AI-First Software Engineer

About the role

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. About AIHR 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. Role and Responsibilities 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. Your responsibilities include: Contributing to technical delivery for the Member Squad across backend services, APIs, integrations, search and personalization features, learning experience improvements, and internal tooling 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 Understanding the full software delivery cycle, from product intent and technical discovery to architecture, implementation, testing, deployment, observability, and production support 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 Creating context packs, repository instructions, rules, prompts, task specs, and reusable workflows that improve AI-agent output and reduce expensive noise Choosing appropriate models and tools for planning, coding, debugging, reviewing, documentation, and summarization instead of defaulting to the most expensive option Managing context layers such as product intent, domain knowledge, architecture constraints, repository patterns, task-specific requirements, test expectations, production risks, and security constraints Reviewing AI-generated code for correctness, security, performance, maintainability, architecture fit, test quality, repository consistency, and domain assumptions Identifying AI-agent failure modes such as hallucinated APIs, shallow tests, overbroad refactors, weak abstractions, hidden security issues, and plausible but incorrect explanations Defining stop conditions for agent work, preventing runaway loops or excessive token usage, and knowing when to take over manually 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 Helping the squad adopt AI-first engineering practices through prompts, review checklists, pairing, documentation and hands-on support 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 typical day 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 co

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