Senior AI Engineer

Responsibilities:

• Ideate, design, prototype, and implement AI solutions that automate IT operations and enhance employee experience.
• Translate operational pain points, incidents, requests, and repetitive engineering tasks into AI-enabled automation opportunities with measurable business value.
• Build and maintain AI, ML, and generative AI components such as LLM-based assistants, RAG pipelines, orchestration workflows, and intelligent remediation capabilities.
• Integrate AI solutions with enterprise platforms and data sources, including IT service management, endpoint management, and employee experience tooling.
• Apply responsible AI, privacy, security, and governance principles throughout design, development, and deployment.
• Use engineering best practices, including version control, peer review, testing, CI/CD, and operational observability.
• Contribute to MLOps and LLMOps practices, including model evaluation, prompt quality, performance monitoring, drift awareness, safety controls and cost management.
• Collaborate with platform leads, architects, security teams, and business stakeholders to shape scalable and supportable solutions.
• Document solutions clearly and support knowledge sharing across the team.
• Drive continuous improvement through experimentation, analytics, and iterative delivery.

Requirements:

• 3-5 years of software engineering, automation engineering or AI engineering experience, with evidence of delivering production solutions.
• University degree in Computer Science, Data Science, Engineering or equivalent relevant experience.
• Strong hands-on experience with Python and common AI or ML libraries and frameworks.
• Practical experience building LLM-based applications, including retrieval-augmented generation, prompt design, tool calling or agent-style workflows.
• Working knowledge of cloud AI services such as Azure OpenAI, Azure AI Foundry, AWS Bedrock or similar platforms.
• Experience integrating with enterprise APIs, data sources and automation platforms; familiarity with vector databases is beneficial.
• Understanding of AI security, privacy, responsible AI practices and relevant regulatory expectations including EU AI Act principles.
• Working knowledge of Azure DevOps or equivalent tooling for source control and CI/CD.
• Strong analytical, documentation and problem-solving skills.
• Strong communication and collaboration skills with the ability to work independently when required.
• Proficiency in written and spoken English.
• German, Italian or Spanish would be an advantage but are not essential.

Beneficial Experience:

• Experience with agentic AI frameworks or orchestration patterns.
• Knowledge of ServiceNow, Microsoft Intune, Nexthink or Microsoft 365 and Copilot ecosystems.
• Exposure to MLOps or LLMOps tools, evaluation frameworks, or model lifecycle management practices.
• Data engineering or analytics skills such as SQL, data pipelines, or Power BI.
• Relevant certifications such as Azure AI Engineer or ITIL Foundation.