GenAI Engineer – AI Center of Excellence – The Hague (Hybrid) | Visa + Salary Insights
📍 Location: nl
🏷 Type: Not specified
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Job Overview
This Mid-Level GenAI Engineering role focuses on building production-ready AI systems within an enterprise AI Center of Excellence. Based in The Hague, this position targets engineers with 2–5 years experience who can design scalable, reusable AI assets using modern LLM frameworks and cloud infrastructure. The ideal candidate combines software engineering rigor with applied machine learning deployment expertise, working across business units to deliver high-impact, enterprise-grade AI solutions.
🌍 Work Eligibility & Location
- 🌍 Visa Sponsorship: Not specified
- ✈️ Relocation Support: Not specified
- 🏠 Remote Type: Hybrid
- ⏰ Timezone Requirement: European business hours
- 🌐 Country Restrictions: EU-based candidates preferred
- 🗣️ Language Requirement: English
This is a Hybrid role anchored in the Netherlands, requiring proximity to The Hague for collaboration. While explicit visa sponsorship is not confirmed, candidates already authorized to work in the EU will have a significant advantage. International applicants may face limitations unless sponsorship is clarified during the hiring process.
💰 Salary Intelligence
- 💰 Official Salary: €4,827 – €6,895/month
- 📊 Estimated Range: €75,000 – €105,000/year
- 📈 Level: Mid-Level
The compensation is highly competitive for a Mid-Level AI Engineering role in Europe, especially within the financial services sector. The inclusion of a 13th-month salary and additional benefits further strengthens the total package. Compared to similar GenAI roles, this range aligns with top-tier enterprise positions requiring both ML deployment and backend engineering expertise.
📊 Role Breakdown
This role is heavily execution-focused, combining AI engineering and platform development. Approximately 40% of the work involves designing and building reusable AI components using frameworks like LangChain and LangGraph. Another 25% is dedicated to deploying models as APIs using FastAPI and integrating them into enterprise systems. Around 20% focuses on CI/CD pipelines, testing, and performance optimization, including latency reduction and caching strategies. The remaining 15% involves cross-functional collaboration, translating business requirements into scalable AI solutions and contributing to innovation initiatives. Success in this role requires consistently delivering production-grade AI systems that are reusable, secure, and aligned with enterprise architecture standards.
🧩 Required Skills & Fit
- ✅ Must: Strong Python and FastAPI development experience
- ✅ Must: Hands-on with LangChain, LangGraph, or similar LLM frameworks
- ✅ Must: Experience with CI/CD, Docker, Kubernetes, and cloud platforms (preferably Azure)
- ➕ Bonus: Experience with Terraform and infrastructure as code
- ➕ Bonus: Background in ML lifecycle and data pipelines
📈 Difficulty & Competitiveness
- ⚡ Level: Moderate–High
- 📊 Experience barrier: 2–5 years
- 🧠 Skill complexity: High (LLMs + DevOps + Backend)
- 🌍 Competition: Strong across EU AI talent pool
This role sits at a Moderate–High difficulty level due to the combination of LLM frameworks, cloud engineering, and production deployment expectations. The 2–5 years requirement is accessible, but the real barrier is demonstrating applied experience with end-to-end AI systems. Competition is intense among engineers transitioning into GenAI roles within Europe.
🚀 Career Impact
- ⭐ Impact Rating: ⭐⭐⭐⭐☆
- 🏢 Brand value: Strong enterprise AI exposure
- 📚 Skill growth: Advanced GenAI + MLOps
- 🚀 Future opportunities: AI Architect, Staff Engineer, GenAI Lead
This role delivers strong career acceleration by positioning you at the intersection of enterprise AI adoption and GenAI system design. You will gain hands-on exposure to scalable AI architecture, significantly improving your profile for future roles such as AI Architect or GenAI Lead. The emphasis on reusable assets also builds long-term platform engineering credibility.
📋 Key Responsibilities
You will design, build, and maintain reusable AI systems using LangChain and LangGraph. A core responsibility is to deploy machine learning models as APIs using FastAPI and integrate them into enterprise environments. You will optimize system performance through latency improvements and caching strategies while ensuring security and compliance. Additionally, you will collaborate cross-functionally with data scientists and product teams to improve the ML lifecycle. Expect to contribute to CI/CD pipelines, conduct code reviews, and continuously enhance platform scalability using cloud-native tools like Azure, Docker, and Kubernetes.
🎯 Application Strategy
- 🎯 Best apply method: Apply via official company careers page
- 🔥 Highlight: Production-level LLM applications
- 🔥 Highlight: Experience with API deployment and CI/CD
- ❌ Avoid: Generic ML-only resumes without deployment experience
- ❌ Avoid: Lack of real-world AI system scalability examples
🧠 Application Optimization (Adaptive)
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Mid-level GenAI Engineer building reusable enterprise AI systems using LLM frameworks, APIs, and cloud infrastructure.
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📅 Application Signals
This role shows high urgency as it was recently posted, indicating active hiring demand within the AI team. Given the rise of GenAI adoption, expect strong competition from candidates with LLM and MLOps experience. Early applications with strong project portfolios will significantly improve your chances of progressing to interview stages.
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✅ Job Source & Verification
This job listing originates from the official careers portal of a major financial services organization, ensuring high source credibility. The posting was marked as recently updated, indicating that the position is actively being recruited for. Always verify details directly on the company site before applying to ensure accuracy and application validity.
