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About the Role

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. This 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 focused on delivering trusted AI features without unnecessarily slowing innovation, while ensuring AI products protect customer, enterprise, and operational data.

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.

Requirements

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.

Skills

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
Developer enablement
Cross-functional communication
Ownership
Sound judgment
Python
Go

Location

Singapore
Australia

Work Type

Full-time

Experience Level

Senior

About the Company

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.
We design, build, and operate a new class of digital infrastructure – the AI Factory.
Our model-to-grid technology approach has pushed the boundaries of multi-generational liquid cooling systems, energy management, AI software orchestration, and construction.
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.
Firmus AI Cloud is our large-scale GPU cloud platform, purpose-built to deliver energy-efficient AI compute at scale.
It empowers developers, enterprises, educational institutions, and government users to train and deploy AI models with unmatched efficiency and cost savings.
As an NVIDIA Cloud and Engineering partner in Asia Pacific, you will gain skills, experience, and exposure across the AI industry.
We are founder-led, not a big corporate, with fast decision-making and minimal bureaucracy.
Ownership comes early, and you will have a direct line to outcomes.
Work alongside founders and experts in AI infrastructure, energy systems and next-generation compute.
We back our people to grow into new domains and take on challenges beyond their previous experience.

Equal Opportunity

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.