ML Software Engineer

Kyriba; Courbevoie; Sur place; EUR 45 000 - 65 000; Plein temps

Résumé du poste

A leading company specializing in liquidity performance is looking for a Software Engineer to integrate machine learning models into production systems. The ideal candidate will have a strong software engineering background, experience in ML libraries, and the ability to optimize model performance in real-time environments. This position provides an opportunity to work with cutting-edge technology and make a significant impact on the company's products and customer satisfaction.

Qualifications

• 3+ years of experience in software development.
• Proficiency in ML libraries and data preprocessing.
• Strong understanding of RESTful APIs and microservices.

Responsabilités

• Integrate pre-trained ML models into production systems.
• Develop data preprocessing and transformation pipelines.
• Build monitoring systems for model performance.

Connaissances

ML libraries (scikit-learn, XGBoost, LightGBM)

RESTful APIs

data preprocessing

Feature engineering

problem-solving abilities

team collaboration

Formation

Bachelor's degree in Computer Science, Software Engineering, or related field

Outils

Python

Java

Docker

Flask

FastAPI

SQL

Git

Jenkins

Prometheus

Description du poste

It's fun to work in a company where people truly BELIEVE in what they're doing!

About Us

Kyriba is a global leader in liquidity performance that empowers CFOs, Treasurers and IT leaders to connect, protect, forecast and optimize their liquidity. As a secure and scalable SaaS solution, Kyriba brings intelligence and financial automation that enables companies and banks of all sizes to improve their financial performance and increase operational efficiency. Kyriba’s real-time data and AI-empowered tools empower its 3,000 customers worldwide to quantify exposures, project cash and liquidity, and take action to protect balance sheets, income statements and cash flows. Kyriba manages more than 3.5 billion bank transactions and $15 trillion in payments annually and gives customers complete visibility and actionability, so they can optimize and fully harness liquidity across the enterprise and outperform their business strategy. For more information, visit.

Position Overview

We are seeking a Software Engineer to integrate and implement standard machine learning models (such as classification, regression, clustering) into our production systems. This role focuses on incorporating pre-trained ML models into our existing applications and data flows, ensuring reliable and efficient implementation.

Key Responsibilities

• Integrate pre-trained ML models (scikit-learn, XGBoost, etc.) into production systems
• Implement model serving solutions for classification and regression tasks
• Develop data preprocessing and transformation pipelines
• Ensure efficient model inference in production environments
• Build monitoring systems for model performance and data drift
• Optimize model serving for latency and throughput
• Create documentation for model integration and maintenance

Required Qualifications

• Bachelor's degree in Computer Science, Software Engineering, or related field
• 3+ years of experience in software development
• Experience with ML libraries (scikit-learn, XGBoost, LightGBM)
• Proficiency in data preprocessing and feature engineering
• Strong understanding of RESTful APIs and microservices
• Experience with version control (Git) and CI / CD pipelines
• Knowledge of SQL and database systems

Technical Skills

• Programming Languages : Python, Java
• Model Serving : Flask, FastAPI, or similar
• Databases : SQL, NoSQL
• Version Control : Git
• Containers : Docker
• CI / CD Tools : Jenkins, GitLab CI, or similar
• Monitoring Tools : Prometheus, Grafana, or similar

Preferred Qualifications

• Experience with model versioning and deployment tools
• Knowledge of feature stores and model registry concepts
• Understanding of statistical analysis and data validation
• Experience with distributed computing
• Familiarity with A / B testing and experiment tracking
• Experience with cloud platforms (AWS, Azure, or GCP)
• Strong software engineering practices
• Understanding of ML model lifecycle
• Data structure and algorithm expertise
• Performance optimization skills
• System design and architecture
• Production monitoring and troubleshooting

Soft Skills

• Strong problem-solving abilities
• Team collaboration
• Technical documentation skills
• Project management capabilities