Senior Data & AI Engineer (m/f) (AI)

Location: Luxembourg; in-person time is required at an ARHS office

Company

hirify.global, part of Accenture, delivers complex public-sector IT projects covering systems integration, informatics, analytics, solution implementation, and program management.

What you will do

• Lead the machine learning domain by defining engineering standards, ensuring reusable practices, and aligning AI work with organizational goals.
• Architect end-to-end AI/ML pipelines covering data ingestion, feature engineering, model training, deployment, and monitoring.
• Develop and customize LLM solutions using open-source models such as Mistral and LLaMA, including RAG, prompt tuning, model compression, and vector search.
• Design NLP pipelines for information extraction, classification, semantic search, document understanding, OCR, and chatbots.
• Deploy production AI services with high-availability APIs across Azure, AWS, hybrid cloud, on-premises infrastructure, and NVIDIA edge devices.
• Automate ML infrastructure and integrate AI components into event-driven and serverless systems with observability, resilience, and scalability.

Requirements

• At least 5 years of hands-on experience in AI/ML engineering and experience leading complex AI initiatives across full-stack data pipelines and AI systems.
• Strong Python, REST API architecture, performance tuning, and secure API development skills.
• Experience with Transformers, LLMs, RAG architectures, NLP, OCR pipelines, vector search, and model deployment.
• Deep expertise in Azure and/or AWS, plus Docker, GitHub Actions, Terraform, Ansible, and CI/CD pipelines.
• Experience with edge inference, Jetson Orin or Xavier NX, Ollama or ONNX Runtime, hybrid deployments, and workload orchestration.
• Strong communication in English or French is required; leadership, mentoring, autonomous decision-making, Agile, and DevOps experience are expected.

Nice to have

• Background in Java, .NET, TypeScript, or Spring Boot.
• AWS or Azure certification.

Culture & Benefits

• Flexible support for individual work/life needs alongside required in-person collaboration.
• Opportunities to mentor junior and mid-level engineers and share internal ML knowledge.
• Emphasis on clean, documented, maintainable, reproducible, and production-ready AI solutions.
• Focus on ethical AI use and best practices across MLOps and engineering.