Top AI Engineer Jobs

Build and deploy autonomous and semi-autonomous AI agents for an Agentic SOC, integrating SIEM, EDR, XDR, and threat intelligence systems. Translate SOC playbooks into agentic pipelines, optimize RAG workflows and LLM performance, and design human-in-the-loop escalation. Secure LLM architectures against prompt injection, data poisoning, tool abuse, and OWASP Top 10 for LLM risks. Develop scalable AI security solutions using cloud platforms and collaborate with analysts and threat hunters.

Build and deploy AI agents for security operations, translating SOC playbooks into agentic pipelines and optimizing RAG and LLM workflows. Design human-in-the-loop escalation, integrate SIEM, EDR, XDR, and threat intelligence systems, and secure AI architectures against prompt injection, data poisoning, and tool abuse. The role also involves production cloud deployment, orchestration, vector databases, and scalable AI services.

Develop, test, and optimize prompts for generative AI models and large language models. Analyze prompt performance, troubleshoot model behavior, and collaborate on AI solutions that improve mission workflows. Support model fine-tuning and Retrieval-Augmented Generation initiatives while researching advances in AI. The role includes structured prompt-engineering training before transitioning to client-facing work supporting U.S. Customs and Border Protection.

Develop and optimize prompts for generative AI and large language models supporting U.S. Customs and Border Protection. Test prompt variations, analyze results, troubleshoot model behavior, and collaborate on fine-tuning and Retrieval-Augmented Generation solutions. The role requires software development experience, programming skills, onsite availability in Ashburn or Washington, D.C., and the ability to pass a CBP background investigation.

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.

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.

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.

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 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.

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.

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.

Designs, builds, secures, and governs enterprise-scale agentic AI systems. Responsibilities include developing LLM, RAG, prompt engineering, and multi-agent solutions; implementing defenses against prompt injection, jailbreaking, and tool misuse; creating resilient, observable, auditable pipelines; and applying software engineering, CI/CD, infrastructure-as-code, and microservices practices. The role also leads cross-functional alignment, advises executives, develops architecture roadmaps, and supports AI governance in regulated environments.

Build and operate production AI and LLM systems for safety-critical enterprise applications. Responsibilities include multi-step agent orchestration, retrieval across Neo4j and vector stores, evaluation frameworks, model comparison, observability, quality monitoring, failure handling, and production incident response. The role requires strong Python and experience with asynchronous services, LLM workflows, evaluation, retrieval, graph reasoning, and enterprise AI security. It also contributes to model selection, technical direction, and the AI roadmap.

Lead AI and LLM offensive security engineering by breaking agentic systems, researching prompt injection, tool-use exploitation, model and supply-chain attacks, and automating repeatable attack modules for NodeZero. Build production-grade LLM applications and microservices with safety, reliability, cost, and observability considerations. Drive threat modeling, exploit development, and technical strategy for AI security capabilities.

Build and maintain scalable .NET applications across web, desktop, console, and mobile platforms while developing AI capabilities on Azure. Responsibilities include creating RAG solutions, LLM agents, MCP integrations, AI evaluation harnesses, guardrails, and secure application integrations. The role also supports databases, automated testing, DevOps practices, and third-party APIs. The engineer will mentor developers on GitHub Copilot, Claude, AI coding practices, and responsible review of AI-generated code.

Own technical presales pilots for Commure Ambient AI across healthcare practices and ambulatory EHRs. Scope workflows, build and debug browser, DOM, and API integrations, configure advanced AI features, coordinate templates, triage issues, maintain Salesforce status, support RFPs and security questionnaires, and deliver documented handoffs to implementation. Partner with account executives, engineering, template, and operations teams while improving pilot tooling and processes.

Build and operate production AI agents, including autonomous coding systems, retrieval and knowledge layers, MCP tooling, evaluation frameworks, and secure LLM workflows. Improve model cost, latency, reliability, and observability on AWS Bedrock while integrating Snowflake, documentation, knowledge graphs, Jira, Slack, and developer workflows. Responsibilities include prompt and model evaluation, security hardening, design documentation, code review, mentoring, and on-call ownership.

Designs, develops, deploys, and supports generative AI applications on AWS using Amazon Bedrock. Builds RAG systems, AI agents, document-processing workflows, Python APIs, evaluation pipelines, and security safeguards. Integrates enterprise data and APIs, automates infrastructure and delivery, monitors production workloads, and optimizes performance, reliability, and cost. Translates customer requirements into solution designs, demonstrations, implementation plans, and operational handoffs.

Build and productionize AI agents for PR and communications workflows. Develop Python agent runtimes, MCP servers, Django/Vue applications, chat interfaces, streaming responses, permission-aware APIs, evaluation systems, and quality improvements. Collaborate on prompting, tool use, memory, guardrails, observability, security, deployment, and platform architecture while using customer feedback to improve agent quality, latency, and cost.