AI Native Senior .NET Engineer

About OPSWAT

OPSWAT is a global leader in IT, OT, and ICS critical infrastructure cybersecurity. They deliver an end-to-end platform that provides public and private sector organizations and enterprises with the critical advantage needed to protect their complex networks, secure their devices, and ensure compliance. With over 20 years of innovative technology, OPSWAT has earned the trust of more than 1,700 organizations, governments, and institutions globally, solidifying their role in protecting the world’s critical infrastructure and securing our way of life.

The Product: MetaDefender MFT

MetaDefender MFT Enterprise is the next-generation evolution of OPSWAT's Managed File Transfer solution – a complete rearchitecture designed for scale, security, and the future. It is built to be:

• A modern, scalable, and resilient platform capable of serving millions of users across enterprise and SaaS deployments.
• A secure file exchange solution for both humans and systems, embedding OPSWAT's threat prevention technology — multiscanning, Deep CDR™ Technology, and DLP — directly into every file flow.
• A future-ready foundation that supports cloud-native SaaS delivery alongside traditional on-premise enterprise deployments.

As an engineer on this project, you will be building MFT Enterprise from the ground up, setting architecture patterns, defining quality standards, and shipping a platform trusted by governments and critical infrastructure operators worldwide.

The Position

At OPSWAT’s Technology Development Centre in Timisoara, they are looking for a talented AI Native Senior .NET Engineer to join their team working on one of their most strategic solutions: MetaDefender Secure Managed File Transfer (MFT). If you’re experienced in backend development, interested in cybersecurity, and excited to make a real-world impact by protecting critical infrastructure, this is your opportunity!

As an AI Native Senior .NET Engineer, you will orchestrate a fleet of AI agents to architect, build, and deploy production-grade systems, with AI agents being a core part of your daily workflow. You will build complex multi-service systems, create custom coding harnesses and test frameworks, and reverse-engineer existing systems entirely through AI workflows. You will be measured by the speed of deployment and the robustness of what you ship, not the volume of your commits. You will think in terms of “how do these pieces fit together as a system” and blend product, design, and analytical thinking into your delivery.

You Will Have the Opportunity to

• Take ownership of features from concept to delivery: write precise specifications, work closely with teammates and stakeholders to refine requirements, and drive your work through to production with full accountability.
• Work with AI as a first-class collaborator — your primary workflow involves orchestrating agents to create specs, generate code and tests, verify results, and perform reviews. Delegate well-scoped tasks to agents, then review, validate, and own every line that ships.
• Participate in code reviews with a security-first lens, offering constructive feedback and holding the quality bar for both human-written and AI-generated code.
• Create custom coding harnesses and test frameworks for structured, repeatable AI-assisted development — giving the team reliable scaffolding that agents can operate within safely and consistently.
• Maintain and evolve the .NET Core codebase, continuously raising performance, scalability, and code quality as the product grows toward millions of users.
• Author and keep current AI context files for every service you own — machine-readable architecture docs that encode coding standards, architectural constraints, and forbidden patterns so agents produce consistent, safe, on-pattern code across the team.
• Help define and codify AI-native engineering practices for the MFT Enterprise team — establishing playbooks, context file standards, and review norms the broader engineering organization can adopt.

What We Are Looking For

• AI-Native Fluency: Deep, hands-on experience integrating AI agents as core engineering tools, not add-ons — you approach every problem with an AI-native mindset. Must show real work (PRs, commit history, or portfolio) where agents drove backend velocity.
• Technical Excellence (.NET Core / C#): Strong proficiency in .NET / C# with a S.O.L.I.D understanding of programming principles, design patterns, and best practices. AI tools amplify strong fundamentals — they do not replace them.
• Mission-Focused Delivery: 5+ years experience developing scalable solutions, including familiarity with relational/document databases and modern CI/CD systems for seamless integration and delivery.
• Quality and Test Discipline: Focus on high-quality tested solutions — proficiency in unit testing with xUnit or equivalent. Uses AI agents to generate test scaffolding quickly, then validates that coverage is real and meaningful.
• Growth Mindset: Passion for continuous learning. The AI coding ecosystem evolves rapidly — new models, agents, and tooling patterns ship constantly. This role requires staying current across the landscape and actively improving team workflows as the toolchain matures.

It Would Be Nice If You Had

• Agent Discipline: Writes precise, unambiguous task specifications that agents can execute without re-prompting, and treats all AI-generated code as a junior engineer’s PR — reviewing for logic, security, edge-case handling, and architectural fit before anything ships.
• Async programming (.NET): Knowledge of asynchronous .NET patterns — async/await, TPL — with experience validating AI-generated async code for thread-safety in high-throughput scenarios.
• Cloud-native and containers: Hands-on with Docker, Kubernetes, and AWS/Azure/GCP — and experience using AI agents to script infrastructure and deployment automation tasks.
• Security review of AI-generated code: Security-first instincts for reviewing AI-generated code: spotting injection risks, insecure defaults, missing input validation, and privilege escalation vectors in file-handling and authentication code.
• AI context file authoring: Experience authoring 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 with running AI agents inside CI/CD pipelines — auto-fixing lint, generating missing tests, or running safe migrations as pipeline steps.
• Personal projects: A home or personal project that demonstrates commitment to programming — including any AI-tooled projects that show how you work with agents.