Senior AI Developer
On-siteHelsinki, Uusimaa, Finland or Oulu, North Ostrobothnia, Finland
Job Summary
Build AI-native products by designing agentic capabilities that serve as the core experience, moving from idea to prototype in hours. Own end-to-end delivery of ambiguous, high-stakes agentic features, translating product intent into precise specs and driving them to production with rigorous cost, latency, and security guardrails. Coach less experienced developers through pairing, code reviews, and spec analysis while championing shared tooling, eval harnesses, and improved team rituals. Stay current with the agentic engineering toolkit to evaluate new approaches and lower the cost of doing the right thing across the team.
Required Qualifications
- 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
- 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
Desired Qualifications
- 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
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