Senior Data & AI Engineer (NLP, LLMs)

TL;DR

Senior Data & AI Engineer (NLP, LLMs): Lead the design, development, and deployment of advanced AI solutions across NLP, computer vision, and LLMs with an accent on end-to-end AI/ML pipelines, production-grade APIs, and hybrid edge-to-cloud deployments. Focus on scaling intelligent systems from edge devices to cloud infrastructure while ensuring privacy, latency, performance, and mentoring engineering teams in MLOps best practices.

Company

ARHS Group (part of Accenture) delivers complex public sector IT projects, including systems integration, informatics and analytics, and AI/ML solutions.

What you will do

• Act as team lead for the ML domain: define engineering standards, ensure reusability across projects, and align AI practices with company goals.
• Architect and oversee AI/ML pipelines from data ingestion and feature engineering to model training, deployment, and post-deployment monitoring.
• Develop and customize LLM-based solutions using open-source models (e.g., Mistral, LLaMA) on hybrid cloud/on-prem infrastructure with privacy, latency, and performance constraints.
• Design and supervise NLP pipelines for information extraction, classification, semantic search, and document understanding.
• Deliver production-grade AI services with high-availability APIs (e.g., FastAPI) and deploy AI workloads to cloud (Azure/AWS) and edge devices (e.g., NVIDIA Jetson).
• Own infrastructure automation and integrate AI components into event-driven/serverless ecosystems (e.g., Azure Functions/Event Grid), ensuring observability, resilience, and scalability.

Requirements

• Location: In-person work at an ARHS – Part of Accenture office in Luxembourg.
• Minimum 5 years of hands-on experience in AI/ML engineering.
• Proven ability to lead and deliver complex AI initiatives across full-stack data pipelines and AI systems.
• Strong programming and API skills (Python, REST API architecture, performance tuning, secure API development).
• Deep expertise in NLP & ML (Transformers, LLMs, RAG architectures, prompt tuning, vector search, OCR pipelines).
• Expertise in Azure and/or AWS plus MLOps/infrastructure (Docker, GitHub Actions, Terraform/Ansible, CI/CD) and edge/hybrid inference (e.g., Jetson, ONNX/Ollama runtime).

Nice to have

• Background in traditional software development (e.g., Java, .NET, TypeScript, Spring Boot).
• AWS/Azure certifications.
• Strong communication in English or French (both is a plus).
• Familiarity with Agile and DevOps cultures.

Culture & Benefits

• In-person time is required to support collaboration, learning, and relationship-building with clients and colleagues.
• Flexible approach to support work/life needs.
• Mentorship and coaching focus, with an emphasis on MLOps, reproducibility, and ethical AI use.
• Opportunity to consolidate and share internal ML know-how across the organization.