Top Artificial Intelligence Jobs

Develop and optimize prompts for generative AI models and LLMs supporting U.S. Customs and Border Protection. Test prompt variations, analyze model behavior, support fine-tuning and RAG strategies, troubleshoot issues, and collaborate with technical teams to integrate AI solutions. The role requires software development experience, hands-on LLM experience, programming skills, and onsite availability in Ashburn, Virginia and/or Washington, D.C.

Develop and optimize prompts for large language models and generative AI applications supporting U.S. Customs and Border Protection. Responsibilities include testing prompt variations, analyzing model behavior, collaborating on process automation, supporting fine-tuning and RAG strategies, researching AI advancements, and debugging model issues. The role requires onsite work in Ashburn, Virginia and/or Washington, D.C., U.S. citizenship, and successful completion of a CBP background investigation.

Develops and optimizes prompts for generative AI models and large language models supporting U.S. Customs and Border Protection. Responsibilities include prompt testing, performance analysis, model behavior debugging, collaboration on AI process integrations, research into emerging techniques, and support for fine-tuning and RAG strategies. The role requires programming experience, hands-on LLM experience, prompt engineering knowledge, onsite availability in Ashburn or Washington, D.C., U.S. citizenship, and ability to pass a CBP background investigation.

Design, build, and operate production AI agents and foundational cloud platform infrastructure. Develop agentic workflows for incident triage, environment provisioning, configuration generation, remediation, and developer self-service. Operate highly available AWS and Kubernetes systems, evolve infrastructure-as-code and GitOps practices, improve observability and reliability, and contribute to Cortex’s AI-augmented developer platform. Mentor engineers, document architecture, participate in on-call rotations, and collaborate across engineering, product, and security teams.

Develops and optimizes prompts for generative AI and large language models supporting U.S. Customs and Border Protection. Responsibilities include testing prompt variations, analyzing model behavior, collaborating on AI integrations, researching emerging techniques, supporting fine-tuning and retrieval-augmented generation, and debugging model issues. The role requires software development experience, hands-on LLM experience, programming knowledge, prompt engineering familiarity, U.S. citizenship, and onsite availability in Ashburn or Washington, D.C.

Design and deploy production AI/ML systems for federal cybercrime investigations. Build machine learning models, data pipelines, ETL workflows, databases, APIs, and microservices for investigative analytics. Implement explainable, auditable, secure, and compliant AI with human oversight, monitoring, bias detection, and model governance. Support authorization and risk management processes, document architectures and data provenance, and collaborate with engineering, infrastructure, cybersecurity, and investigative stakeholders.

Serves as a technical architect and strategic advisor for Cisco’s enterprise AI strategy. Defines architectures for agentic systems, multi-agent orchestration, durable execution, and interoperable AI platforms. Leads build-versus-buy decisions, establishes engineering standards, mentors senior technical staff, resolves complex AI challenges, and owns security for autonomous systems. Requires extensive distributed systems, LLM production, and multi-agent experience, with preferred expertise in AIOps, inference optimization, AI governance, and executive-level technical influence.

Lead the architecture and development of an internal AI developer platform. Build cross-platform onboarding, telemetry, secure sandboxing, AI agent integrations, and provider-agnostic abstractions. Establish engineering standards, review code and architecture, support production incidents, evaluate emerging AI technologies, and shape the platform roadmap. Mentor senior engineers and collaborate with platform users and stakeholders in a regulated financial-services environment, with emphasis on security, observability, reliability, and agent evaluation.

Conduct adversarial testing of deepfake detection models across audio, video, image, and text. Build attack simulations, red-team infrastructure, test harnesses, adversarial datasets, and automated regression testing. Analyze emerging generative AI threats, prioritize vulnerabilities, collaborate with AI, Data, Product, and Security teams, and communicate findings to technical and nontechnical stakeholders. The role also supports customer briefings, research publications, platform security testing, and ethical synthetic-media handling practices.

Leads the technical go-to-market for AI training-data and evaluation programs. Builds relationships with AI researchers, identifies opportunities, designs pilots and expert panels, defines rubrics and quality methodology, and converts successful pilots into production contracts. Owns technical discovery, evaluation metrics, sampling, acceptance thresholds, bid content, and credibility strategies while partnering with commercial and delivery leaders.

Develops and deploys generative AI capabilities for defense information operations, including LLM applications, agentic workflows, RAG systems, prompt engineering, model evaluation, and API integrations. The role requires translating operational needs into deployable AI solutions, implementing responsible AI safeguards, supporting demonstrations and milestones, and researching multimodal and synthetic-data techniques. Candidates need Python, cloud, MLOps, vector database, and defense-platform experience, plus U.S. citizenship and eligibility for a TS/SCI with Polygraph clearance.

Lead the technical go-to-market strategy for AI solutions, building relationships with AI researchers and evaluation teams. Own technical discovery, program and panel design, quality methodology, pilot success criteria, bid content, and credibility strategy. Translate model capability gaps into expert-driven evaluation and training-data programs, partner with sales and delivery teams, and help convert pilots into production contracts. The role combines deep AI evaluation expertise, statistical quality analysis, customer-facing technical leadership, and business development.

