Director of Engineering (AI)

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. 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 principles, and modern architecture. You can evaluate AI-assisted code with the same rigor as human-authored code. AI tools amplify strong fundamentals; they do not replace them. Scalable systems experience: a proven history building scalable, distributed solutions — including relational/document databases, cloud-native services, and modern CI/CD systems. Quality and test discipline: a high bar for tested, maintainable code; you use AI to accelerate test scaffolding, then validate that coverage is real and meaningful. Distributed-team leadership: demonstrated ability to lead and align engineers across countries and time zones, building trust, clarity, and accountability in a remote/hybrid, multi-country setup. Talent magnet and clear communicator: you identify strong engineers, make a compelling case for why they should join, and create an environment where they do their best work — and you communicate technical decisions to non-technical stakeholders without oversimplifying. Security-conscious by default: operating in critical infrastructure markets means security is never an afterthought; you hold the team to a security-first bar on design, code review, and incident response. Nice To Have: Domain familiarity: experience with managed file transfer, secure content delivery, data security, or critical infrastructure markets — you understand the trade-offs customers in this space make. .NET / C# ecosystem: hands-on or managerial experience with .NET Core / C# — enough to evaluate architectural decisions, review PRs credibly, and hire well for the stack. Cloud-native and containers: experience leading the design or operation of cloud-native SaaS platforms — Docker, Kubernetes, and AWS/Azure/GCP — including using AI agents to script infrastructure and deployment automation. AI context-file authoring: experience writing AI context files (e.g., CLAUDE.md, .cursorrules, or equivalent) that encode architecture decisions and forbidden patterns for consistent, safe AI output across a team. AI in CI/CD pipelines: familiarity running AI agents inside pipelines — auto-fixing lint, generating missing tests, or running safe migrations as pipeline steps — not as experiments, but as how the team actually works. International team experience: a track record of building and maintaining engineering culture and delivery quality across multiple countries and time zones. Async and high-throughput systems: experience validating concurrency and thread-safety in high-throughput services. Stable, growing international company background with an exceptional customer group Opportunity to improve your professional skills The newest technology environment Language course and opportunity for active recreation – kettlebell, football and office massage Attractive working environment – nice office full of accessories (fruits every day, coffee, breakfast, tea etc.) Regular team events and Happy Hour activities