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København, Region Hovedstaden, Danmark
Onsite
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About the Team
About the Role
The Peakon MLOps Engineer is responsible for operating, hardening, and continuously improving the production infrastructure that powers the Peakon Agent, AI Features and related ML workloads. This ownership spans deployments, monitoring, and on-call workflows. Key operational functions include managing the entire deployment lifecycle for the Peakon Agent and other AI Features, contributing to driving operational excellence across the ML platform. Furthermore, the role involves building and maintaining tooling to surface evaluation data, supporting performance testing and load simulations, and collaborating on the automation of essential security upgrades for ML dependencies.
About You
Basic Qualifications Summary
Minimum 8 years of relevant industry experience. Holds a Bachelor’s/Master or PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, or equivalent practical experience.
Proven track record as an MLOps or ML-savvy SRE/Platform Engineer supporting production-grade LLM and agentic systems. Proficient in Python and frameworks like LangChain and LlamaIndex.
Hands-on experience operating containerized services (Docker, Kubernetes) using Git-based workflows (GitOps, GitHub Actions). Solid understanding of modern ML stacks (platforms, feature stores, registries, messaging layers) or deep platform engineering background.
Demonstrated ability to own production infrastructure end-to-end—managing monitoring, incident response, rollbacks, and continuous reliability/uptime improvements.
Deep understanding of the model development lifecycle, specifically regarding model monitoring, regression tracking, and automated evaluation using tools like LangSmith.
Strong communication and collaboration skills under pressure, acting as a bridge between ML engineers, backend teams, and central platform/security specialists.
Other Qualifications Summary
Solid knowledge of data science principles and ML algorithms applied directly to LLMs, RAG, and autonomous decision-making agents.
Experience leading model-building processes, including advanced fine-tuning, alignment techniques, prompt engineering, and simulations of agent behaviors.
Strong understanding of software development principles coupled with demonstrated proficiency in System Design and Architectural Governance to deploy, scale, and maintain high-availability ML models.
Expertise in threat modeling and security for ML/agent systems to enforce strict behavioral guardrails. Proven experience navigating highly regulated enterprise environments to ensure data auditability, clear ownership boundaries, and strict compliance.
Track record of strong technical decision quality and leadership, with the ability to coordinate cross-functional initiatives, translate complex business needs into resilient implementations, and mentor team members while bridging the gap between ML engineering and platform teams.
Workday Pay Transparency Statement (For EU Locations Only)
Listed below is the base salary range applicable to this position. Workday pay ranges (and the precise pay offered to the successful candidate) are based on a number of objective criteria such as relevant experience and skills, and educational qualifications, level of responsibility, demands of the role, work location and business need. As a part of the total compensation package, this role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus, as well as annual refresh stock grants awarded by Workday Inc. For more information regarding Workday’s comprehensive benefits, please click here.
Primary Location Base Pay Range: kr838,000 DKK - kr1,256,000 DKK Denmark
Additional Location Base Pay Range: €96,000 EUR - €144,000 EUR Ireland
Our Approach to Flexible Work
With Flex Work, we’re combining the best of both worlds: in-person time and remote. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. We know that flexibility can take shape in many ways, so rather than a number of required days in-office each week, we simply spend at least half (50%) of our time each quarter in the office or in the field with our customers, prospects, and partners (depending on role). This means you'll have the freedom to create a flexible schedule that caters to your business, team, and personal needs, while being intentional to make the most of time spent together. Those in our remote "home office" roles also have the opportunity to come together in our offices for important moments that matter.
Pursuant to applicable Fair Chance law, Workday will consider for employment qualified applicants with arrest and conviction records.
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