Asks for

The ad says“Thorough, current, hands-on expertise in AI-native engineering: agents, tool use, multi-step workflows, RAG, context- and harness-engineering, including common failure modes and dependability patterns.”
The ad says“Thorough, current, hands-on expertise in AI-native engineering: agents, tool use, multi-step workflows, RAG, context- and harness-engineering, including common failure modes and dependability patterns.”
The ad says“Deep familiarity with everyday product engineering workflows (code review, CI/CD, incident handling, delivery pressure).”
The ad says“Thorough, current, hands-on expertise in AI-native engineering: agents, tool use, multi-step workflows, RAG, context- and harness-engineering, including common failure modes and dependability patterns.”
The ad says“Practical experience with eval and observability frameworks for non-deterministic, LLM-powered systems.”
The ad says“Practical experience with eval and observability frameworks for non-deterministic, LLM-powered systems.”
The ad says“Thorough, current, hands-on expertise in AI-native engineering: agents, tool use, multi-step workflows, RAG, context- and harness-engineering, including common failure modes and dependability patterns.”
The ad says“Thorough, current, hands-on expertise in AI-native engineering: agents, tool use, multi-step workflows, RAG, context- and harness-engineering, including common failure modes and dependability patterns.”
The ad says“6+ years of software engineering experience with strong production engineering fundamentals (shipping and operating real systems).”
The ad says“Thorough, current, hands-on expertise in AI-native engineering: agents, tool use, multi-step workflows, RAG, context- and harness-engineering, including common failure modes and dependability patterns.”

Senior AI Engineer — AI-native SDLC Platform

Shine · Product & Tech · Engineering Enablement

Location: Berlin (Germany) with option for 2 remote days/week, or remote-friendly across European hubs (Berlin / Copenhagen / Porto / Gdansk / Madrid)

About Shine

Shine is the financial copilot for entrepreneurs and small business owners. Founded by serial entrepreneurs Rico Andersen and Martin Hegelund, Shine is a leading European fintech unicorn on a mission to restore the joy of running a business, by ending wasted time on financial admin. Shine offers a connected solution for invoicing, accounting, payroll, business accounts, payments, and financing, meaning business owners can focus their energy on growing a healthy business, not held back by manual admin.

Part of something bigger

Today we're part of Cegid, a European leader in cloud software for finance and accounting. Together we're building Europe's leading financial copilot for small businesses and their accountants. Shine already supports more than 400,000 small businesses. As part of Cegid, we now reach over one million small businesses and 15,000 accountants across Europe.

We're a multicultural team working from France, Germany, Denmark and the Netherlands, contributing to a wider European network that spans Spain, Portugal, and Poland.

The Engineering Enablement Team at Shine

Shine is moving all of Product & Tech toward AI-native engineering. The Enablement team builds the platform, workflows, and standards that make this vision real—placing tools directly into the hands of engineers. Working under the guidance of program technical leadership, the team turns AI-native SDLC concepts into working software, reference implementations, and widely adopted practices.

Your Role as a Senior AI Engineer

You are where the AI-native platform gets built and lands in the hands of engineers. You will work closely embedded with product engineering teams—close enough to how they build software that your tooling, patterns, and support never feel alien or imposed from the outside.

This is a senior individual contributor role with real delivery ownership. While technical direction is set at the program level, bringing it to life, making it reliable, and ensuring high adoption rests substantially on you.

Your Responsibilities & What You Own

Platform components, end-to-end: Build and maintain concrete parts of the AI-native SDLC platform (agent workflows, MCP servers, skills, configuration presets, eval harnesses, developer tooling). Deliver production-grade components with clear ownership, quality gates, and lifecycles.
Hands-on Platform Engineering (~80% of time): Write production code for agent infrastructure, tooling integrations, MCP servers, and eval pipelines.
Team Adoption & Pairing: Embed with product engineering teams in their codebases to introduce AI-native workflows, debug friction firsthand, run enablement sessions/workshops, and translate blockers into platform improvements.
Working Patterns & Reference Implementations: Produce credible, working examples in real repositories so teams adopt patterns by copying what demonstrably works.
Quality, Evals & Observability: Build eval harnesses, observability, and verification for non-deterministic systems to make agent behavior trustworthy before reaching teams.
Engineering Standards & Frontier Research: Contribute to shared coding conventions, trust/verification policies, and presets while evaluating new models, tools, and protocols (MCP, A2A, agent frameworks).

How We Measure Success

Delivery: Platform components shipped to production quality with evals and observability; trending down rework rates and incidents.
Adoption: Active weekly use of platform workflows by your supported teams; reduced time-to-productivity for adopting new AI-native workflows.
Trust: High DX survey satisfaction, trust in agent-generated work, and engineering teams actively pulling you in rather than platform push.

About You

Must-Have

6+ years of software engineering experience with strong production engineering fundamentals (shipping and operating real systems).
Thorough, current, hands-on expertise in AI-native engineering: agents, tool use, multi-step workflows, RAG, context- and harness-engineering, including common failure modes and dependability patterns.
Practical experience with eval and observability frameworks for non-deterministic, LLM-powered systems.
Deep familiarity with everyday product engineering workflows (code review, CI/CD, incident handling, delivery pressure).
Strong collaborative mindset (teamplay as a working style): comfortable in other teams' codebases, giving/receiving direct feedback.
Strong delivery orientation: iterative shipping, preferring working v1 over perfect plans, with accountability for long-term outcomes.
Excellent technical communication across all engineering levels.

Strong Plus

Hands-on experience with frontier tools: Claude Code, Cursor, LangGraph, MCP server development, harness engineering, agent eval frameworks.
Background in Developer Experience (DX), Platform Engineering, or internal developer platforms.
Awareness of agentic security concerns (prompt injection, privilege escalation, data governance).
Experience supporting engineering teams through major workflow or tooling transitions, or background in regulated industries (fintech, banking).

What This Role Is Not

This is not a research role, a solo-architect role, or a slideware role. It is not a role where you define strategy from a distance or bear the burden of organizational mobilization alone (which receives full executive backing)—your part is making the platform so good and practical that adoption feels like the obvious choice.

Your Hiring Experience Matters

Just as we respect our customers' time, we respect yours. Your experience with Shine and Cegid should feel simple, transparent and genuinely supportive.

If this sounds like somewhere you want to grow, we'd love to hear from you!

Equal Opportunity Employer

We follow the principle of equal treatment to consider all job applicants and do not discriminate based on their gender, sexual orientation, color, racial or ethnic origin, religion, disability, etc. as per applicable law.

Asks for

Values