Top Artificial Intelligence Jobs

Leads the design and delivery of knowledge graph, semantic, and AI solutions for enterprise clients. The role includes ontology and schema design, graph-based machine learning, AI data pipelines, data architecture, client consulting, business-case development, thought leadership, and team or workstream leadership. Candidates will work across technical, research, product, and client teams to develop scalable solutions using graph, vector, cloud, NLP, and LLM technologies.

Deploy and benchmark LLM inference on NVIDIA GPU systems, configure KV cache offloading to network-attached storage, analyze performance bottlenecks with Python, and collaborate with FPGA, SoC, and firmware engineers to validate end-to-end data paths. The intern will quantify storage requirements, maintain reproducible experiments, and present findings that inform future SSD controller architectures.

Build and deploy production AI integrations, automations, and internal tools using LLM APIs, agent frameworks, and cloud platforms. Lead client discovery, AI strategy, migrations, workflow integrations, proofs of concept, and solution delivery. Monitor and evaluate AI applications, improve quality and performance, and apply security, privacy, and governance practices. Collaborate in pre-sales, scope projects, estimate effort, and communicate technical solutions to non-technical stakeholders.

Architect and build scalable, secure cloud infrastructure for SaaS platforms. Lead CI/CD automation, Kubernetes-based microservices scaling, infrastructure-as-code adoption, cross-functional DevOps projects, and AI-assisted operational improvements. Collaborate with engineering, QA, and product teams to improve delivery, reliability, cost efficiency, and developer productivity. Participate in on-call rotation and serve as a DevOps subject matter expert.

Own the strategy, roadmap, and delivery of AI-powered capabilities across Mapbox Maps. Partner with engineering, customers, and cross-functional teams to identify use cases, prioritize development, validate solutions, and launch products. Manage pricing and packaging, sales enablement, marketing, trials, and partnerships while driving customer adoption, business growth, and scalable experiences for developers and end users.

Build scalable data and ML systems for LLM training, post-training, evaluation, and continuous improvement. Responsibilities include creating data pipelines, human-in-the-loop labeling workflows, synthetic datasets, quality metrics, and automated evaluation systems. The role applies LLMs and machine learning to data generation and validation, processes large-scale structured and unstructured data, translates research into production systems, and provides technical leadership and mentorship.

Develop and deploy AI-enabled software solutions, including LLM applications, RAG pipelines, model-serving APIs, data workflows, testing, evaluations, monitoring, and CI/CD support. The role uses Python, SQL, modern generative AI frameworks, vector databases, Docker, and cloud platforms while documenting work, following AI safety standards, and collaborating through code reviews under close supervision.

Supports Revenue Operations by identifying, deploying, testing, and scaling AI tools, agents, analytics, and automation for Sales teams. The role prepares trusted datasets, maintains data quality and governance, enables self-service analytics, validates AI outputs, and translates business needs into scalable technical solutions. It partners across Sales, Revenue Operations, data, and technology teams to improve efficiency, reduce manual work, accelerate insights, and drive adoption of AI-enabled workflows.

Leads company-wide AI effectiveness by defining knowledge strategy and governance, prioritizing reusable AI use cases, scaling solutions, activating employees through an AI Champions Network, and measuring adoption, spending, and ROI. Partners with executives and Data, IT, Engineering, and Finance teams to build shared AI infrastructure and drive adoption across a remote-first organization.

Build automation for NVIDIA switches and InfiniBand fabrics, including configuration management, drift reconciliation, NAT gateways, L4 load balancing, and resilient network control loops. Define large-scale network behavior around BGP, convergence, failover, and production failures. Lead technical direction, design standards, and customer deployments while writing production software in Go or Python for AI infrastructure.

Implements and monitors cybersecurity controls, assesses vulnerabilities, responds to incidents, supports remediation, and helps maintain security policies and incident response plans. The role conducts tabletop exercises, business continuity and disaster recovery testing, collaborates with IT operations and service providers, supports the service desk, and educates staff on security practices. Regular travel to remote plant and farm facilities, physical work, and occasional after-hours support are required.

Intern will conduct industrial robot simulations, develop AI and simulation systems, balance simulation fidelity with computational complexity, help close the sim-to-real gap, reduce physical prototyping, and generate photorealistic 2D, 3D, and video synthetic datasets for robotics applications. The role is a 12-week, onsite internship in San Jose, California, supporting ABB’s robotics R&D team.

Lead product strategy, roadmaps, prioritization, and delivery for enterprise AI platform solutions, including Xylem’s internal generative AI platform. Partner with engineering, architecture, cybersecurity, governance, legal, procurement, and business teams to deliver secure, scalable, reusable AI capabilities. Drive responsible AI adoption, platform governance, technical decisions, stakeholder alignment, and measurable business value across a complex global organization.

