AI-First Software Engineer

Are you an engineer who can combine strong software engineering fundamentals with modern AI-agent-based delivery? Do you know how to break complex work into clear, bounded tasks that AI agents can execute safely, then review the output with a sharp critical eye? AIHR is looking for an AI-First Software Engineer to join one of our Squads as a hands-on engineer contributing across product, architecture, and delivery. Join us and help build the learning platform that helps HR professionals around the world grow their careers.

Founded in 2016 with the mission to future-proof HR, the Academy to Innovate HR (AIHR) has become the world's market leader in online training for human resources (HR) professionals. We have a global customer base spread across 140+ countries, amongst which companies like Unilever, Reckitt, Goldman Sachs, Philips, Deloitte, Nike, Heineken, and UBS. It is our goal to continuously upskill and empower 1,000,000 HR Professionals.

We are an international team of 90+ people, driven by excellence, innovation, and a hunger to grow in everything we do. As such, we strive to provide the world's best courses and excellent support to our customers while continuously optimizing every aspect of our work. With over 30 nationalities, our team is diverse, yet we all share a few traits: we're friendly, enthusiastic, and great team players.

Being a fast-growing company, working at AIHR means taking on a lot of responsibility and getting countless opportunities to develop yourself in new areas and the potential to craft your own role.

As an AI-First Software Engineer, you will be a key technical contributor in one of our Squads. The Squad consists of approximately 4 engineers and 1 Product Manager, with a healthy mix of experience levels.

This is a hands-on hybrid tech role. You will still ship production code, make technical decisions, and debug hard problems, but you will do that by orchestrating AI-enabled workflows. You will help the Squad move toward a more AI-native engineering operating model by designing the right context, delegating implementation work to agents, reviewing and validating generated output.

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  Contributing to technical delivery for the Member Squad across backend services, APIs, integrations, search and personalization features, learning experience improvements, and internal tooling

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  Working closely with the Product Manager to translate product goals into technical plans, break down complex initiatives, identify risks early, and keep delivery moving pragmatically

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  Understanding the full software delivery cycle, from product intent and technical discovery to architecture, implementation, testing, deployment, observability, and production support

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  Decomposing complex engineering work into clear, bounded tasks that AI agents can execute safely, with the right context, constraints, examples, acceptance criteria and review loops

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  Creating context packs, repository instructions, rules, prompts, task specs, and reusable workflows that improve AI-agent output and reduce expensive noise

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  Choosing appropriate models and tools for planning, coding, debugging, reviewing, documentation, and summarization instead of defaulting to the most expensive option

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  Managing context layers such as product intent, domain knowledge, architecture constraints, repository patterns, task-specific requirements, test expectations, production risks, and security constraints

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  Reviewing AI-generated code for correctness, security, performance, maintainability, architecture fit, test quality, repository consistency, and domain assumptions

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  Identifying AI-agent failure modes such as hallucinated APIs, shallow tests, overbroad refactors, weak abstractions, hidden security issues, and plausible but incorrect explanations

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  Defining stop conditions for agent work, preventing runaway loops or excessive token usage, and knowing when to take over manually

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  Using retrieval, documentation, code search, and structured context to ground agent work safely, including awareness of MCP-style tool and context integration, RAG, permissions, source relevance, and untrusted tool output

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  Helping the squad adopt AI-first engineering practices through prompts, review checklists, pairing, documentation and hands-on support 

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  Shipping backend-leaning full-stack product changes across our C#/.NET backend, APIs, MongoDB/PostgreSQL data layer, Kafka event-driven architecture, and Angular/TypeScript frontends when needed

On a Monday morning, you start by reviewing the squad's delivery board and aligning with the Product Manager on priorities for the sprint. There is a backend API change needed for a new personalization feature, so you spend the first part of the morning clarifying the product intent, outlining the architecture, and decomposing the work into bounded tasks an AI agent can execute safely.

After lunch with colleagues in our Rotterdam office, you prepare the right context for the agent: relevant repository patterns, domain constraints, acceptance criteria, testing expectations, and examples of similar code. When the first output comes back, you review it carefully, spot a subtle edge case in the data handling logic, and correct it before it reaches CI.

Later in the afternoon, you pair with one of the engineers who has questions about the CQRS pattern used in the codebase and how to safely delegate part of the implementation to an AI agent. You use it as an opportunity to narrow the task, define the expected behavior, limit context, and review the generated tests instead of blindly trusting the machine because it sounded confident.

On a Friday, you present a short retrospective on the AI-agent workflow you introduced this week: what worked, what produced noise, where the agent made incorrect assumptions, and how you tightened the prompts, context packs, and review criteria for next time. Afterwards, there is the weekly team activity, drinks, good conversation, and a chance to catch up with colleagues from other squads. We work hard at AIHR, but we genuinely enjoy doing it together.

We're looking for someone with strong engineering fundamentals, a genuine appetite for AI-first ways of working and the instinct to make the people around them better - not just deliver great work themselves.

• Solid professional experience as a software engineer (at least 3 years of experience)

• Strong backend engineering experience with C# / .NET - this is a hard requirement

• Proven experience building and maintaining production systems, including APIs, service architecture, databases, CI/CD, testing, observability, and production support

• Understanding of the full software delivery cycle and the ability to reason across backend, frontend, infrastructure, testing, and product constraints

• Hands-on experience using AI coding tools or agents such as Claude Code, Cursor, Codex, GitHub Copilot, or similar tools as part of real delivery work, or strong motivation and ability to adopt them quickly

• Ability to decompose engineering work into clear, bounded tasks for AI agents, including context, constraints, examples, acceptance criteria, and review loops

• Ability to review, validate, and correct AI-generated code with strong judgment around architecture, security, performance, maintainability, test quality, and domain fit

• Awareness of AI-agent concepts such as context engineering, MCP-style tool and context integration, RAG, retrieval quality, model selection, token budgets, evals, guardrails, and human-in-the-loop validation

• Ability to identify hallucinated APIs, shallow tests, overbroad changes, weak abstractions, security issues, and incorrect domain assumptions in AI-generated output

• Comfortable working closely with Product Managers and translating product goals into engineering execution

• Comfortable supporting other engineers through pairing, code reviews and reusable AI-first workflows

• Strong written and verbal communication in English - you can explain your decisions, trade-offs, risks, and validation approach clearly

• Familiarity with event-driven systems (Kafka), MongoDB, PostgreSQL, AWS, GitHub Actions, Datadog, Auth0, or search/personalization systems is a strong plus

• Angular / TypeScript experience is welcome but not required - a strong backend engineer who can navigate the frontend with AI support is more valuable to us than someone who manually perfects CSS while the backend suffers