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Frontier Strike, part of Enterprise Security Technology (EntSecTech), builds and operates the Harness Orchestrator, an autonomous AI red-teaming platform that continuously tests our own identity and AI-integrated systems the way a real attacker would. This role builds the orchestration engine, policy gateways, and secrets-isolation systems that let the team safely point automated adversarial testing at production. AI serves as a core part of the development workflow here, pairing hands-on distributed-systems engineering with modern AI-assisted tooling to ship secure, production-grade code faster.

• Design and extend Go microservices that orchestrate multi-container test suites (primary and utility containers, shared ephemeral volumes, lifecycle synchronization) so new attack and test techniques can be onboarded as repeatable, automated suites.
• Build policy-based sidecar gateways that mediate every test suite's access to its target system, whether mocked, staging, or production, enforcing least-privilege and blast-radius limits as tests move up the risk tiers.
• Implement pipeline stages that dedupe, suppress noise, and auto-escalate critical findings (injection, server-side request forgery (SSRF), privilege escalation, data exfiltration) so only real signal reaches the persistent findings store.
• Design per-test-suite secrets isolation (dedicated vault, pod, and service account per suite) with automated credential rotation, so a compromised or misbehaving test suite can't reach another suite's credentials.
• Deploy and operate containerized workloads on Kubernetes via Helm and Terraform on Amazon Web Services (AWS), at a scale where individual test runs can cost significantly more or less depending on suite complexity.
• Integrate with internal large language model (LLM) gateway services to track token usage and cost per scan, and build throttling and authorization controls for expensive test executions.
• Implement role-based access control (RBAC), network sandboxing, and geofencing so automated red-teaming can be safely extended from test environments to production.
• Monitor and troubleshoot the platform's distributed components, ensuring findings-pipeline data integrity and proactively catching misconfigurations before they become production incidents.
• Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow, pushing the boundaries of AI development tools to deliver secure, optimized, and high-quality code.
• Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale.
• Contribute to building and maintaining shared system context, an explicit repository of system designs, constraints, and standards that enables AI to operate accurately and reliably. Critically evaluate code, whether human- or AI-generated, for correctness, quality, security, and performance.

• 2 - 4 years of professional software development experience.
• Demonstrates proficiency in Go; experience with Python or Java is also acceptable.
• Has experience with distributed systems, microservices, and Representational State Transfer (REST) or gRPC application programming interfaces (APIs).
• Is familiar with Kubernetes, Docker, Helm, and Terraform in a cloud environment, AWS preferred.
• Understands software security fundamentals: the OWASP Top 10, least privilege, and secrets management.
• Shows strong problem-solving and debugging skills in distributed, containerized systems.
• Works comfortably in a large-scale enterprise environment with production-safety constraints.
• Takes a demonstrated, genuine AI-first approach to engineering, using AI to move faster, build fluency across the stack, and contribute well beyond a core specialty.
• Has experience using AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor) in development workflows.
• Applies advanced prompt engineering skills, writing precise, structured prompts and cultivating the system context that makes AI outputs reliable, secure, and production-ready.
• A Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent experience.

• Experience with policy engines (e.g., Open Policy Agent (OPA)) or other fine-grained authorization frameworks.
• Exposure to AI/LLM security, adversarial testing, or red-teaming.
• Experience with secrets vault technologies (HashiCorp Vault, cloud key management services (KMS), etc.).
• Familiarity with sidecar proxy/gateway patterns and mutual Transport Layer Security (mTLS).
• Experience with identity and access management (IAM), cybersecurity, or compliance frameworks (NIST, ISO, SOC 2).

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