Top Artificial Intelligence Jobs

Leads IREX’s AI-in-education strategy for the ASAS program in Jordan while advancing the organization’s global AI portfolio. Advises on responsible AI policy, governance, investment, risk, evidence, and institutional readiness; guides higher-education and workforce initiatives; develops scalable AI offerings; builds partnerships; supports business development; and represents IREX in regional and global forums. The role emphasizes strategy, program leadership, thought leadership, and responsible adoption rather than software engineering.

Designs, develops, tests, deploys, monitors, and continuously improves machine learning models for complex business and national security missions. Integrates data pipelines, evaluates model performance, ensures data and output quality, and transforms prototypes into scalable production solutions. Requires strong expertise in machine learning algorithms, software architecture, data engineering, model validation, and system integration. The role may lead distributed teams and requires an active TS/SCI clearance with polygraph.

Administer enterprise AI and search platforms, including Glean, Rovo, Claude, Gemini, and AWS Bedrock. Manage users, permissions, connectors, licensing, RBAC, agents, and workspace settings while enforcing least-privilege access and data-handling standards. Troubleshoot configuration and access issues, document AI deployments, monitor usage and data exposure, support business teams, report platform health, and coordinate complex builds with Security, IT Engineering, and senior technical staff.

Leads enterprise data and AI architecture across Snowflake, AWS, and Google Cloud. Designs governed data products, semantic layers, knowledge graphs, lakehouse platforms, integrations, and AI-ready foundations for analytics, machine learning, generative AI, and agentic applications. Establishes architecture standards for security, privacy, lineage, responsible AI, and interoperability; guides complex cross-functional initiatives; prototypes emerging technologies; influences senior stakeholders; and mentors architects and engineers.

Builds secure, reusable AI engineering platforms and agentic AI solutions in a Microsoft Azure environment. Responsibilities include designing agent workflows and integrations, developing CI/CD pipelines, implementing automated testing and security scanning, applying responsible AI and privacy controls, protecting sensitive data, monitoring performance and costs, and creating standards, playbooks, and training for enterprise AI adoption.

Leads enterprise UI/UX engineering and digital experience strategy, architecture, and delivery. Responsibilities include modernizing legacy applications with React and Angular, governing design systems, establishing front-end standards, ensuring accessibility and security compliance, and developing AI-powered experiences such as copilots, conversational interfaces, intelligent search, personalization, and workflow automation. The role partners across Product, Engineering, Security, Architecture, and business teams in financial services environments.

Own and optimize Netpreme’s production LLM inference infrastructure, including vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, Kubernetes deployments, benchmarking, distributed serving, KV caching, batching, routing, and scheduling. The role focuses on improving latency, throughput, scalability, reliability, and efficiency across diverse LLM and multimodal workloads while partnering with hardware, model, and systems engineers.

Build and deploy production-grade AI applications and agentic solutions for clients and Oliver Wyman. Responsibilities include full-stack development, agent orchestration, retrieval and knowledge pipelines, cloud architecture, enterprise integrations, security, evaluation, monitoring, and continuous improvement. The role collaborates with product owners, consultants, technical teams, and clients while translating business objectives into reliable AI products.

Leads multiple engineering teams building agentic AI, machine learning, data science, and enterprise AI platforms. Oversees architecture, production delivery, scalability, security, governance, CI/CD, observability, and model lifecycle management. Partners with product, cloud, security, and infrastructure teams to define roadmaps and deliver reusable AI frameworks using LLMs, RAG, vector databases, agent orchestration, and microservices. Mentors engineering leaders and communicates execution, risks, and outcomes to senior stakeholders.

Leads enterprise AI security, including threat modeling for LLMs and ML pipelines, AI security tool administration, adversarial attack research, red-team exercises, AI governance, and security controls. Partners with engineering and product teams to secure AI systems throughout development and deployment, evaluates third-party AI risks, and mentors security professionals. Requires extensive cybersecurity and AI/ML security experience, Python proficiency, cloud expertise, and hands-on Palo Alto Networks AI security tooling.

Build and operate production AI agents, orchestration workflows, APIs, tools, and MCP infrastructure. Develop retrieval, grounding, evaluation, security, authorization, and multi-tenant capabilities for customer-facing systems. Lead cross-functional initiatives, improve reliability and performance, investigate production issues, support customers and partners, write technical documentation, review code, mentor engineers, and participate in on-call rotations.

Designs and implements security controls for AI solutions, including threat modeling, adversarial testing, secure development, identity and access management, data protection, monitoring, and incident response. The role builds AI-enabled cybersecurity automation, validates AI deployments, supports engineering teams with secure implementation patterns, and communicates security risks and recommendations to technical and business stakeholders.

Designs, builds, and deploys secure AI applications for Finance, including knowledge assistance, document processing, financial analysis, and workflow automation. Converts prototypes into maintainable production systems with enterprise integrations, permission-aware retrieval, testing, monitoring, audit controls, and human approval workflows. Partners with Finance and Digital teams to define requirements, architecture, and acceptance criteria, while creating reusable components, documentation, and operational support practices.

