Machine Learning Engineer

Machine Learning Engineer - Python / Production ML

Elinext is an international software development company with 25+ years of experience, helping clients worldwide build and scale reliable digital products. We work across Fintech, Healthcare, Manufacturing, AI/ML, Cloud, Enterprise Software, and other domains.

We are currently looking for an experienced Machine Learning Engineer to join an international project in the financial domain.

📍 Location: Poland, Georgia, Uzbekistan, Kazakhstan, Armenia and other eligible locations

About the role:

The main focus is on taking machine learning models beyond the research stage and turning them into reliable production-grade systems.

You will work with real financial datasets, deploy and maintain ML services, monitor model performance, improve data pipelines, and collaborate closely with software engineers and data scientists.

Responsibilities:

— Develop, deploy, and maintain machine learning models and services;
— Keep existing ML solutions performant, stable, and robust;
— Turn research prototypes into production-grade ML systems;
— Harden code, add tests, logging, monitoring, and observability;
— Own model serving, deployment, scaling, and lifecycle management;
— Monitor model performance and detect drift;
— Build and maintain retraining processes;
— Engineer and evaluate features on real financial datasets;
— Calibrate and validate models to ensure reliable behavior;
— Build and improve data pipelines;
— Collaborate with software engineers and data scientists;
— Document approaches, experiments, and architecture decisions.

MUST HAVE:

— Strong software engineering skills in Python;
— Clean, typed, maintainable, and well-tested code;
— Experience with version control and CI/CD;
— Strong knowledge of NumPy, Pandas, scikit-learn, PyTorch;
— Solid understanding of machine learning, statistics, and model evaluation;
— Proven experience taking ML models into production;
— Experience with model serving, monitoring, drift detection, and retraining;
— Experience building production APIs and services with FastAPI, Flask, or similar;
— Experience with Docker / containerization;
— Experience deploying services on Kubernetes or similar environments;
— Strong feature engineering experience with structured / tabular data;
— Experience working with large datasets and reliable data pipelines;
— Strong communication skills with technical and business stakeholders.

Nice to Have:

— Financial / fintech project experience;
— Experience with production MLOps practices;
— Experience with highly scalable ML platforms;
— Experience with model explainability and reproducibility;
— Experience with distributed data processing or cloud environments.

What We Offer:

— Small-company feel within a fast-growing international environment;
— Friendly, collaborative, and mission-driven team;
— Vacation (depends on the location) + 5 additional paid sick days;
— Medical insurance;
— Corporate English courses;
— Corporate events and team-building activities;
— Support with professional certifications;
— Reimbursement for professional courses and training;
— Long-term international projects;
— Opportunities for professional and technical growth.