Senior AI Engineer - MLOps, Evaluation & Production AI

DTU — Technical University of Denmark; Denmark; Hybrid; DKK 900,000 - 1,400,000; Full time

Benefits offered by this job

Health insurance

Pension scheme

Sixth week of holiday

Lunch scheme

Job summary

Rambøll Stakeholder Intelligence is expanding its AI Engineering function and seeking a senior AI/ML engineer to mature the AI practice across SaaS products such as SurveyXact, PeopleXact and HeartMind. You will guide evaluation, MLOps, and deployment while collaborating with product teams and stakeholders.

The role is hands-on and contributor-focused, with broad cross-product influence, mentoring responsibilities, and a strong emphasis on secure, ethical AI practices within a hybrid work setup.

Qualifications

• Several years of hands-on experience building and operating ML/LLM systems in production.
• Experience with evaluation and MLOps frameworks, including golden datasets, deterministic tests, and regression analysis.
• Deep MLOps practice: deployment, lifecycle management and monitoring in production.
• Experience taking prompt engineering and RAG beyond proof-of-concept into reliable production systems.
• Hands-on experience with AI agents and understanding their failure modes.
• Proficiency in Python (Java a plus).

Responsibilities

• Define evaluation practice for model, prompt, or system performance using golden datasets and human evaluation.
• Establish MLOps foundations for on-prem open-source LLMs, including deployment pipelines and versioning.
• Build production-grade LLM features in collaboration with product teams.
• Mentor AI Engineers and establish patterns and best practices across the team.
• Engage with subject-matter experts and customer teams to ensure secure, scalable AI usage.

Skills

Python

Java

Job description

Rambøll Stakeholder Intelligence is expanding its AI Engineering function and seeking a senior AI/ML engineer to mature the AI practice across SaaS products such as SurveyXact, PeopleXact and HeartMind. You will guide evaluation, MLOps, and deployment while collaborating with product teams and stakeholders.

The role is hands-on and contributor-focused, with broad cross-product influence, mentoring responsibilities, and a strong emphasis on secure, ethical AI practices within a hybrid work setup.