Ready to leave your footprint on the future of fintech and AI? We are seeking an experienced MLOps Engineer to join our platform engineering team.
At Lunar we are democratizing the power of money and changing the way we all bank, pay, and invest. Since starting in Aarhus in 2015, we’ve grown rapidly and are now a major player in the Nordics, with offices in Copenhagen, Aarhus and Stockholm
So who are we? Are we a tech company or a bank? Well, we’re both, breaking free from the usual categories. Here, tech isn’t just a cool add-on; it’s the core of how we do things. With our own banking license, we go head-to-head with traditional banks. What sets us apart is the mix of tech and financial services, giving us the power to shake up a dusty industry that’s ready for a change.
You will play a key part in building and maintaining the infrastructure and processes that enable the successful development, deployment, and monitoring of both traditional machine learning (ML) models and generative AI (GenAI) models across our organization. We embrace a platform engineering approach, where our primary goal is to empower data scientists and software engineers by providing them with the tools, infrastructure, and self-service capabilities they need to effectively build, deploy, and manage their own AI/ML solutions. We’re building something truly special here at Lunar, and we want you to be a part of it!
You will be located in either Copenhagen or Aarhus, and report to Rolf Njor Jensen, Director of Technology.

Technical Skills:

• Software Engineering: Proficiency in Golang, Python, and software development best practices (e.g., CICD, automated testing, version control).
• Cloud Native Architectures: Hands-on experience with event sourcing, CQRS, microservices, data mesh, and lakehouses.
• Cloud Computing: Experience with at least one major cloud provider (AWS, Azure, GCP), including associated services (e.g., security, compute, storage, networking).
• Infrastructure as Code: Experience with Terraform and immutable infrastructure principles (cattle, not pets).
• Containerization & Orchestration: Familiarity with Docker and Kubernetes.
• ML Frameworks: Experience with popular machine learning frameworks like TensorFlow, PyTorch, or scikit-learn.
• GenAI Frameworks: Familiarity with tools for developing and deploying GenAI models, such as Hugging Face Transformers or TensorFlow Hub.
• MLOps Tools: Experience with tools like Kubeflow, MLflow, or similar platforms

Additional Experience:

• Data Pipelines: Experience in data processing and orchestration with tools like Apache Airflow or Prefect.
• CI/CD Pipelines: Familiarity with CI/CD tools such as Jenkins or GitLab CI/CD

MLOps Pipeline Development:

• Design, build, and maintain robust CI/CD pipelines for training, testing, and deploying a variety of ML models Regression, classification, clustering, etc.
• Help design and implement platform components for applications to effectively and efficiently use LLMs and access context data as relevant for the applications in question.

Infrastructure & Platform Management:

• Manage and optimize infrastructure (AWS, Kubernetes) for ML and GenAI model development and deployment.
• Build self-service platforms for data scientists and engineers to access necessary tools and resources.

Model Monitoring & Maintenance:

• Develop monitoring systems to track performance, identify anomalies, and ensure model quality.
• Implement retraining and updating mechanisms for models, including fine-tuning and prompt engineering for GenAI models.

Data Management & Version Control:

• Establish and maintain data pipelines to support efficient data flow and version control for datasets, code, and models.
• Focus on supporting GenAI-specific data requirements (e.g., large-scale text and image datasets).

Collaboration & Communication:

• Collaborate with data scientists, engineers, and other stakeholders to align MLOps solutions with project needs.
• Translate technical needs into effective solutions that align with the platform engineering approach.

Research & Development:

• Stay updated on advancements in ML, GenAI technologies, and MLOps best practices.
• Explore and evaluate new tools and technologies to improve AI/ML operations.
• Contribute to developing best practices and standards for MLOps, addressing the unique challenges of GenAI models.

We are excited to welcome applicants for this impactful role! The ideal candidate will have strong experience in the following areas:

Technical Skills:

• Software Engineering: Proficiency in Golang, Python, and software development best practices (e.g., CICD, automated testing, version control).
• Cloud Native Architectures: Hands-on experience with event sourcing, CQRS, microservices, data mesh, and lakehouses.
• Cloud Computing: Experience with at least one major cloud provider (AWS, Azure, GCP), including associated services (e.g., security, compute, storage, networking).
• Infrastructure as Code: Experience with Terraform and immutable infrastructure principles (cattle, not pets).
• Containerization & Orchestration: Familiarity with Docker and Kubernetes.
• ML Frameworks: Experience with popular machine learning frameworks like TensorFlow, PyTorch, or scikit-learn.
• GenAI Frameworks: Familiarity with tools for developing and deploying GenAI models, such as Hugging Face Transformers or TensorFlow Hub.
• MLOps Tools: Experience with tools like Kubeflow, MLflow, or similar platforms.

Additional Experience:

• Data Pipelines: Experience in data processing and orchestration with tools like Apache Airflow or Prefect.
• CI/CD Pipelines: Familiarity with CI/CD tools such as Jenkins or GitLab CI/CD.