---
title: Senior Machine Learning Engineer at Elinext
description: Senior Machine Learning Engineer
---

# Senior Machine Learning Engineer

**Company:** Elinext  
**Location:** Poland, Georgia, Uzbekistan, Kazakhstan, Armenia, EU countries, and other locations  
**Posted:** 2026-10-29  
**Apply by:** 2026-12-13

**Tags:** python, data, product manager

[Apply / View original posting](https://t.me/Pol_relocation/59138)

## Job description

Senior Machine Learning Engineer

**Locations:** Poland, Georgia, Uzbekistan, Kazakhstan, Armenia, EU countries, and other locations.
**English Level:** B2 or higher

Elinext is an international software development company with 25+ years of experience delivering custom software solutions and dedicated engineering teams for clients worldwide.

About the Role
We are looking for a **Senior Machine Learning Engineer** to develop, deploy, and maintain machine learning and AI solutions for advanced financial data processing and reconciliation platforms.
You will work with large and complex financial transaction datasets to build solutions for transaction matching, reconciliation, and exception handling.

The role covers a broad spectrum of applied AI, from classical machine learning (supervised, unsupervised, and deep learning) to agentic AI solutions leveraging large language models (LLMs), tool use, and multi-step reasoning.

This is a hands-on engineering position focused on transforming ML prototypes into reliable, scalable, and production-ready systems. You will take end-to-end ownership of ML services and collaborate closely with data scientists, software engineers, product managers, and domain experts.

💻Responsibilities
— Develop, deploy, maintain, and optimize machine learning models and services in production.
— Transform research prototypes into production-grade ML systems with robust code, automated testing, and observability.
— Own model serving, deployment, scaling, monitoring, drift detection, and retraining.
— Perform feature engineering, model evaluation, calibration, and validation using real-world financial datasets.
— Build and maintain reliable data pipelines for processing large datasets.
— Collaborate with software engineers and data scientists to enhance financial data processing and transaction matching platforms.
— Ensure ML services remain accurate, scalable, reliable, and maintainable.
— Document technical approaches, methodologies, and decisions to ensure transparency and reproducibility.

🔎Requirements
— 4–6+ years of experience in Machine Learning Engineering or Software Engineering with a strong ML focus.
— Proven experience deploying, maintaining, and operating ML models in production environments.
— Strong Python development skills, including clean, typed, well-tested code, version control, and CI/CD practices.
— Strong proficiency with NumPy, Pandas, scikit-learn, and PyTorch.
— Solid understanding of machine learning algorithms, statistics, model evaluation, and validation techniques.
— Hands-on experience with model serving, monitoring, drift detection, and retraining.
— Experience developing production services and REST APIs using FastAPI, Flask, or similar frameworks.
— Experience with containerization and orchestration technologies such as Docker and Kubernetes or equivalent platforms.
— Strong experience with feature engineering for structured and tabular datasets.
— Ability to process large datasets and develop reliable data pipelines.
— Experience collaborating with cross-functional engineering and product teams.
— Strong analytical thinking, problem-solving skills, and a proactive, ownership-driven mindset.
— Excellent communication skills with both technical and business stakeholders.
— English proficiency at B2 level or higher.
— Degree in Computer Science, Engineering, Statistics, Mathematics, or a related field, or equivalent practical experience.

💚What We Offer
— Friendly and collaborative international team.
— 25 calendar days of paid vacation and 5 paid sick days.
— Medical insurance.
— Corporate English language courses.
— Corporate events and team-building activities.
— Professional development and certification support.
— Reimbursement for professional courses and training.
— Long-term international projects.
— Opportunities for career growth and professional development.

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