Agentic AI Development Engineer
Fortinet · Sunnyvale
We are a cybersecurity company building a next-generation AI-driven operations platform, designed to power complex, high-stakes workflows. Our product integrates generative AI deeply into real-time operational environments—combining reasoning, retrieval, and automation into scalable, trustworthy systems.
1. ABOUT THE ROLE
We’re a cybersecurity company building a next-generation AI-driven operations platform, designed to power complex, high-stakes workflows. The product integrates generative AI deeply into real-time operational environments—combining reasoning, retrieval, and automation into scalable, trustworthy systems.
We’re looking for an Applied AI Engineer with strong backend and AI experience who can architect, build, and scale secure, performant systems. You’ll work closely with product, AI/ML, and design teams to deliver capabilities that drive investigation speed, streamline operations, and unlock new modes of human-AI collaboration.
2. KEY RESPONSIBILITIES
2.1 Development
End-to-end ownership of complex system features: from backend services and API design to user-facing interactions.
Architect and implement scalable AI agent and backend systems for high-volume, real-time operational workloads.
Integrate LLMs and GenAI components into production workflows, including fine-tuning, prompt orchestration, retrieval pipelines, and evaluation loops.
Design and implement robust data flows (e.g., event streams, message queues, job orchestration) to support next-gen SOC/NOC capabilities.
Build resilient real-time user experiences (e.g., dashboards, streaming data visualization, multi-user collaboration).
Define clear contracts between AI services, backend APIs, and frontend clients.
Contribute to trustworthy AI delivery: streaming responses with structured outputs, redaction/guardrails, and human-in-the-loop review.
2.2 Collaboration
Partner with AI/ML engineers to productionize models, optimize inference pipelines, and collect feedback signals for iterative improvement.
Collaborate with design and frontend engineers to translate complex backend/AI systems into intuitive UIs.
Lead technical reviews, and help shape coding standards and architectural patterns.
Communicate clearly with both technical and non-technical stakeholders about trade-offs, performance, and reliability.
3. REQUIRED QUALIFICATIONS
3.1 Experience
Proven track record shipping data-intensive and AI-enhanced applications at scale.
Experience owning features from system architecture through production delivery.
BS: 5+ years in backend or full-stack systems for high-availability, security-sensitive environments OR
MS: 3+ years in the same OR
PhD: 0+ year in the same
3.2 Technical Skills
Core Backend & Platform (Must Have)
Proficiency with at least one modern backend runtime/language (e.g., Python, Go) and associated frameworks.
Strong background in designing APIs (REST/WebSocket/GraphQL) and integrating with real-time/event-driven systems.
Deep understanding of databases and storage paradigms (e.g., Postgres, graph DBs, time-series stores).
Experience with authentication/authorization, session management, and enterprise integrations.
Familiarity with distributed systems, scalability, and observability best practices.
AI/ML Integration (Strongly Desired)
Hands-on experience building or integrating AI systems in production.
Familiarity with multi-agent, retrieval-augmented generation (RAG), prompt engineering, evaluation, and guardrails.
Exposure to model fine-tuning workflows or orchestration frameworks for multi-tool AI agents.
Frontend (Nice-to-Have)
Solid proficiency with modern JavaScript/TypeScript and a component-based framework (React or equivalent).
Experience building data-heavy dashboards and visualization for real-time operations.
Understanding of streaming UX patterns (WebSocket/SSE) and responsive design systems.
Communication & Soft Skills
Strong problem-solving skills and attention to detail.
Excellent written and verbal communication.
Comfortable operating in fast-moving, ambiguous contexts.
Experience working with distributed teams.
4. PREFERRED QUALIFICATIONS (BONUS POINTS)
Experience with event-driven architectures, message queues, and background job orchestration.
Exposure to graph/time-series data modeling and visualization.
Knowledge of real-time collaboration systems (presence, concurrency control).
Familiarity with SOC/NOC operations workflows, incident management, and observability pipelines.
Contributions to open-source AI/ML tools or backend frameworks.
WHAT WE OFFER
Opportunity to shape the future of AI-assisted cybersecurity and operations at scale.
End-to-end ownership of high-impact product surfaces used daily by enterprise customers.
Collaborative environment with experienced engineers, researchers, and designers.
Continuous learning in AI/ML, distributed systems, and modern web technologies.
Flexible work arrangements and competitive compensation.y Desired)
Hands-on experience building or integrating AI systems in production.
Familiarity with multi-agent, retrieval-augmented generation (RAG), prompt engineering, evaluation, and guardrails.
Exposure to model fine-tuning workflows or orchestration frameworks for multi-tool AI agents.
Frontend (Nice-to-Have)
Solid proficiency with modern JavaScript/TypeScript and a component-based framework (React or equivalent).
Experience building data-heavy dashboards and visualization for real-time operations.
Understanding of streaming UX patterns (WebSocket/SSE) and responsive design systems.
Communication & Soft Skills
Strong problem-solving skills and attention to detail.
Excellent written and verbal communication.
Comfortable operating in fast-moving, ambiguous contexts.
Experience working with distributed teams.