Top Artificial Intelligence Jobs

Develop and deploy production-grade AI capabilities, including multi-agent architectures, RAG systems, knowledge graphs, fine-tuned language models, inference workflows, and intelligent integrations. Build evaluation frameworks, integration tests, monitoring, and secure distributed services using Python and modern AI tools. Collaborate with engineering teams to deliver scalable, reliable solutions for mission-ready environments. The role requires TS/SCI clearance, DoD 8140 Level II certification, a bachelor's degree, and travel up to 25%.

Leads a cross-functional AI/ML engineering team developing NLP, agentic AI, and machine learning capabilities for therapeutic research and development. Responsibilities include setting AI strategy, designing and deploying production systems, evaluating models, developing RAG and agent workflows, ensuring governance and privacy compliance, presenting to leadership, and supervising, mentoring, and recruiting team members.

Build and ship production AI systems for enterprise portfolio and planning workflows. Develop anomaly and signal detection, LLM-powered insight synthesis, natural-language planning rule translation, evaluation frameworks, and MCP tools. Partner with external AI providers initially, then establish internal standards for prompt design, model selection, agent architecture, reliability, and evaluation. Collaborate closely with product, design, and domain engineering teams.

Leads global engineering teams developing Private AI products and services, integrating with vSphere, NSX, and vSAN. Owns platform reliability, security, lifecycle management, SDLC execution, and day-two operations. Defines Broadcom’s upstream contribution strategy and represents the company in the Kubernetes and CNCF ecosystems. Requires deep Kubernetes expertise, significant open-source influence, distributed-systems experience, and global engineering leadership.

Lead AI/ML initiatives for Air Force condition-based maintenance, from predictive model development through production deployment. Architect data pipelines, develop machine learning, deep learning, NLP, and generative AI solutions, and build knowledge graph capabilities. Provide technical leadership, integrate models into applications and workflows, and monitor data quality, model performance, drift, and bias. Collaborate with engineering, cloud, software, and product teams while supporting defense aviation logistics and sustainment missions.

Leads global engineering, architecture, delivery, and modernization for a securities valuation platform supporting pricing, valuation models, fund accounting, and NAV calculations. The role drives cloud adoption, resilient application architecture, DevOps, automation, data and AI capabilities, regulatory compliance, operational stability, and technology strategy. The Vice President also develops global talent, partners with business and risk stakeholders, and delivers scalable solutions across financial services.

Enables business teams to adopt governed AI by identifying use cases, demonstrating and prototyping AI assistants and automations, training users, testing outputs, and guiding risk reviews. Creates enablement materials, tracks adoption and business outcomes, supports AI requirements development, and collaborates with IT, Security, Legal, and AI governance stakeholders. The role emphasizes responsible AI, process improvement, stakeholder engagement, and practical adoption of approved generative AI tools.

Lead a platform engineering team responsible for tier-zero systems, identity, permissions, sessions, and messaging infrastructure. Own delivery, quality, architecture, technical debt, and team development while partnering with Product, Design, and Recruiting. Embed AI tooling into engineering workflows and build AI-powered automation to improve productivity. The role also involves hiring, mentoring engineers, making architectural decisions, and connecting platform outcomes to customer value.

Leads enterprise workflow modernization and architecture while designing scalable AI solutions, distributed systems, integrations, and digital products. Establishes technical standards, governance, responsible AI practices, and AI-enabled SDLC improvements. Partners with product, design, architecture, security, operations, and business teams to deliver secure, resilient workflow experiences. Provides hands-on technical leadership, mentorship, modernization strategy, and guidance on enterprise AI investments.

Leads LaunchDarkly’s enterprise AI operating model, including portfolio management, spend and adoption reporting, governance, policy operations, vendor reviews, audits, roadmap tracking, communications, and enablement. Partners with Finance, IT, Security, Legal, Procurement, executives, and distributed AI centers of excellence to improve responsible AI adoption, reduce duplication and costs, accelerate reviews, and demonstrate measurable business value.

Drive new enterprise business for Automation Anywhere’s Agentic Process Automation platform. Responsibilities include prospecting, managing sales pipelines, conducting customer presentations and demonstrations, attending industry events, leading proof-of-concept exercises and RFX responses, and coordinating solution design with technical teams. The role requires consultative enterprise software selling, quota attainment, technical communication, and collaboration with sales engineers, partners, and professional services.

Designs and improves security controls for enterprise AI, ML, generative AI, LLM, agent, and cloud environments. Responsibilities include AI security architecture, threat modeling, application and API security, IAM, data and model protection, monitoring, incident response, automation, and governance. The role partners with cybersecurity, IT, privacy, legal, compliance, data, application, and business teams to secure AI platforms throughout their lifecycle and address risks such as prompt injection, data leakage, excessive agency, and supply-chain compromise.

