Top AI Engineer Jobs

Provides technical direction for AI threat defense within Cyber Defense. The role develops strategies, architectures, detection content, telemetry, investigation methods, playbooks, and response workflows for threats involving AI applications, agents, models, infrastructure, and integrations. It also embeds responsible AI into SOC and CIRT operations, partners with engineering and AppSec teams, tracks emerging adversary techniques, mentors security professionals, and influences executives, customers, regulators, and industry groups.

Lead AI application security across Fortinet by conducting threat modeling, code reviews, penetration testing, AI red teaming, and architectural assessments. Develop security guardrails, filters, policy controls, and CI/CD security checks for LLMs, chatbots, MCP implementations, and autonomous agents. Research emerging AI attack vectors, define mitigation strategies, coach teams, and ensure AI systems meet security, safety, privacy, and compliance standards.

Own security assessment and risk management for AI, LLM, Claude, MCP, and agentic systems. Define security controls, access governance, monitoring frameworks, standards, and training. Partner with AI Enablement and engineering teams on threat modeling, architecture reviews, and secure deployment. Build AI security monitoring tools, investigate risks such as prompt injection and data leakage, and track emerging attack vectors, defenses, and regulations.

Design, develop, deploy, monitor, and support enterprise AI agents and Generative AI applications. Build RAG pipelines, agentic workflows, prompt-engineering solutions, LLM integrations, APIs, microservices, and CI/CD processes. Provide production troubleshooting, optimization, incident resolution, and ongoing enhancements across cloud environments. Translate client requirements into practical AI solutions, evaluate emerging AI technologies, and explain complex concepts to technical and non-technical stakeholders. Korean bilingual proficiency is mandatory.

Build and own Dotmatics Luma’s production agentic AI system, including reasoning loops, tools, memory, state persistence, security, sandboxing, recovery, evaluation, and observability. Develop supporting services and interfaces in Python, Node.js/TypeScript, and React. Lead AI-native development using coding agents, improve reliability and performance, and coach others on agent-driven engineering practices.

Design, develop, and operationalize AI/ML capabilities within secure DoD environments. Build agentic workflows, APIs, production services, and MLOps processes for classification, summarization, enrichment, and intelligent routing of lessons-learned data. Manage model deployment, validation, monitoring, and continuous improvement using Databricks, MLflow, and related tools. Integrate structured and unstructured data, troubleshoot production systems, and collaborate with engineering and mission teams while applying responsible AI and security practices.

Secure AI/ML models, data pipelines, and AI-enabled applications by conducting vulnerability assessments, threat modeling, incident response, and security reviews. Build guardrails, monitoring, detection, access controls, encryption, and data-segmentation measures. Partner with engineering and analytics teams throughout the AI development lifecycle, assess third-party AI tools, document remediation plans, and translate emerging threats such as prompt injection, data poisoning, jailbreaks, and model-supply-chain risks into practical mitigations.

Develop and deploy production AI/ML and generative AI solutions for FDA modernization initiatives. Build RAG pipelines, LLM applications, machine learning models, APIs, data pipelines, and MLOps/LLMOps capabilities. Evaluate model quality, implement responsible AI safeguards, monitor production systems, and integrate AI services into enterprise applications. Collaborate with data, software, cloud, and product teams in an Agile environment while documenting solutions and mentoring less-experienced engineers.

Leads client-facing discovery and technical product development for AI initiatives. Designs, prototypes, deploys, and operates agentic systems, retrieval pipelines, enterprise integrations, and production-grade generative AI applications. Establishes evaluation, observability, safety, governance, and reliability frameworks while translating business needs into technical specifications. Requires strong Python and software engineering expertise, cloud and DevOps experience, retrieval architecture knowledge, and at least seven years of engineering experience, including production generative AI experience.

Lead client-facing AI discovery and technical product leadership while designing, prototyping, and productionizing agentic systems, retrieval applications, and enterprise AI workflows. Build full-stack software, evaluation and observability frameworks, safety guardrails, and cloud deployments. Translate business needs into technical specifications, prioritize use cases, and guide solutions from rapid proof of concept through secure, reliable production systems.

Secure AI-powered applications, APIs, GenAI solutions, and RAG systems through architecture reviews, application security assessments, penetration testing, secure coding, and implementation of authentication, authorization, data protection, and AI-specific security controls. Collaborate with application, AI engineering, enterprise security, and architecture teams to address risks such as prompt injection, insecure outputs, data leakage, insecure plugins, and retrieval poisoning.

Designs, develops, and deploys agentic AI systems for cybersecurity, including vulnerability analysis, threat detection, alert triage, secure code generation, and remediation recommendations. Builds RAG pipelines, LLM integrations, APIs, microservices, and cloud-based platforms. Implements AI guardrails, threat modeling, human-in-the-loop governance, adversarial testing, explainability, and performance optimization while integrating systems with SIEM, SOAR, vulnerability management, and endpoint security tools.

