Senior AI Security Engineer

Job description

### **Firmus Technologies** Firmus Technologies is a global leader pioneering the development and operation of efficient AI infrastructure across Asia Pacific. Founded in Australia in 2019, our mission is to create the most efficient AI infrastructure by combining cutting-edge technology with a steadfast commitment to sustainability. At Firmus, we are unique in our approach. We design, build, and operate a new class of digital infrastructure – the AI Factory. Through our model-to-grid technology approach, we have pushed the boundaries of multi-generational liquid cooling systems, energy management, AI software orchestration, and construction. For our customers, this approach allows us to make every watt count and deliver low-cost AI tokens globally. ### **Firmus AI Cloud** Our large-scale GPU cloud platform, Firmus AI Cloud, is purpose-built to deliver energy-efficient AI compute at scale to customers. It empowers developers, enterprises, educational institutions, and government users to train and deploy AI models with unmatched efficiency and cost savings. With an ever-growing suite of services and applications, we are committed to delivering a cloud experience that is market-leading, proprietary, and built to scale. ### **Why Firmus?** As an NVIDIA Cloud and Engineering partner in Asia Pacific, you will gain skills, experience, and exposure across the AI industry and be part of shaping what this industry looks like for decades to come. We are founder-led, not a big corporate. Decisions happen fast, our leaders are accessible, and there's minimum bureaucracy between you and the work. Ownership comes early. Whatever your role, you will have a direct line to outcomes, helping shape how the business grows as we scale nationally across a long-term, large-scale roadmap. Work alongside founders and experts in AI infrastructure, energy systems and next-generation compute. What we build here has impact beyond the business. Our AI Factories are designed to operate as assets to the energy grid to actively strengthen the communities and regions they operate in rather than drawing from them. Considering applying? You don't need a perfect background to join our team. If you're driven and curious, there's a path for you. We back our people to grow into new domains and take on challenges beyond their previous experience. **ROLE SUMMARY** The Senior AI Security Engineer - AI Products & Applications will be embedded within the AI & Applications team to enable the secure design, development, release, and operation of AI products and applications. The role works directly with AI engineers, application engineers, product managers, inference engineers, DevOps, and platform teams to build practical security controls into agentic workflows, retrieval-augmented generation (RAG), model-serving APIs, enterprise-data integrations, and user-facing AI experiences. This is a product- and application-oriented security role. The engineer will help teams deliver trusted AI features without unnecessarily slowing innovation, while ensuring that AI products protect customer, enterprise, and operational data. The role reports operationally to the Head of AI & Applications, with a dotted-line reporting relationship to the Head of Cybersecurity to maintain alignment with enterprise security strategy, risk management, compliance requirements, and incident-response processes. **KEY RESPONSIBILITIES** - Partner with AI product, application, and engineering teams from discovery through production to define secure-by-design architectures for AI-powered products and services. - Threat-model LLM-enabled and agentic workflows, including direct and indirect prompt injection, data exfiltration, unsafe tool use, excessive agent permissions, cross-tenant access, model abuse, and unintended autonomous actions. - Design secure patterns for agent identity, delegated authorization, scoped credentials, tool allowlists, approval gates, action validation, execution sandboxing, rollback, and auditability. - Secure RAG and enterprise-knowledge workflows, including document ingestion, indexing, retrieval permissions, metadata filtering, source attribution, tenant isolation, sensitive-data classification, and data-retention controls. - Define security controls for user-facing AI products, including authentication, authorization, consent, rate limiting, abuse detection, content and output controls, conversation-data handling, and customer-facing audit trails. - Secure model-serving and inference APIs through workload identity, API authentication, tenant-aware access controls, quotas, request validation, model access policies, usage monitoring, and logging controls. - Build reusable AI security guardrails, libraries, reference architectures, templates, policy-as-code, and developer tooling that make secure implementation the default path for product teams. - Work with DevOps and Platform teams to ensure Kubernetes, custom job-scheduler integrations, CI/CD pipelines, secrets management, containers, and runtime environments provide the required security posture for AI applications. - Lead vulnerability management for AI application dependencies, model-serving runtimes, agent frameworks, SDKs, APIs, containers, CUDA and GPU software components, and related infrastructure. - Embed security requirements into application design reviews, pull-request and