Build, deploy, monitor, and improve production-grade AI agents using Python, Claude Agent SDK, and AWS Bedrock AgentCore. Design golden datasets and LLM-as-a-Judge evaluations, integrate automated quality gates into CI/CD, and implement Langfuse or OpenTelemetry observability. Evaluate agent accuracy, task completion, tool execution, and performance while supporting RAG pipelines and AWS data workflows. The role also involves troubleshooting, continuous evaluation, and potentially agentic red-teaming.

Provide legal counsel for Salesforce AI products and research programs across product development, privacy, data protection, commercial agreements, and strategic transactions. Advise executives, engineers, and product managers; negotiate agreements with technology partners and service providers; support AI-related commercial escalations and transaction diligence; deliver client training; and build strong cross-functional relationships.

Build and ship full-stack features across frontend, backend, APIs, data, integrations, and Salesforce. Use AI-assisted development tools throughout the software development lifecycle, develop agentic and AI-powered business features, and own solutions from design through production support. Responsibilities include automated testing, CI/CD, code reviews, Agile collaboration, enterprise integrations, and maintaining secure, scalable, high-quality software.

Lead design and implementation of Copilot AI experiences and platform services across Windows, Mac, and mobile. Build secure, performant, distributed systems integrating models and agentic workflows, collaborate with cross-functional teams, ship well-tested maintainable code, and drive iterative product delivery.

Leads the architecture, roadmap, and delivery of AI/ML and LLM solutions for defense and intelligence missions. Develops projects from proof of concept through production, supports customer demonstrations, collaborates on bids and proposals, and guides multidisciplinary teams. The role requires cloud-native and distributed-systems expertise, stakeholder engagement, innovation, and experience with edge AI, autonomous systems, intelligence analysis, logistics, or readiness programs. Remote work is available with up to 25% travel and occasional on-site requirements.

Lead technical customer activation for AI Spend Management and Router. Own discovery, implementation planning, provider connections, troubleshooting, first-use verification, and sustained adoption. Translate AI usage, costs, and governance concepts between developers, AI administrators, and Finance leaders. Coordinate with Sales, Customer Success, Solutions, Product, and Engineering on integrations and escalations. Document recurring issues and create repeatable implementation playbooks for an evolving product.

Develop AI agents, prompts, workflows, contextual data layers, orchestration frameworks, and machine learning models. Test and optimize solutions using structured and unstructured data, evaluate accuracy and scalability, analyze edge cases and failure modes, and document experiments and model performance. The 10-week remote internship is intended for students at least in their third year studying data science, AI, computer science, or a related field.

Leads strategy, roadmaps, implementation, integration, governance, and continuous improvement for enterprise business applications. Partners with cross-functional leaders to improve processes, data quality, reporting, automation, security, and compliance. Manages the business systems team, vendors, licensing, contracts, budgets, and service performance while evaluating emerging technologies such as AI and workflow automation.

Own the vision, strategy, roadmap, and measurable outcomes for Zoom AI Services developer experiences. Lead UX and product design, rapidly prototype AI-native workflows, and partner with engineering, research, design, developer relations, and go-to-market teams to launch developer platforms and AI products. Drive adoption, activation, retention, and customer satisfaction while operating as a hands-on individual contributor.

Supports end-to-end delivery of governed AI solutions across business functions, including generative AI agents, machine learning, computer vision, and optimization applications. Collaborates with data scientists, engineers, product owners, and business stakeholders to prepare data, test and troubleshoot solutions, enforce architecture and coding standards, support Agile delivery, and measure adoption and business value. Helps ensure responsible AI, security, quality, and production readiness across Mosaic’s Azure-based enterprise AI platform.

Define and drive CrowdStrike’s company-wide AI and agentic architecture. Design production AI systems, shared platforms, lifecycle workflows, and developer tooling across products and business units. Improve performance, resilience, scalability, observability, governance, security, and auditability across cloud and edge environments. Build reference implementations, guide architectural decisions, resolve complex engineering challenges, mentor technical leaders, and align senior stakeholders around AI strategy and implementation.

Owns and executes an AI-focused digital product roadmap, prioritizing cross-functional demand and defining measurable outcomes. The role frames customer and business problems, uses research and analytics for decisions, advocates for intuitive user experiences, and partners with engineering, UX, architecture, and stakeholders. It also explores and scales Generative AI and agentic technologies, audits knowledge sources, and improves how knowledge is delivered to AI agents and translated into production capabilities.

Leads application and AI security across the software development lifecycle, focusing on LLM-based, internally developed, and agentic AI applications. Partners with development and architecture teams on threat modeling, security requirements, secure design, vulnerability prioritization, remediation, and security standards. Reviews application architectures, source code, data flows, trust boundaries, and AI integrations while developing guidance, automation, training, and reusable security patterns.