption, and mentor future AI practitioners while applying LLMs, AI agents, machine learning, NLP, and data modeling.

Design, develop, and deploy enterprise AI applications and agentic solutions using LLMs, RAG, intelligent automation, and machine learning. Build data pipelines, APIs, microservices, and reusable AI components with Python and SQL. Collaborate with architects, data scientists, product owners, and engineering teams in a regulated financial-services environment. Lead multiple AI use cases, oversee AI engineers within a Center of Excellence, document solutions, and evaluate emerging AI technologies.

Intern will develop software for Zebra handheld mobile computers, integrating Android telemetry, sensor data collection, machine-learning datasets, and multimodal demo applications using RFID, vision, barcode, and camera capabilities. The role also involves building LLM-powered autonomous agents, supporting Android platform development, strengthening technical and leadership skills, and collaborating on team assignments. This hybrid internship requires at least three days per week in the Holtsville, New York office.

Lead end-to-end operations for large-scale AI training supercomputers, coordinating engineering, datacenter, business, and customer teams. Manage infrastructure availability, training reliability, incident response, repair and recovery, maintenance, operational readiness, SLAs, and customer commitments. Identify operational risks, lead recovery from complex issues, and convert lessons learned into scalable improvements in observability, automation, tooling, and fleet operations.

Own the strategy, roadmap, and execution for Okta’s AI agent identity products. Define capabilities for agent authentication, delegated authorization, dynamic permissions, lifecycle management, and auditability. Partner with engineering, design, architecture, and stakeholders to launch secure, scalable features based on OAuth/OIDC and emerging AI frameworks. Engage customers, monitor market trends, analyze product data, and support marketing and technical sales as the AI identity subject matter expert.

Leads and scales a forward-deployed engineering team delivering agentic AI solutions from customer discovery through production and optimization. Owns customer outcomes, staffing, technical quality, reusable assets, executive relationships, and prototype-to-production conversion. Partners with Sales, Product, Engineering, Services, and Customer Success to shape engagements, influence the roadmap, and drive adoption. The role requires hands-on software engineering, LLM and enterprise integration expertise, executive communication, commercial judgment, and regular customer travel.

Leads the design, implementation, optimization, governance, and evaluation of complex AI solutions, agents, and automations. Partners with business leaders to deliver AI-enabled healthcare experiences, workflows, and operational improvements. Serves as an AI subject matter expert, researches emerging capabilities, establishes best practices, supports responsible AI and risk mitigation, leads cross-functional initiatives, and mentors less experienced analysts.

Design and build distributed backend systems and AI agent frameworks for mission-critical power-grid management. Develop ML production infrastructure, model serving, inference pipelines, orchestration layers, and real-time feedback loops. Lead architecture, testing, CI/CD, performance tuning, observability, scalability, fault tolerance, and security. Collaborate with data scientists, power systems engineers, and DevOps teams to deploy AI solutions in control-center environments.

Facilitate AI literacy, college readiness, career exploration, and leadership workshops for middle and high school students. Mentor student leaders, support college volunteers, connect scholars with internships and academic opportunities, and help organize family events. Maintain attendance, opportunity, survey, and contact records; assist with reporting and approved AI-enabled workflow improvements. Collaborate with schools, families, employers, and community partners under staff supervision while following student privacy, confidentiality, and AmeriCorps requirements.

Design, build, and deploy secure end-to-end web applications integrating AI and large language models in a Department of War environment. Develop frontend interfaces, backend APIs, databases, vector search, and Retrieval-Augmented Generation workflows. Containerize applications with Docker or Podman and support deployment in restricted or air-gapped environments. The role may also involve local open-weight model deployment, runtime optimization, AI orchestration, guardrails, and evaluation.

Evaluates enterprise workplace AI platforms, including capabilities, integrations, security, governance, deployment, pricing, and licensing. Completes vendor training and certifications, maintains platform comparisons, presents findings, supports pre-sales discovery and solution scoping, and assists with manufacturer partnerships and AI portfolio enablement. Collaborates across consulting practices to map platforms to client needs and help accelerate AI solution sales.

Provide L1 customer and technical support by monitoring tickets, troubleshooting basic hardware and software issues, meeting SLAs, documenting resolutions, escalating problems, and communicating clearly with customers. Use ChatGPT and Microsoft Copilot to research solutions, summarize information, improve documentation, and work independently with minimal supervision. Rotational shifts, including nights, weekends, and holidays, may be required.

Develop and deploy machine learning models, datasets, algorithms, and training recipes to improve AI safety for multimodal LLMs and agentic systems. Work across LLM security, frontier risks, agentic safety, multi-turn evaluation, content safety, hallucinations, and fairness. Apply post-training methods including SFT and RLHF/RLAIF, conduct safety evaluations and research, and collaborate cross-functionally with engineering, data science, and research teams.

