Machine Learning Engineer - Allegro Pay

As a Machine Learning Engineer, you will be responsible for the machine learning/data platform and support Data Scientists in building machine learning models, making business-critical decisions, and shipping models to production while ensuring high availability and performance.

Responsibilities

• Develop a modern MLOps ecosystem to automate machine learning model building and deployment.
• Design and implement a platform for training predictive models, including classifiers, deep neural networks, and graph models.
• Deploy models to production and optimize deployment pipelines to enable Data Scientist self-service.
• Manage, monitor, and recalibrate production models.
• Support development of the Feature Store, which provides predictors for production models.
• Find synergies and build connectors with the FinTech AI platform, enabling agentic workflows to interact with models and features.

Technologies

Python, Snowflake, Airflow, Azure, Kedro, MLflow, .NET, Kubernetes, Tableau.

What sets the role apart

• Work on ML applications in finance, with large-scale, sophisticated algorithms, business impact, and demanding technical requirements.
• Directly influence real-time predictive models used by millions of customers.
• Apply an engineering mindset to the development of analytical solutions.
• Access conference attendance, team training budgets, and study aids.

Requirements

• Degree in Computer Science, Mathematics, or another technical major.
• At least 2 years of experience building ML-driven solutions.
• Fluent Python programming and familiarity with the MLE toolchain: scikit-learn, PyTorch, Pandas, and FastAPI or Flask.
• Comfort using AI-assisted programming tools, including Opencode, Codex, and Claude.
• Experience with modern Python-based orchestration tools, such as Airflow or Dagster.
• Strong analytical skills and SQL knowledge.
• Application of DevOps principles.
• Practical understanding of statistical and machine-learning methods, especially decision-tree and neural-network algorithms.
• Ability to make independent decisions and take ownership of created code.

Nice to have

• Experience with the .NET ecosystem.
• Knowledge of cloud-based MLOps tools: AzureML, Google Vertex AI, or AWS SageMaker.
• Experience with SQL-based data transformation frameworks such as dbt.

Benefits

• Flexible hybrid working model (4/1), with start times between 7:00 a.m. and 10:00 a.m. and 30 days of occasional remote work.
• Annual performance and company-results bonus of up to 10% of gross annual salary.
• Well-equipped offices and work tools.
• Choice of a 16-inch or 14-inch MacBook Pro, or a comparable Dell Windows laptop, with necessary accessories.
• Cafeteria-plan benefits, including medical, sports, lunch, insurance, and purchase-voucher options.
• Job-related English classes.
• Training budget, inter-team tourism, hackathons, and an internal learning platform.
• Additional day off for volunteering.
• Social events.

Working environment

• Autonomy in team organization, technology choices, and ownership of delivered work.
• Developer tooling and self-service platform capabilities, including Kubernetes, Docker, Consul, GitHub, and GitHub Actions.
• Modern AI tools for automating repetitive tasks.
• Opportunity to work with a large-scale environment of microservices, a high-throughput data bus, service mesh, large data volumes, and production machine learning.