Secure enterprise AI, ML, GenAI, and agentic AI systems through risk assessments, threat modeling, runtime controls, security testing, monitoring, and development guardrails. Evaluate risks such as prompt injection, data leakage, unsafe tool execution, retrieval poisoning, and model abuse. Collaborate with engineering, cloud, application security, architecture, and business teams on secure deployments, AI governance, compliance, remediation, and secure use of AI development tools.

Build and operate production Python services, AI-enabled workflows, RAG systems, multi-agent applications, and security automation platforms. Develop integrations, scalable data pipelines, code-review tools, and AI evaluation frameworks while managing reliability, latency, security, and cost. Partner with engineering and security stakeholders to identify opportunities and deliver maintainable solutions from concept through production.

Develop and maintain bespoke enterprise applications and websites using Microsoft technologies and Azure. Collaborate with business, project management, and IT teams to deliver application capabilities, automate development processes, support monitoring, and manage upgrades and integrations. Apply LLM prompting, evaluation, RAG, grounding, and agentic AI techniques to translate business requirements into measurable AI solutions.

Build and ship production AI-enabled product features for transportation agencies. Partner with product, design, customer success, and data science teams to identify valuable AI applications, implement full-stack capabilities, evaluate LLM correctness and grounding, enforce authorization and customer isolation, and operate features in production. Select appropriate techniques such as prompting, retrieval, tool use, text-to-SQL, fine-tuning, or conventional software while managing reliability, latency, cost, monitoring, and incident response.

Hands-on QA Engineer responsible for exploratory, functional, regression, API, security, accessibility, integration, and AI-feature testing across a legal SaaS platform. The role validates document retrieval and delivery workflows, investigates defects, uses SQL, logs, dashboards, and simulators for backend verification, and creates targeted automated checks with tools such as Playwright. Responsibilities also include testing authentication, permissions, LLM behavior, prompt injection, third-party integrations, and production issues while collaborating with engineering and product teams.

Build and scale production generative and agentic AI systems supporting the software delivery lifecycle. Develop multi-agent workflows with Google ADK, Gemini, and Claude; integrate enterprise tools such as JIRA and GitHub; create RAG pipelines and evaluation harnesses; and build asynchronous Python and FastAPI services backed by PostgreSQL. Apply testing, observability, CI/CD, containerization, secure coding, and AI guardrails to reliable enterprise deployments.

Develop and maintain full-stack Java applications using Java EE, RESTful microservices, Angular, TypeScript, Node.js, SQL, PL/SQL, and Oracle. Use AI coding assistants such as GitHub Copilot for code generation, debugging, optimization, and prompt-driven development. Validate AI-generated code for accuracy, performance, and security while applying JVM performance profiling. Collaborate on production-ready solutions, with familiarity in CI/CD, DevOps, Unix scripting, Docker, caching, and SAML preferred.

Develop and deliver native iOS features for MyFitnessPal’s AI-powered food logging experience. Responsibilities include implementing user interfaces, optimizing performance and network communications, building tested and secure solutions, collaborating with product and design teams, researching technical approaches, mentoring developers, and contributing to deployment and engineering best practices.

Build and operate AI agent capabilities and full-stack experiences for an internal enterprise intelligence platform. Develop retrieval, RAG, orchestration, tool integrations, prompts, evaluations, observability, APIs, and user interfaces for search, chat, and workflow automation. Collaborate with business stakeholders and data engineering teams while applying secure, reliable software engineering practices, including testing, CI/CD, monitoring, and on-call ownership.

Develop and ship software end to end using AI-assisted coding tools, while reviewing, testing, securing, and correcting generated code. Lead modernization work, evaluate AI tooling through measurable outcomes, establish guardrails, and share effective practices with engineering colleagues. The role requires strong software fundamentals, experience across legacy and modern stacks, production ownership, and independent judgment in ambiguous situations.

Assess and secure emerging AI technologies through architecture reviews, code reviews, penetration testing, threat modeling, and remediation guidance. Partner with engineering and business teams to establish secure development standards, research AI vulnerabilities, and build tools supporting secure AI use at scale. The role focuses on LLMs, agentic systems, RAG architectures, prompt injection, data exfiltration, privilege escalation, and model supply chain risks.

Bridges clinical practice and AI innovation by identifying clinical challenges, translating them into product requirements, deploying and evaluating AI capabilities, validating outputs, and supporting model improvement. Partners with clinicians, hospitals, product management, R&D, UX, quality, and regulatory teams to conduct clinical evaluations, generate evidence, validate software, integrate solutions into hospital IT environments, and drive adoption of AI-enabled healthcare products.

Build and deploy production-grade AI agent systems that automate legal workflows. Responsibilities include developing Python-based agents, rapidly prototyping and evaluating product features, integrating customer and legal-team feedback, and creating AI-native workflows with streaming and long-running tasks. The role requires strong coding, LLM experience, scalable enterprise software skills, and an understanding of infrastructure systems such as databases and cloud storage.