Build, test, maintain, and support enterprise AI platform capabilities, including APIs, backend services, workflows, automation, integrations, and governed generative AI solutions. Contribute to prompt management, retrieval-augmented generation, model routing, evaluation, observability, secure data access, platform onboarding, testing, documentation, monitoring, incident resolution, and responsible AI practices. Collaborate with engineers, architects, product owners, security teams, data teams, and business stakeholders to deliver reliable production services.

Define AI product vision, strategy, and roadmaps; translate business needs into scalable AI architectures; and lead products from ideation through deployment. The role combines product management, enterprise architecture, software engineering, and technical leadership across Generative AI, LLMs, machine learning, cloud platforms, APIs, and microservices. Responsibilities include stakeholder engagement, backlog prioritization, architecture reviews, governance, security, Responsible AI practices, emerging technology evaluation, and cross-functional team leadership.

Lead the organization’s Generative AI strategy, architecture, development, and production deployment. Build scalable solutions using LLMs, RAG, AI agents, prompt engineering, vector databases, and model adaptation. Establish standards for evaluation, security, governance, monitoring, and responsible AI. Lead technical teams, mentor engineers, translate business needs into AI applications, manage proofs of concept, collaborate with stakeholders, and support client engagements and presales activities.

Design and optimize high-performance AI compute kernels for GEMM, attention, normalization, convolution, and related primitives. Improve throughput, latency, memory behavior, and precision across heterogeneous hardware. Integrate kernels with Triton and PyTorch, profile workloads using performance tools, evaluate mixed-precision formats, and collaborate with compiler, runtime, and hardware teams on ISA and memory architecture co-design.

Build and operate reliable, scalable cloud infrastructure for AI workflows, advertising operations, and data systems. Responsibilities include designing asynchronous and event-driven systems, maintaining CI/CD and infrastructure automation, improving observability, security, performance, and cost efficiency, and leading incident response and disaster recovery. The role partners across engineering and product teams on capacity planning, reliability, troubleshooting, and post-incident improvements.

Design and deploy agentic AI systems for life sciences, including LLM orchestration, multi-agent collaboration, NLP, retrieval, information extraction, and clinical AI applications. Develop evaluation, prompt optimization, observability, and monitoring systems. Collaborate with product, design, clinical, and engineering teams; mentor engineers; shape AI strategy; and contribute through publications, conferences, and open-source work.

AI Interns support real-world data and machine learning projects using Python, Jupyter Notebooks, and SQL. Responsibilities include data analysis, basic machine learning, research, project documentation, and progress updates. The role is designed for students pursuing degrees in mathematics, computer science, engineering, or related fields and provides hands-on experience during a full-time summer internship.

Leads enterprise second-line risk oversight for AI, generative and autonomous technologies, blockchain, digital assets, stablecoins, and tokenization. Establishes AI governance, risk classification, independent challenge, regulatory readiness, monitoring, and escalation frameworks. Oversees digital asset risks including custody, smart contracts, cybersecurity, financial crime, liquidity, and operational resilience. Advises executives and governance bodies, coordinates cross-functional risk disciplines, and leads a team developing sustainable emerging-technology risk capabilities.

Build and test production-grade AI agents, copilots, and automated workflows for Roku engineering teams. Interns will connect agents to data, services, and tools; evaluate reliability and failure modes; write tested and documented code; participate in reviews; and collaborate with engineers and mentors. The 12-week paid internship includes ownership of a scoped project from problem definition through delivery.

Lead the end-to-end architecture of scalable AI, machine learning, and GenAI systems from model training through production inference. Partner with product, engineering, data science, and clinical teams to develop reference architectures, MLOps infrastructure, governance, security, and cost-efficient deployment strategies. Evaluate emerging technologies, mentor teams, promote responsible AI adoption, and translate business objectives into production-ready solutions.

Build and support enterprise AI-orchestrated workflows and API integrations across ITSM, HR, identity, access, and internal business systems. Develop Python and JSON automation logic, troubleshoot APIs and authentication, and implement logging, monitoring, retries, correlation IDs, and error handling. Review AI-generated code, support production workflows, collaborate with stakeholders, and create reusable automation templates and documentation while following security, governance, testing, and release standards.

Build and own software systems supporting Tutor’s go-to-market operations. Develop CRM capabilities, AI agents, sales workflow automation, market-mapping tools, and integrations with robotics, deployment, documentation, and agent platforms. Work directly with sales and demand-generation teams, attend customer activities, and improve tools based on pipeline, conversion, and seller-time outcomes. The role requires substantial independent software development and close embedding within a business function.