Leads 0-to-1 product strategy, ecosystem integrations, and secure deployment architecture for an AI security portfolio. Owns partnerships with foundation model providers, governs MCP gateways, agentic identity, guardrails, prompt shields, and red-teaming systems. Works with forward-deployed engineers and enterprise design partners to architect complex AI deployments, troubleshoot production implementations, and translate field requirements into product and model roadmaps.

Leads application and AI security engineering across architecture, software delivery, cloud platforms, APIs, and agentic AI systems. Builds reusable security controls, automated testing, risk-based quality gates, and secure engineering patterns. Conducts code reviews, threat modeling, authorized security testing, vulnerability triage, incident support, and remediation validation. Partners with distributed engineering teams, mentors technical leaders, and communicates security risks to executives, customers, and assurance stakeholders.

Coordinates strategic AI programs across engineering, product, legal, risk, compliance, security, HR, and business teams. Develops execution plans, governance frameworks, status reporting, risk registers, and executive communications. Supports AI platform and agent deployment initiatives, AI policy development, enterprise training, stakeholder alignment, and adoption. Requires strong program management, AI technology understanding, organizational skills, and experience managing complex cross-functional initiatives.

Lead end-to-end production AI development, including agentic workflows, document intelligence, underwriting copilots, conversational AI, RAG systems, fine-tuned models, evaluation frameworks, and shared AI platform components. Own design, deployment, observability, reliability, cost, and security of LLM systems while mentoring engineers and collaborating with product, credit, underwriting, and platform teams in a regulated lending environment.

Build and own full-stack, AI-native products for online learning using TypeScript, Vue.js, Node.js, Azure, retrieval pipelines, agentic workflows, model APIs, and LLM evaluations. Lead features from prototype through production, ensuring scalability, security, observability, cost control, and graceful model failure handling. Mentor engineers, establish AI-assisted development standards, conduct code reviews, and collaborate with product and engineering teams in a remote, async-first environment.

Design and optimize prompts, agent architectures, MCP tools, RAG workflows, and context-management strategies for reliable AI applications. Develop and integrate LLM-powered solutions with enterprise systems, APIs, healthcare data, and backend services. Establish evaluation and testing frameworks, improve response quality, and support deployment, monitoring, troubleshooting, security, compliance, governance, and responsible AI practices.

Build and deploy GenAI, ML, RAG, and agentic systems while conducting offensive security research and red teaming against AI applications. Identify prompt injection, data leakage, model theft, adversarial attacks, and other vulnerabilities; develop scanning and fuzzing tools; and implement secure-by-design hardening strategies. The role also involves cross-functional collaboration, technical research and writing, threat intelligence monitoring, community representation, and mentoring engineers.

Leads enterprise AI and application security architecture, governance, threat modeling, secure design reviews, and security tooling across cloud-native, AI, API, and microservices environments. Partners with engineering, DevOps, architecture, legal, compliance, and risk teams to implement secure-by-design practices, manage emerging AI threats, establish governance standards, and measure security maturity. Provides technical leadership, prototypes controls, evaluates third-party AI platforms, and mentors security and development teams.

Leads the architecture and delivery of AI-native, full-stack products using TypeScript, Vue.js, Node.js, PostgreSQL, and Azure. Designs retrieval pipelines, agentic workflows, model integrations, evaluation frameworks, observability, and human-in-the-loop experiences. Sets technical direction, leads cross-functional initiatives, mentors engineers, establishes engineering standards, and ensures AI systems are reliable, secure, scalable, accessible, and cost-effective in production.

Develops and governs enterprise classified AI architecture and multi-year roadmaps. Designs secure end-to-end AI platforms with model serving, RAG, vector search, data pipelines, orchestration, and mission-system integration. Leads architecture governance, trade studies, standards, design reviews, and repository management while incorporating identity, encryption, auditability, RMF, NIST, STIG, and Zero Trust requirements. Coordinates infrastructure, cybersecurity, network, and application teams, produces system designs, and aligns AI investments with DoD directives. Up to 10% travel may be required.

Provides hands-on technical leadership for full-stack AI-enabled enterprise applications. Designs Angular and TypeScript frontends, Java and Spring Boot backend services, REST APIs, and LLM integrations. Mentors developers, conducts code reviews, establishes engineering practices, resolves complex technical issues, and partners with architects, product owners, business stakeholders, and AI/ML teams. The hybrid Tucson role requires onsite work one to three times monthly, including occasional factory or classified-environment work.

Build and deploy production LLM applications, agentic systems, RAG capabilities, and intelligent automation on Azure. Own AI capabilities end to end, including data and embedding pipelines, enterprise integrations, evaluation, observability, security, and operational handoff. Work directly with business and engineering teams in a forward-deployed model, transforming ambiguous use cases into production systems and reusable AI accelerators.