Build and own backend infrastructure for an AI-native life sciences platform, including scalable APIs, data pipelines, background workers, orchestration, RAG workflows, and real-time inference systems. Improve reliability, observability, security, multi-tenant data isolation, explainability, latency, and cost efficiency while collaborating with product, AI, engineering, and customer-facing teams.

Designs, develops, tests, and maintains Python-based Model Context Protocol servers and AI-agent integration components. Builds secure MCP tools, APIs, connectors, database integrations, and enterprise application interfaces, including Oracle and PeopleSoft systems. Implements authentication, authorization, least-privilege access, validation, reliability controls, automated testing, monitoring, and documentation. Troubleshoots integrations and participates in Agile delivery, architecture, code reviews, and release activities while supporting secure federal and enterprise environments.

Build and lead North’s Applied AI engineering team, taking AI solutions from prototype to production in regulated financial-services workflows. Responsibilities include architecting agentic systems, chatbots, copilots, and ML models; establishing secure enterprise integrations; managing evaluations, monitoring, CI/CD, incident response, and cost controls; and setting technical strategy and responsible-AI governance. The role requires close collaboration with business, product, risk, compliance, legal, and security stakeholders.

Builds, tests, publishes, validates, and maintains governed AI agents and automations for a bank. Translates business needs into controlled AI use cases, manages prompts and knowledge sources, performs security and accuracy testing, maintains documentation and inventories, monitors post-release performance, develops reporting, and supports AI training and enablement. Partners with technology, security, risk, compliance, legal, audit, and business teams to ensure responsible adoption and lifecycle governance.

Builds infrastructure, evaluation systems, tooling, and workflows for AI-powered software development. The role focuses on background agents, agent performance measurement, evaluation sets, feedback loops, developer adoption, code review improvements, reliability, observability, and safe autonomous engineering workflows. It requires hands-on software engineering, technical leadership, product judgment, collaboration with internal users, and the ability to deliver ambiguous, dependency-heavy projects.

Lead company-wide AI enablement and transformation by building and deploying LLM-powered agents, automations, evaluation frameworks, and safe deployment tooling. Consolidate existing AI initiatives, establish governance and data-handling guardrails, manage the use-case pipeline, select vendors and platforms, redesign workflows, and measure adoption, quality, efficiency, and employee impact. The role also involves coaching a largely nontechnical workforce through workshops, playbooks, office hours, and AI champions without direct reports.

Build and operate PEMCO’s end-to-end AI platform, including GPU infrastructure, model serving, AI gateways, retrieval systems, agent orchestration, observability, evaluation, cost telemetry, security, and governance. Lead production deployment, monitoring, incident response, lifecycle management, and architecture standards for AI systems. Optimize model routing, costs, performance, and reliability across Azure and potential on-premises environments while influencing organization-wide AI platform and governance decisions.

Build and operate Turing’s enterprise AI platform, including platform services, model gateways, multi-tenant infrastructure, CI/CD, infrastructure as code, deployment tooling, observability, and FinOps telemetry. The role supports reliable deployments across customer cloud accounts, dedicated instances, and sovereign environments. Responsibilities include production software engineering, on-call operations, incident response, secure platform design, and operational accountability.

Architects modular, multi-tenant SaaS platforms and AI-driven engineering workflows. Owns service boundaries, module contracts, identity, security, cloud infrastructure, migrations, observability, and agent guardrails. Designs AI product features, selects model providers, establishes evaluation and cost controls, and reviews high-risk changes. Coaches developers on specification writing and agent orchestration while collaborating with product and engineering leadership.

Designs and implements hardware and software security architectures for AI inference systems. Owns hardware root of trust, secure boot, confidential computing, isolation, embedded firmware security, model protection, and threat modeling. Leads security features from architecture and emulation through deployment and remediation, collaborating across hardware, software, and research teams. Requires extensive hardware security, silicon, embedded systems, low-level firmware, and security engineering experience.

Design, develop, and deploy production AI/ML systems involving foundation models, agentic workflows, RAG, multimodal learning, model evaluation, and edge inference. Build data pipelines, cloud architectures, APIs, and software integrations while applying AI assurance practices. Lead technical tasks, mentor junior staff, collaborate with customers and cross-functional teams, contribute to Agile delivery, and support advanced government and commercial R&D proposals.

Develops AI-powered cybersecurity applications that protect enterprise systems, applications, and data. Responsibilities include integrating and maintaining threat defense and user access management capabilities, troubleshooting escalated incidents, investigating security issues, coordinating with managed security providers, and recommending corrective actions. The role involves cloud-native development, agentic AI, RAG, security controls, APIs, testing, monitoring, and software engineering best practices.

Build autonomous AI agents that translate proven attack paths into targeted security-control changes, safely apply or stage those changes, and verify remediation by rerunning attacks. Responsibilities include ML reasoning systems, evaluation and training data pipelines, counterfactual experiments, heterogeneous control-plane integrations, safety architecture, staged rollouts, rollback, auditability, and diagnosing agent failure modes. The role requires production ML engineering, agentic systems or LLM post-training, outcome-based evaluation, Python software engineering, distributed systems, and cloud infrastructure experience.