Lead AI Security Engineer

EPAM is a global leader in AI transformation engineering and integrated consulting, serving Forbes Global 2000 companies and ambitious startups. With over thirty years of expertise in custom software, product and platform engineering, they empower clients to become AI-Native enterprises, driving measurable value from innovation and digital investments.

• 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 secure coding principles
• Practical experience with application security tooling such as SAST, DAST, SCA, secrets scanning, and integrating into CI/CD
• Working knowledge of at least one programming language (e.g., 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 on at least one major cloud platform such as Azure, AWS, or GCP
• Experience with production projects or engineering teams
• Ability to work closely with developers, architects, QA engineers, DevOps, product, and security teams and 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 AI agents, integrating LLMs, prompt engineering, and secure AI tools usage
• Good communication skills to explain security risks, technical decisions, and remediation plans to technical and non-technical stakeholders

Nice to have:

• Experience with application security platforms and tools such as Snyk, Checkmarx, Veracode, SonarQube, Semgrep, GitHub Advanced Security, Burp Suite, OWASP ZAP
• 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, Trivy
• Experience with container and Kubernetes security including image scanning, registries, runtime protection, network policies
• Experience with API security, secrets management (e.g., HashiCorp Vault, Azure Key Vault), and microservice security patterns
• Understanding of compliance or security frameworks such as ISO 27001, NIST, CIS Benchmarks, PCI DSS, HIPAA, SOC 2, 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, AutoGen
• Understanding AI and LLM application security risks including prompt injection, insecure output handling, data leakage, model governance, and AI supply chain risks (e.g. OWASP Top 10 for LLM Applications)
• Security certifications such as CSSLP, GWAPT/GWEB, OSCP/OSWE, CISSP, CISM, CCSP, and AI-related certifications like AI-900, AI-102

• 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, partnering with development teams to track issues to remediation
• Define, implement, and maintain security gates and policies in CI/CD pipelines balancing 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, develop and deliver secure-coding training, guidelines, and reusable security patterns for developers
• Define and maintain application security standards, baselines, and policy-as-code; 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
• Build and integrate AI agents and LLM-backed automations into 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, tuning them for accuracy, signal quality, and safe behavior
• Implement retrieval over codebases, security standards, and remediation guidance so AI assistants answer from current, authoritative internal context
• Build evaluation, validation, and human-in-the-loop checkpoints into AI-assisted AppSec workflows including output verification, guardrails, and approval gates
• 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
• Design, implement, and operate security controls for AI- and LLM-powered application features aligned to the OWASP Top 10 for LLM Applications
• Define and enforce guardrails for secure adoption of AI in product engineering, advising development teams on building AI features securely

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