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 of secure coding principles
• Practical experience with application security tooling such as SAST, DAST, SCA, secrets scanning, and their integration into CI/CD pipelines
• 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 across major cloud platforms 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, influencing without owning codebase
• Ability to follow, maintain, and improve defined security processes
• Practical understanding of AI-assisted productivity and automation beyond basic chatbot usage, including building or configuring AI agents, LLM integration, prompt engineering, and secure AI tool usage
• Good communication skills for explaining 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 like Terraform, Bicep, OPA, Checkov, Trivy
• Experience with container and Kubernetes security, including image scanning, registries, runtime protection, network policies
• Experience with API security, secrets management tools such as HashiCorp Vault, Azure Key Vault, and microservice security patterns
• Familiarity with compliance/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 such as Azure OpenAI, Amazon Bedrock, Microsoft Copilot Studio, LangChain, AutoGen
• Understanding of AI and LLM application security risks including prompt injection and model governance
• Security certifications like CSSLP, GWAPT, OSCP, CISSP, CISM, 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; 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 and developer velocity
• Secure the software supply chain, including dependency and open-source risk management, SBOM generation, artifact integrity and signing, 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; develop and deliver secure-coding training, guidelines, and reusable security patterns
• 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 to reduce manual effort in AppSec activities such as vulnerability triage and automated code review
• Build and integrate AI agents and LLM-backed automations into the SDLC and CI/CD via function calling, REST, and webhooks
• Develop, test, and maintain reusable prompts, structured-prompting patterns, and prompt templates for recurring AppSec tasks
• Implement retrieval over codebases, security standards, and remediation guidance (e.g., RAG) for AI assistants
• Build evaluation, validation, and human-in-the-loop checkpoints into AI-assisted workflows, including output verification, guardrails, and approval gates
• Implement security and privacy controls for AppSec AI usage, including least-privilege access, secrets handling, prompt-injection resistance, and auditability
• Design and operate security controls for AI- and LLM-powered application features aligned with 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, data protection, auditability, and human-in-the-loop processes

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