Machine Learning Engineer

This is a fully remote position, open to applicants in Finland, +2 more states.

📋 Description

• Design, construct, and uphold scalable MLOps infrastructure for machine learning and Generative AI applications.

• Create automated training, validation, testing, deployment, and CI/CD pipelines for machine learning models.

• Execute experiment tracking, model versioning, model registries, and artifact management in alignment with MLOps best practices.

• Develop and sustain workflow orchestration, feature engineering, and data processing pipelines.

• Oversee production ML systems, focusing on model performance, data quality, drift detection, latency, and overall system health.

• Administer the complete model lifecycle, encompassing retraining, rollback, reproducibility, governance, and auditability.

• Containerize ML workloads with Docker and deploy scalable services utilizing cloud-native technologies and orchestration platforms.

• Create and manage Infrastructure as Code (IaC) for AI platforms and cloud resources.

• Collaborate with Data Scientists and software engineers to transition, enhance, and scale machine learning solutions.

• Assess and adopt new MLOps tools, frameworks, and best practices, including support for LLM and agentic AI applications.

⛳️ Requirements

• Bachelor's or Master's degree in Computer Science, Machine Learning, Software Engineering, or a related discipline.

• Proficient in Python programming and skilled in SQL.

• Experience with MLflow for experiment tracking, model registry, versioning, and model lifecycle management.

• Familiarity with contemporary ML platforms such as Snowflake, dbt, Snowpark ML, Vertex AI, or Amazon SageMaker.

• In-depth understanding of the complete machine learning lifecycle, including experimentation, deployment, monitoring, retraining, and governance.

• Proficient with Git, software engineering best practices, and Infrastructure as Code (e.g., Terraform or CloudFormation).

• Experience with Docker, containerized ML workloads, and container orchestration systems such as Kubernetes.

• Practical experience with cloud platforms like AWS, Azure, or Google Cloud Platform, including production monitoring and observability.

• Knowledge of feature stores, model registries, artifact repositories, and current MLOps practices.

• Experience deploying LLM or Generative AI applications is highly desirable, along with strong problem-solving, communication, and collaboration skills.

🏝️ Benefits

• Fair pay and employee stock option: We value the input of every employee and want you to tap into the growth we build together. That’s why our salaries are competitive and reassessed regularly, and you have access to an employee stock option program.

• Flexible Paid Time Off: We offer a flexible paid time off policy, providing up to 5 weeks of annual vacation days and paid family leave (subject to country regulations). Additionally, you can benefit from hybrid or remote work options, promoting a healthy work-life balance.

• Regular Fun With Your Team: To spend other than work-related time with your teammates, you get a team activity budget for three quarters a year. The fourth quarter is reserved for a company-wide event.