Senior Machine Learning Engineer

Cari; København; Hybrid; DKK 651,000 - 957,000; Full time

Job summary

Cari is seeking a Senior Machine Learning Engineer to turn models and AI prototypes into reliable production services. You will design, deploy and operate machine learning, generative AI and agent-based applications on Databricks, AWS and Azure, and set engineering practices that keep them performing.

Your work helps colleagues move from promising ideas to dependable tools they use every day, supporting progress for people living with serious chronic diseases.

Qualifications

• Degree in computer science, engineering, mathematics or related field, or equivalent practical experience.
• 5+ years in machine learning engineering, software engineering or applied AI with production ML systems.
• Strong hands-on Databricks expertise, including Python, PySpark, Spark SQL, Delta Lake, MLflow, production pipelines, model serving, workflow orchestration and Unity Catalog governance.
• Strong practical knowledge of AWS and Azure for deploying and operating ML or AI workloads, along with Git, CI/CD, containers and infrastructure as code, such as Terraform.
• Demonstrated implementation experience across MLOps, LLMOps, AgentOps and DataOps, from reproducible training, model registries and rollback to retrieval-augmented generation, safety testing, agent tracing, tool permissions, data contracts and lineage.
• Strong Python and SQL skills, solid machine learning fundamentals, experience with frameworks such as scikit-learn, PyTorch or TensorFlow, and practical experience building LLM applications with embeddings, retrieval, structured outputs and tool calling.
• Experience with observability, incident response and production support, an understanding of data privacy, security, responsible AI and model governance, and the communication skills to explain technical trade-offs and mentor colleagues.

Responsibilities

• Design, build, deploy and maintain ML models, LLM applications and AI agents, including batch and real-time inference services integrated into business applications.
• Build training, evaluation, deployment and monitoring workflows with Databricks and MLflow, and operate AI infrastructure on AWS and Azure that balances performance, reliability, security and cost.
• Establish MLOps, LLMOps, AgentOps and DataOps practices - covering versioning, automated testing, rollback, safety controls, human oversight and reliable, well-governed data.
• Monitor model quality, data drift, latency, availability and cost, investigate failures and define evaluation criteria that measure technical quality alongside business outcomes.
• Lead technical design discussions, review code, mentor engineers and document operational procedures to raise engineering standards across the team.

Skills

Databricks

Python

PySpark

Spark SQL

Delta Lake

MLflow

MLOps

LLMOps

DataOps

Git

CI/CD

Containers

Terraform

Kubernetes

AWS

Azure

Education

Bachelor's degree in CS or related field

Tools

Terraform

Docker

Kubernetes

Unity Catalog governance

Job description

As Senior Machine Learning Engineer, you will turn models and AI prototypes into reliable, secure and measurable production services in Research & Development. You will design, deploy and operate machine learning, generative AI and agent-based applications on Databricks, AWS and Azure, and set the engineering practices that keep them performing. Your work helps colleagues move from promising ideas to dependable tools they use every day, supporting progress for people living with serious chronic diseases.

What you'll bring

• A degree in computer science, engineering, mathematics or a related field, or equivalent practical experience.
• Typically, 5+ years in machine learning engineering, software engineering or applied AI, including ownership of production ML systems, with examples of your contribution, architecture decisions, deployment approach, operational challenges and measured outcomes.
• Strong hands-on Databricks expertise, including Python, PySpark, Spark SQL, Delta Lake, MLflow, production pipelines, model serving, workflow orchestration and Unity Catalog governance.
• Strong practical knowledge of both AWS and Azure for deploying and operating ML or AI workloads, along with Git, CI/CD, containers and infrastructure as code, such as Terraform.
• Demonstrated implementation experience across MLOps, LLMOps, AgentOps and DataOps, from reproducible training, model registries and rollback to retrieval-augmented generation, safety testing, agent tracing, tool permissions, data contracts and lineage.
• Strong Python and SQL skills, solid machine learning fundamentals, experience with frameworks such as scikit-learn, PyTorch or TensorFlow, and practical experience building LLM applications with embeddings, retrieval, structured outputs and tool calling.
• Experience with observability, incident response and production support, an understanding of data privacy, security, responsible AI and model governance, and the communication skills to explain technical trade-offs and mentor colleagues.

Desirable, but not essential

• Experience with Azure Machine Learning, Azure AI services, Amazon SageMaker or Amazon Bedrock; Kubernetes, GPU workloads and distributed training; feature stores, vector search, fine-tuning and agent orchestration frameworks; evaluation frameworks for probabilistic systems; or operating AI in regulated or security-sensitive environments. Relevant Databricks, AWS or Azure certifications are a plus.

What you'll do

• Design, build, deploy and maintain ML models, LLM applications and AI agents, including batch and real-time inference services integrated into business applications.
• Build training, evaluation, deployment and monitoring workflows with Databricks and MLflow, and operate AI infrastructure on AWS and Azure that balances performance, reliability, security and cost.
• Establish MLOps, LLMOps, AgentOps and DataOps practices - covering versioning, automated testing, rollback, safety controls, human oversight and reliable, well-governed data.
• Monitor model quality, data drift, latency, availability and cost, investigate failures and define evaluation criteria that measure technical quality alongside business outcomes.
• Lead technical design discussions, review code, mentor engineers and document operational procedures to raise engineering standards across the team.

Who you'll work with

You will join the Data and Machine Learning Operations team in Research & Development, a team of machine learning, data, cloud and software engineers. In R&D, colleagues explore scientific ideas and advance them towards clinical testing, bringing curiosity, data, technology and operational excellence together.

You will work closely with data scientists, data engineers, software engineers and business stakeholders, turning their models and prototypes into services people can rely on. By leading design discussions and making technical trade-offs clear, you will help the team decide, own it and move at pace.

What you can expect here

You will work at the forefront of AI and technology, as Novo upskills its entire workforce to advance AI as a main driver for innovation for people living with serious chronic diseases. You will have the trust to own production systems end to end, shape engineering practices and make your voice heard in technical decisions.

You will grow alongside skilled colleagues on the job and through relevant learning offerings, in a culture with clear performance expectations where honest feedback and strong results are recognised.

Salary

For this role, the Annual Base Salary ranges from 651,000 to 956,900 DKK, corresponding to the level of the position.

Incentives and Benefits

The salary package may include short-term and/or long-term incentives as well as other employee benefits based on position level, location, functional area and relevant market benchmarks.

Apply by 22 October 2026.

Applications are reviewed on an ongoing basis, so we encourage you as soon as possible.

We are an equal opportunities employer and are committed to an inclusive recruitment process and equality of opportunity for all applicants.