CI/CD controls, release processes, operational runbooks, and incident-response procedures. - Develop security telemetry and detections for anomalous agent actions, unsafe tool calls, unusual data retrieval, credential misuse, inference API abuse, suspicious workload behavior, and policy violations. - Act as the principal security liaison between AI & Applications and the Cybersecurity function, coordinating architecture reviews, security exceptions, risk acceptance, compliance evidence, incident response, and remediation tracking. **SKILLS AND EXPERIENCE** - 5+ years of experience in security engineering, application security, cloud security, platform security, or a related field. - Demonstrated experience embedding security practices into software engineering or product-development teams and supporting secure delivery from design through production. - Strong understanding of application security, API security, secure software development lifecycle practices, threat modeling, vulnerability management, and security automation. - Practical understanding of AI and LLM application security risks, including prompt injection, indirect prompt injection, jailbreaks, insecure tool use, excessive agency, model abuse, sensitive-data exposure, cross-tenant data leakage, and supply-chain risks. - Experience securing agentic applications, RAG systems, enterprise search, vector databases, model-serving APIs, enterprise-data connectors, or comparable AI-enabled systems. - Experience designing identity and access controls using OIDC, OAuth 2.0, SSO, RBAC, ABAC, workload identity, service accounts, secrets management, and least-privilege principles. - Strong knowledge of Kubernetes and container security, including admission controls, network policies, RBAC, runtime protection, image scanning, software supply-chain security, and policy engines. - Experience with secure CI/CD, dependency governance, SBOMs, image signing, provenance, artifact management, and controlled-release processes. - Proficiency in Python, Go, or a similar language for automation, security tooling, integrations, and analysis. - Familiarity with cloud and infrastructure security, including logging, monitoring, incident response, encryption, key management, and security controls for multi-tenant systems. - Knowledge of security and compliance frameworks such as NIST, ISO 27001, SOC 2, CIS Controls, OWASP ASVS, OWASP API Security Top 10, or equivalent frameworks. **KEY COMPETENCIES** - Secure-by-design AI product and application engineering. - Threat modeling for LLM, RAG, agentic, and autonomous workflows. - Application, API, identity, and authorization security. - Secure AI data flows, retrieval controls, and tenant isolation. - Agent governance, tool authorization, human-in-the-loop controls, and action auditability. - Kubernetes, container, CI/CD, and software supply-chain security. - Risk-based prioritization that balances product velocity, user experience, operational reliability, and security requirements. - Developer enablement through reusable guardrails, secure patterns, automation, and clear documentation. - Strong cross-functional communication with AI engineers, product teams, platform teams, DevOps, legal/compliance stakeholders, and Cybersecurity leadership. - Ownership, sound judgment, and the ability to operate effectively in a fast-moving AI product environment. **SUCCESS METRICS** - Percentage of AI products, agentic workflows, RAG systems, and inference services that complete security design review before production release. - Adoption rate of reusable security patterns, guardrails, libraries, templates, and policy controls across AI & Applications projects. - Coverage of authentication, authorization, tenant isolation, approval gates, audit logging, secrets management, and data-protection controls across AI applications. - Reduction in critical and high-risk security findings discovered late in development, during release, or after production deployment. - Mean time to identify, triage, and remediate vulnerabilities affecting AI applications, agent frameworks, model-serving components, APIs, and dependencies. - Reduction in unsafe or unauthorized agent actions, insecure tool calls, sensitive-data exposure events, abnormal retrieval attempts, and inference API abuse. - Security readiness of AI product releases, including completion of threat models, architecture reviews, security testing, operational runbooks, and exception approvals. - Demonstrable improvement in the time required for product teams to adopt approved security controls and obtain production-release clearance. - Quality and completeness of security telemetry, audit evidence, incident investigations, and post-incident remediation. Effective execution of the dotted-line operating model, including consistent alignment with the Head of Cybersecurity on risk posture, standards, escalations, compliance, and incident response **LOCATION** Singapore or Australia ### **Diversity** At Firmus, we are committed to building a diverse and inclusive workplace. We encourage applications from candidates of all backgrounds who are passionate about creating a more sustainable future through innovative engineering solutions. Join us in our mission to revolutionise the AI industry through sustainable practices and cutting-edge engineering. Apply now to be part of shaping the future of sustainable AI infrastructure.