Lead the enterprise AI security and Agentic AI program by designing secure AI agents for cybersecurity operations, integrating Microsoft AI and security platforms, and establishing governance, risk controls, auditability, and responsible AI practices. Build reusable frameworks and guardrails addressing prompt injection, permissions, data leakage, and unauthorized actions. Partner with Security, Risk, Compliance, Legal, and Technology teams to enable compliant enterprise AI adoption.

Develop an end-to-end evidence management and synthesis system using data pipelines, publication search and deduplication, machine learning, and LLM workflows. The intern will integrate AI tools, manage data, collaborate with cross-functional stakeholders, and present findings. Required skills include Python, SQL, machine learning concepts, systems design, communication, and current enrollment in a relevant bachelor's or master's degree program.

Own Chainguard’s growth marketing program for developer and AI-agent discovery. Lead SEO, AEO, technical content, programmatic pages, structured data, and agent integrations to drive signups and pipeline. Evaluate recommendations across search engines, LLMs, and coding agents, while building attribution and reducing adoption friction. Partner cross-functionally with Product, Product Marketing, Growth, Web, and Paid Media. The role requires technical fluency, advanced SQL, coding-agent experience, and 4+ years working with technical audiences.

Own the enterprise AI strategy, roadmap, governance, investment priorities, and measurement framework. Identify and prototype high-value AI use cases, guide pilots into production, establish responsible-AI standards for sensitive data, and communicate outcomes to executives. Build adoption through enablement and reusable practices while coordinating technology, business, legal, security, data, and vendor partners. Lead the emerging AI strategy function and hold teams accountable for delivery and value realization.

Uses Peraton[x] and generative AI to turn executive questions into defensible analysis, workflows, visualizations, and automated reports. Builds and evaluates agentic AI applications using structured and unstructured data, validates outputs, develops quality frameworks, and creates reusable prompts and procedures. Partners with engineering and product teams to improve the platform, demonstrates use cases, supports pilots, and trains users. The role requires executive communication, analytical judgment, AI evaluation expertise, and cross-functional collaboration.

Build and evaluate secure machine learning systems, investigate AI vulnerabilities and adversarial attacks, develop defensive techniques, prototype emerging technologies, and transition AI capabilities to government sponsors. Collaborate with researchers and technical teams, communicate complex concepts, mentor junior staff, and support applications including computer vision, NLP, planning, robotics, and trustworthy AI.

Provide hands-on coaching, troubleshooting, micro-training, and adoption support for Microsoft Copilot, Gemini, and Claude. Help employees use generative AI effectively in daily work, refine prompts, develop practical resources, monitor adoption, gather feedback, and promote responsible use aligned with governance, privacy, and risk-management policies.

Design and build multi-agent applications using Google's ADK, including agents, tools, backend APIs, databases, and TypeScript web interfaces. Deploy and operate services on GCP with Cloud Run and Agent Engine. Develop evaluation suites, feedback loops, tracing, logging, authentication, CI/CD, and comprehensive testing to ensure reliable agent behavior. Integrate Gemini models, REST and MCP tools, and customer systems while using AI coding assistants and collaborating with technical leads and stakeholders.

Design, build, and operationalize secure Generative AI and machine learning solutions, including Gemini and Vertex AI applications, RAG pipelines, APIs, microservices, and private LLM deployments. Architect zero-trust, containerized AI environments and implement data protection, access controls, encryption, and threat safeguards. Ensure compliance with CMMC, NIST standards, ITAR, EAR, and FedRAMP requirements while maintaining governance artifacts. Partner with IT, cybersecurity, engineering, and business teams to identify use cases, provide technical guidance, and enable safe enterprise AI adoption.

Designs, operates, and improves enterprise AI platforms, applications, integrations, and agentic workflows. Builds secure RAG and tool-enabled solutions, manages governed data access, implements security and compliance controls, and establishes evaluation, monitoring, deployment, and reliability practices. Collaborates with cybersecurity, infrastructure, data, operations, and business stakeholders to deliver scalable AI capabilities and provides reusable guidance and support for internal teams.

Build and lead production AI agents, automations, and reporting systems for paid media, creator and affiliate campaigns, CRO, ecommerce, and client reporting. Integrate major marketing APIs, automate campaign operations, connect spend and attribution data, and create anomaly detection and optimization workflows. Lead technical standards, code and design reviews, mentoring, prioritization, adoption, and delivery while embedding with marketing teams to identify and solve revenue-impacting inefficiencies.

Leads the architecture, development, deployment, and maintenance of enterprise AI-enabled systems. Designs agentic AI solutions, evaluation frameworks, reusable patterns, and secure integrations while improving model quality through prompting and tuning. Partners with business and technical stakeholders on requirements, architecture, strategy, testing, reliability, monitoring, and production support. Drives AI adoption, mentors engineers, conducts design and code reviews, manages change processes, and guides technology direction across major platforms.