Senior AI Security Engineer

Embed security into the full software development lifecycle and drive shift-left and secure-by-design practices across engineering teams

Perform and facilitate threat modeling, architecture security reviews, and design reviews for applications, services, and APIs

Conduct secure code reviews (manual and AI-assisted) and advise developers on secure coding patterns and remediation

Implement, configure, tune, and operate application security tooling, including SAST, DAST, IAST, SCA, secrets scanning, and IaC scanning, integrated into CI/CD pipelines

Triage, validate, prioritize, and reduce false positives in security findings, and partner with development teams to track issues through to remediation

Define, implement, and maintain security gates and policies in CI/CD pipelines that balance risk reduction with developer velocity

Secure the software supply chain, including dependency and open-source risk management, SBOM generation, artifact integrity and signing, and build pipeline hardening

Support and coordinate application penetration testing and validate fixes for identified vulnerabilities

Drive secrets management, secure configuration, API security, container and image security, and microservice security practices

Establish and run a security champions program, and develop and deliver secure-coding training, guidelines, and reusable security patterns for developers

Define and maintain application security standards, baselines, and policy-as-code, and contribute to vulnerability management and risk-acceptance processes

Build, deploy, and maintain AI-assisted automations and agentic workflows that reduce manual effort across daily application security activities, such as: vulnerability triage, deduplication, prioritization, and false-positive reduction; automated and assisted code review with concrete remediation guidance and example fixes; threat modeling support and abuse-case and attack-path generation; finding enrichment, root-cause analysis, and remediation-PR drafting; compliance evidence collection; secure-coding documentation, query, and runbook automation

Build and integrate AI agents and LLM-backed automations into the SDLC and CI/CD pipelines, connecting models to scanners, code hosts, ticketing, and security tooling via function calling, REST, and webhooks

Develop, test, and maintain reusable prompts, structured-prompting patterns, and prompt templates for recurring AppSec tasks, and tune them for accuracy, signal quality, and safe behavior

Implement retrieval over codebases, security standards, and remediation guidance (for example RAG) so AI assistants answer from current, authoritative internal context rather than guesswork

Build evaluation, validation, and human-in-the-loop checkpoints into AI-assisted AppSec workflows, including output verification, guardrails, and approval gates before findings, fixes, or pipeline decisions are acted on

Implement security and privacy controls for AppSec AI usage, including least-privilege access for agents, source-code and secrets handling, prompt-injection resistance, and auditability of AI-driven actions

Design, implement, and operate security controls for AI- and LLM-powered application features, including input and output validation, prompt-injection and jailbreak defenses, tool- and function-call authorization, rate limiting, and model and data access governance, aligned to the OWASP Top 10 for LLM Applications

Define and enforce guardrails for secure adoption of AI in product engineering, covering prompt security, model and tool access control, output handling, data protection, auditability, and human-in-the-loop processes, and advise development teams on building AI features securely

Bachelor's degree in Computer Science, Information Security, Engineering, or equivalent practical experience

Hands-on application security experience across the software development lifecycle

Strong understanding of common application vulnerability classes and mitigations, including the OWASP Top 10, and of secure coding principles

Practical experience with application security tooling, such as SAST, DAST, SCA, and secrets scanning, and integrating it into CI/CD

Working knowledge of at least one programming language (for example Python, Java, C#, JavaScript/TypeScript, or Go) sufficient to read code and assess vulnerabilities

Experience with threat modeling and secure design review methodologies

Understanding of DevOps/DevSecOps practices, CI/CD pipelines, and secure-by-design principles

Familiarity with cloud application security concepts across at least one major cloud platform such as Azure, AWS, or GCP

Experience participating in at least several production projects or engineering teams

Ability to work closely with developers, architects, QA engineers, DevOps, product, and security teams, and to influence without owning the codebase

Ability to follow, maintain, and improve defined security processes

Practical understanding of AI-assisted productivity and automation beyond basic chatbot usage, including at least some of the following: building or configuring AI agents; using AI to automate repetitive security or engineering tasks; integrating LLMs with tools, APIs, documents, or workflows; prompt engineering and structured prompting; creating AI-assisted runbooks, scripts, queries, or documentation; using AI tools securely with awareness of sensitive data handling and access control

Good communication skills and the ability to explain security risks, technical decisions, and remediation plans to both technical and non-technical stakeholders

Experience with application security platforms and tools such as Snyk, Checkmarx, Veracode, SonarQube, Semgrep, GitHub Advanced Security, Burp Suite, OWASP ZAP, or similar

Experience with software supply chain security, including SBOM, SLSA, Sigstore, and dependency and artifact integrity controls

Experience with Infrastructure as Code and policy-as-code security tools such as Terraform, Bicep, ARM templates, OPA, Checkov, or Trivy

Experience with container and Kubernetes security, including image scanning, registries, runtime protection, and network policies

Experience with API security, secrets management (for example HashiCorp Vault, Azure Key Vault), and microservice security patterns

Understanding of at least one compliance or security framework, such as ISO 27001, NIST, CIS Benchmarks, PCI DSS, HIPAA, SOC 2, or SOX

Experience integrating security findings with SIEM/SOAR, ticketing, and vulnerability management workflows

Experience with AI/LLM platforms or frameworks such as Azure OpenAI, Azure AI Foundry, Amazon Bedrock, Microsoft Copilot Studio, LangChain, or AutoGen

Understanding of AI and LLM application security risks, including prompt injection, insecure output handling, data leakage, excessive agency, insecure tool use, model governance, and AI supply chain risks (for example, awareness of the OWASP Top 10 for LLM Applications)

Security certifications such as: CSSLP: Certified Secure Software Lifecycle Professional; GWAPT / GWEB: GIAC Web Application Penetration Tester / Web Application Defender; OSCP / OSWE: Offensive Security certifications; CISSP, CISM, CSSLP, CCSP, or similar; AI-related certifications are a plus, for example: AI-900: Microsoft Azure AI Fundamentals; AI-102: Azure AI Engineer Associate