MLOps Engineer

Fetcherr is an AI-driven company specializing in deep learning, algorithmic trading, and large-scale data solutions. Our core technology, the Large Market Model (LMM), enables accurate demand forecasting and real-time, data-driven decision-making. Originally focused on the airline industry, Fetcherr is expanding its AI solutions across additional industries.

We are looking for an MLOps Engineer to join Fetcherr’s engineering team and help build the foundations of large-scale, production-grade machine learning systems. This role is about owning complex ML infrastructure end-to-end — from data ingestion and feature engineering, through model training and deployment, to monitoring, reliability, and continuous improvement. You’ll work on systems that operate at scale, handle high-volume data, and directly impact real-time decision-making.

If you enjoy solving hard problems around scalability, reliability, automation, and ML in production, this role is for you.

• Responsibilities: Building and operating end-to-end ML pipelines that support training, validation, deployment, and monitoring
• Designing automated, reproducible ML workflows using CI/CD and infrastructure-as-code
• Creating and maintaining data pipelines that handle large, complex datasets for ML workloads
• Ensuring data and feature consistency between training and production environments
• Designing and managing feature engineering workflows, including feature stores and versioning
• Implementing model versioning, experiment tracking, and reproducibility standards
• Monitoring model performance, data drift, and system health, and enabling automated retraining or alerting
• Working closely with ML engineers to turn research into reliable, scalable production systems
• Optimizing infrastructure for performance, cost efficiency, and scalability
• Improving system reliability, observability, and developer productivity through automation and tooling
• Taking part in architectural decisions that shape how ML is built and operated at Fetcherr
  
  

Requirements:

You’ll be a great fit if you have...

• 5+ years of experience building production-grade systems, with a strong focus on Python
• 3+ years of experience in data engineering, working with large-scale data pipelines
• Experience working with ML systems in production and applying MLOps best practices
• Strong understanding of data structures and algorithms
• Experience with distributed computing systems, preferably Dask
• Experience with pipeline orchestration frameworks (Dagster, Prefect, Airflow – preferred)
• Experience with containerized environments such as Docker and Kubernetes
• BSc or MSc in Computer Science, Mathematics, or Engineering – preferred
• Fluent English, written and spoken