Senior AI Platform Engineer (LLMOps & MLOps) (m/f) at ARHS

Luxembourg, , Luxembourg -

Full Time

Start Date

Immediate

Expiry Date

05 Oct, 26

Salary

0.0

Posted On

07 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

LLMOps, MLOps, Kubernetes, Linux, Cloud-native Infrastructure, AI Engineering, Vector Databases, Agentic Workflows, SDLC, Model Context Protocol, Containerization, Automation, French Fluency, English Fluency, Platform Engineering, Developer Tooling

Industry

IT Services and IT Consulting

Description

Company Description Arηs Group, Part of Accenture, specializes in the management of complex public sector IT projects, including systems integration, informatics and analytics, solution implementation and program management. Our team helps lead clients through digital and information systems design, bringing expertise in a variety of areas ranging from software development, data science and security management to machine learning, cloud, and mobile development. Arηs Group was acquired by Accenture in July 2024. Job Description ARHS Group, part of Accenture, is looking for a Senior AI Platform Engineer (LLMOps & MLOps) m/f to join one of our client teams onsite in Luxembourg within the insurance sector. In this role, you will help industrialize the AI ecosystem supporting software engineering teams, contributing to the development of scalable AI platforms, developer tooling, and AI-powered workflows across the Software Development Lifecycle (SDLC). You will work closely with platform engineers and technical experts to integrate modern AI technologies into enterprise development practices. Please note that fluency in French is mandatory for this position. THE WORK: Contribute to the industrialization of AI engineering platforms and developer AI ecosystems. Design and implement AI engineering solutions based on LLMOps, AI coding assistants, and modern AI platform architectures. Develop and deploy AI tooling, including Model Context Protocol (MCP) services, internal marketplaces, and deployment packages. Contribute to the implementation of end-to-end agentic workflows across the Software Development Lifecycle (SDLC). Support the integration of AI technologies into development, testing, deployment, and operational processes. Collaborate with platform engineering teams to build scalable, secure, and reliable AI infrastructure. Contribute to the continuous improvement of AI engineering practices, developer experience, and platform automation. Produce and maintain technical documentation and share best practices with engineering teams. Onsite at client site: This role requires an onsite presence with our clients and partners to support project delivery and strengthen client relationships. Our roles require in-person time to encourage collaboration, learning, and relationship-building with clients, colleagues, and communities. As an employer, we will be as flexible as possible to support your specific work/life needs. HERE’S WHAT YOU’LL NEED: At least 5 years of experience working with platform engineering, Kubernetes, Linux, or cloud-native infrastructure. Hands-on experience with AI Engineering architectures, including LLMOps, AI coding tools (such as Claude Code or similar), vector databases, and AI engineering concepts. Experience implementing or contributing to an agentic workflow across the Software Development Lifecycle (SDLC). Strong knowledge of Kubernetes, Linux, containerized environments, and cloud-native technologies. Experience with AI platforms, MLOps tools, and automation practices. Good understanding of developer tooling and software engineering best practices. Strong analytical and problem-solving skills. Ability to collaborate with cross-functional engineering teams in a fast-paced environment. Fluency in French is mandatory, with a good command of English. BONUS POINTS IF YOU HAVE: Experience with Model Context Protocol (MCP). Knowledge of context engineering, knowledge bases, prompt engineering, or AI skills frameworks. Experience building AI developer tools, internal marketplaces, or deployment packages (RPM or similar). Experience with DevOps, GitOps, CI/CD, or infrastructure automation. Familiarity with modern AI frameworks and enterprise AI governance.

Responsibilities

Industrialize the AI ecosystem by designing and implementing scalable AI platforms, LLMOps solutions, and agentic workflows across the SDLC. Collaborate with platform engineering teams to integrate AI technologies into enterprise development, testing, and operational processes.