Director of Engineering (AI)

About this role

Our client is a global leader in cybersecurity for IT, OT, and ICS critical infrastructure. Their end-to-end platform gives enterprises and public sector organizations the critical advantage they need to protect complex networks, secure devices, and meet compliance requirements.
Over the past 20 years, a consistent commitment to innovative technology has earned the trust of more than 1,700 organizations, governments, and institutions worldwide — cementing the company's role in protecting the world's critical infrastructure and securing our way of life.
On their behalf, we are looking for a Director of Engineering ready to contribute to that mission.
This is a hands-on, technically-centric leadership role — not a pure people-management position — with full accountability for the engineering of MetaDefender Managed File Transfer: architecture, delivery, quality, and the teams that build it. You will lead engineering across multiple deployment form factors — from air-gapped and cross-domain environments to internet-connected and native-cloud deployments — operating with the rigor of a principal engineer and the clarity of an executive.
You are an AI-native engineering leader. You have personally orchestrated fleets of AI agents to architect, build, test, and ship production systems, and you have launched and released real products through a full AI-powered software development lifecycle (SDLC) — from specification and code generation to automated testing, review, and deployment. Product Management defines what to ship; you define how to build it. You own the end-to-end delivery of the roadmap Product Management sets, hold the delivery bar, and build a high-performance culture that the rest of our client's engineering portfolio will look to as a model for AI-native development. You report to the VP, Products, and lead a distributed team across Romania, Vietnam, and Hungary, while the role itself can be based anywhere, with a preference for the UK, USA.

Your Responsibilies:

• Lead as an AI-first effort: make AI agents a first-class part of how the team works — orchestrating agents to create specs, generate code and tests, verify results, and run reviews — while ensuring humans own and validate every line that ships. Treat automation and intelligence as the default, not the exception.

• Stay hands-on: remain directly in the codebase — setting architecture patterns, writing and reviewing critical code, prototyping, and unblocking hard technical problems. You lead by doing, not only by delegating.

• Own end-to-end delivery of the roadmap: take full ownership of delivering the product roadmap set by Product Management — turning the “what to ship” into the “how to build,” and owning execution sequencing, technical risk, quality, security, and release across every deployment form factor.

• Set technical direction (the “how”): drive the architecture and technology choices that underpin a product serving high-assurance environments at scale. Hold a strong engineering point of view and be willing to defend it.

• Build and lead a high-performance team: recruit, develop, and retain top engineers and their leads across Romania, Vietnam, and Hungary; create clear career paths and norms for code quality and review; and build a culture where strong engineers want to stay and grow.

• Drive performance-based management: set clear, measurable expectations and run a rigorous, fair performance culture — recognizing and accelerating top performers, raising the bar continuously, and addressing underperformance decisively by coaching where there is a path and upgrading or exiting the role where there is not.

• Hold a security-first quality bar: champion code review with a security lens for both human-written and AI-generated code, catching injection risks, insecure defaults, missing validation, and privilege-escalation vectors in file-handling and authentication paths before they ship.

• Raise engineering velocity: continuously improve how the team works — CI/CD pipelines, AI-assisted development workflows, incident response, and operational maturity — so the team ships faster and more reliably over time.

• Codify AI-native practices for the portfolio: establish the playbooks, AI context files, coding harnesses, test frameworks, and review norms developed on this product so other product teams can adopt them.

• Partner cross-functionally: work shoulder-to-shoulder with the VP, Products, plus Product Management, Design, and security leaders — turning the product roadmap (the what) into engineering execution (the how), committing to scope and timelines, and pushing back clearly when trade-offs require it.
Requirements

• Full agentic development experience (required): you have personally run a full agentic, AI-powered SDLC end to end — and have done so in B2B and high-security enterprise environments where compliance, data sensitivity, and assurance requirements are non-negotiable. You can show real work (PRs, commit history, shipped products, or a portfolio) where agentic workflows drove delivery — not slideware about AI.

• A product launched via a full AI-powered SDLC: demonstrable experience taking a product from spec to release using an end-to-end AI-assisted lifecycle (spec generation, code/test generation, automated verification, AI-assisted review, and CI/CD), with you accountable for the outcome.

• Proven engineering leadership: 8+ years of software engineering experience and 3+ years leading engineering teams (including managing managers/leads) through complex product delivery at scale. You can point to the decisions you made and why.

• Performance-based leadership: a track record of building high-performing teams through rigorous, fair performance management — promoting excellence, making timely talent decisions, and reducing or replacing low performers when needed.

• Deep technical excellence: strong, current software engineering fundamentals — solid command of design patterns, SOLID…