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, and secrets scanning integrated into CI/CD pipelines
• 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 on major platforms like Azure, AWS, or GCP
• Experience participating in multiple production projects or engineering teams
• Ability to collaborate 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 chatbots including AI agents, LLM integration, prompt engineering, runbook automation, and secure AI tool 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, 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 tools like HashiCorp Vault or 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, 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 (e.g., OWASP Top 10 for LLM Applications)
• Security certifications such as CSSLP, GWAPT/GWEB, OSCP/OSWE, CISSP, CISM, CSSLP, CCSP, or AI-related certifications like AI-900, AI-102

• Embed security into the full software development lifecycle and promote 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 manual and AI-assisted secure code reviews 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 for remediation
• Define, implement, and maintain security gates and policies in CI/CD pipelines balancing risk reduction and 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 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
• Define and maintain application security standards, baselines, and policy-as-code, contributing to vulnerability management and risk-acceptance processes
• Build, deploy, and maintain AI-assisted automations and agentic workflows for vulnerability triage, code review, threat modeling, and compliance
• Integrate AI agents and LLM-backed automations into the SDLC and CI/CD pipelines via function calling, REST, and webhooks
• Develop, test, and maintain reusable prompts, structured-prompting patterns, and prompt templates for AppSec tasks
• Implement retrieval over codebases, security standards, and remediation guidance so AI assistants provide authoritative answers
• 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, secret handling, prompt-injection resistance, and auditability
• Design and operate security controls for AI- and LLM-powered features aligned with the OWASP Top 10 for LLM Applications
• Define and enforce guardrails for secure AI adoption in product engineering and advise development teams on AI feature security

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