Senior Machine Learning Engineer architecting next - generation AI platforms for healthcare and fintech with Nitra's diverse team. Focused on data pipelines, ML infrastructure, and production - ready AI systems.
About the role
- MLOps Engineer maintaining and optimizing machine learning models for healthcare applications. Collaborating with engineers to deliver reliable and effective AI solutions.
Responsibilities
- Own and manage the full lifecycle of both ML models and core infrastructure – from development and deployment to monitoring and continuous improvement
- Build and maintain robust CI/CD pipelines for both software and ML workflows
- Ensure reliability, scalability, observability, and security of production systems and ML infrastructure
- Automate deployment, orchestration, and environment management using modern DevOps tooling
- Collaborate closely with software engineers, ML engineers, and product teams to bring ML-powered features to production
- Proactively detect, troubleshoot, and resolve infrastructure and model performance issues
- Stay up to date with industry best practices in DevOps, MLOps, and infrastructure engineering
- Document infrastructure, workflows, and operational procedures clearly and thoroughly
Requirements
- Experience deploying machine learning models into production and managing their lifecycle
- Experience implementing model governance, including versioning, monitoring, drift detection, and reporting
- Familiarity with MLOps tools such as MLflow, Kubeflow, or DVC
- Solid understanding of CI/CD systems (e.g., GitHub Actions, ArgoCD) and infrastructure-as-code tools (e.g., Terraform, Helm)
- Familiarity with data engineering concepts such as ETL pipelines, data lakes, and large-scale batch/stream processing
- Experience mentoring or supporting colleagues to help them grow their technical skills
- Proven experience in a senior-level DevOps, MLOps, or related infrastructure-focused engineering role
- Strong proficiency in Python
- Deep experience with cloud platforms (AWS, GCP, or Azure) and container orchestration tools (Docker, Kubernetes)
- Ability to design scalable, secure, and observable systems in fast-moving environments
- Strong debugging and problem-solving skills across distributed systems
- Excellent collaboration and communication skills, with experience working in cross-functional teams
- Understanding of security and compliance best practices for both software and ML systems
Benefits
- Hybrid working environment in Copenhagen, London and New York
- Equipment provided by Corti
Job title
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