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, EPAM empowers 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, and secrets scanning, integrated into CI/CD
• Working knowledge of at least one programming language (e.g. Python, Java, C#, JavaScript, TypeScript, or Go) 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 for at least one major cloud platform such as Azure, AWS, or GCP
• Experience participating in production projects or engineering teams
• Ability to collaborate with developers, architects, QA engineers, DevOps, product, and security teams
• Ability to follow, maintain, and improve defined security processes
• Practical understanding of AI-assisted productivity and automation beyond basic chatbot usage, including building/configuring AI agents, integrating LLMs with tools, and prompt engineering
• Good communication skills to explain security risks, technical decisions, and remediation plans to diverse 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, or Trivy
• Experience with container and Kubernetes security, image scanning, registries, runtime protection, and network policies
• Experience with API security, secrets management (e.g., HashiCorp Vault, Azure Key Vault), and microservice security patterns
• Understanding of compliance/security frameworks 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, and model governance
• Security certifications like CSSLP, GWAPT/GWEB, OSCP/OSWE, CISSP, CISM, CCSP, or AI-related certifications such as AI-900 and 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, and partner with development teams to track issues through 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, 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 to reduce manual effort across 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
• Develop, test, and maintain reusable prompts, structured-prompting patterns, and prompt templates for recurring application security tasks
• Implement retrieval over codebases, security standards, and remediation guidance to enable AI assistants to answer from authoritative internal context
• Build evaluation, validation, and human-in-the-loop checkpoints into AI-assisted workflows including output verification and approval gates
• Implement security and privacy controls for AppSec AI usage, including least-privilege access, prompt-injection resistance, and auditability
• Design, implement, and operate security controls for AI- and LLM-powered application features aligned to OWASP Top 10 for LLM Applications
• Define and enforce guardrails for secure AI adoption in product engineering and advise development teams on secure AI feature building

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