Senior AI Developer

Job Description

At WithSecure™, we protect businesses all over the world. Our SaaS solutions safeguard against modern cyber threats, and our innovative Co-security approach reflects our belief that true protection requires collaboration and shared expertise. No one can solve every cyber security problem alone. Our vision is to become Europe’s flagship in cyber security. Every day, our talented teams work to prevent cyber extortion, secure critical infrastructure, and prevent misuse of sensitive data. At WithSecure, it’s our people who make us exceptional – a diverse community that values passion, purpose, and a commitment to workplace well-being. If you’re ready to make an impact with a company that’s transforming cybersecurity, we’d love to hear from you.

In this role, you create new AI-native products and build agentic capabilities into WithSecure products at a senior level. You work AI-first — designing around what agents make possible rather than bolting AI onto something that wasn't built for it. You operate as a senior agentic engineer: you direct AI agents, own the design of the harder and more ambiguous pieces of work end to end, and you're trusted to make the technical calls within them. You work at the leading edge of the agentic engineering toolkit, picking up new approaches early and bringing back what actually works to your peers. Beyond your own output, you act as a multiplier on the people around you, lifting less experienced developers and strengthening the team's practice through coaching, real examples, and the improvements you drive.

Key Responsibilities

• AI-native product creation. Build new products designed AI-first, where the agent is the core experience.

• End-to-end ownership of harder work. Take on the most ambiguous and highest-stakes agentic pieces. Drive from problem framing through to shipped behavior in production.

• Product opportunity shaping. Spot when an emerging AI capability opens a new product opportunity worth pursuing.

• Coaching less experienced developers. Spend deliberate time growing less experienced developers: pair on hard problems, walk through agent traces together, give code and spec reviews that teach. Notice who needs which kind of help.

• Improving ways of working. Spot where team practices and rituals don't fit AI-native work and drive change by example: better review habits, sharper spec patterns, clearer definition of done for agentic features. Influence by demonstration.

• Improving internal tooling and workflows. Build or champion the shared agent skills, eval harnesses, scripts, and dev loops that compound across the team. Lower the cost of doing the right thing for everyone.

• Tooling adoption. Stay current with the AI engineering tool stack. Evaluate new approaches early; bring proven additions to peers.

• Fast iteration. Move from idea to working prototype in hours or days. Use agentic engineering to compress the build-test-learn loop.

• Spec-driven engineering. Translate product intent into precise, agent-consumable specs. Treat wrong agent output as a signal about spec quality before treating it as a model failure.

• Eval and guardrail design. Define behavioral evals, regression suites, and safety guardrails for agentic features that face customers.

• Production-grade agentic features. Meet the production bar for cost, latency, observability, failure handling, and security in every agentic feature shipped.

• Customer-facing agent quality. Take responsibility for how agentic features behave under real customer use, including edge cases, adversarial inputs, and recoverable failure paths.

• Knowledge sharing. Run brown-bags, write internal notes, and surface what's working.

What are we looking for?

• Deep software engineering foundation: design, debugging, testing, code review, performance, security.

• Hands-on fluency with current agentic engineering practices: headless agent runtimes, MCP and tool composition, subagent orchestration, eval and tracing platforms, spec-driven workflows.

• Experience of shipping agentic features into production systems used by real customers.

• Experience of driving ambiguous problems to shipped outcomes with short iteration cycles.

• Experience of lifting less experienced engineers through coaching, pairing, and review.

• Self-directed learning habit demonstrated by visible adoption of new tools and techniques, ahead of peers.

• Working knowledge of cybersecurity fundamentals, enough to operate credibly in a security product context, without needing to be a security specialist.

Key Competencies:

• Agentic engineering. Directs agents rather than implements code. Decomposes work for agent delegation, writes precise specs, and reviews agent output at the rigor needed for production code.

• Ambiguity comfort. Picks up vague problems, frames them, and drives them to a shipped outcome without external structure.

• Tooling fluency and renewal. Maintains practical mastery of the current agentic engineering toolkit. Picks up new tools quickly and is often first to evaluate the next wave.

• Spec craft. Writes specs that agents can consume directly. Uses agent failure as a diagnostic for spec quality.

• Eval design. Builds behavioral evals that distinguish agentic features that work from those that only look like they work. Capable of designing coverage for the harder, fuzzier cases.

• Context engineering. Structures the information agents receive to maximize output quality, repo configuration, in-context examples, scoped tool access.

• Agent debugging. Traces wrong agent output to its source: spec, context, model reasoning, or tool use.

• Production sense for AI features. Reads cost, latency, failure modes, and security risk of agentic features early enough to design around them.

• Product sense. Reads where AI advances open product space and acts on it with concrete prototypes.

• Coaching through review. Uses code, spec, and eval review as moments to teach.

• Force multiplier. Visibly raises what peers can do through pairing, teaching reviews, shared tools, and the practices they bring into the team.

• Initiative on improvements. Notices what's not working: tools, practices, rituals, and acts on it.

• Critical evaluation. Reviews AI-generated output, code, specs, designs. Recognizes subtly wrong output without rubber-stamping.

Bonus points:

• Strong proficiency in AI technologies and tools.
• Proven experience in applying AI solutions to solve real-world business or technical challenges.
• Ability to collaborate with cross-functional teams to implement AI-driven initiatives.
• Strong problem-solving skills, analytical thinking, and continuous learning mindset to keep up with evolving AI technologies.

If you don’t tick every box above, don’t worry — we’re more interested in your potential and willingness to learn.

The salary displayed on this page represents the starting point for the role. Final compensation is discussed individually and depends on experience, skills, and the local market.

What will you get from us

• Meaningful work with real-world impact – your work helps protect businesses and critical infrastructure from modern cyber threats.
• Flexible ways of working, including up to 30 days per year working abroad and paid sick leave from day one.
• A strong focus on well-being, with mental health support and comprehensive benefits.
• Continuous learning, modern tools, and a people-first culture that values trust and collaboration.