Software company was built from the ground up to specialize in new product development and R&D, tackling the most difficult problems in the tech sphere. Now we've expanded to offer early-stage innovation and ideation plus digital transformation business consulting. Our superpower is to deliver all of this under one roof on a global scale. So let's get started and build a better future together.

About the job:

• Automatic launch of games and applications in benchmark mode.
• Real-time telemetry collection: FPS/frametimes, GPU usage, GPU temperature, power, clocks, CPU usage, RAM, and disk activity.
• Low-level event capture (ETW/PresentMon/DXGI) for latency and present-time analysis.
• Orchestration of profilers (OCAT, PresentMon, NVAPI/ADL hooks, Intel GPA/RenderDoc as needed).
• Reliable logging, results storage, test comparison, and report generation.
• Web dashboard with charts and test history.

Responsibilities

• Own the system architecture: from choosing frameworks and database structures to designing scalable APIs and services
• Take a hands-on role in development: build core components, POCs, and critical modules
• Define and document architectural decisions, patterns, and best practices
• Lead technical discussions and decisions within the team and with stakeholders
• Help the team stay unblocked, productive, and aligned with technical direction
• Review code, guide implementation, and ensure high technical quality across the project
• Collaborate with DevOps and QA to ensure stability, observability, and smooth deployments

Requirements

5+ years of experience in backend development with Python

• Proven experience as a Solution Architect, Tech Lead, or Senior Backend Engineer
• Deep knowledge of Python frameworks (FastAPI, Django, Flask)
• Strong understanding of:

- microservices architecture

- API design and service boundaries

- data modelling and relational databases (PostgreSQL)

- async programming and queues (e.g., Celery, RabbitMQ)

• Experience designing systems for scalability, fault tolerance, and maintainability
• Comfort working with Linux environments, containers (Docker), Git workflows
• Excellent communication skills and ability to justify architectural decisions
• Very good English language skills, both written and verbal (min. B2)

Preferred Skills

• Experience with Azure DevOps, GitHub Actions, or other CI/CD platforms
• Familiarity with cloud platforms (Azure, AWS, GCP)
• Experience with system monitoring, logging, and performance profiling
• Bonus: exposure to AI/ML integration, vector databases (e.g., Qdrant, Pinecone)

Locations: Vilnius, Lithuania (hybrid/remote) or Mexico - Guadalajara (hybrid/remote), EU country (fully remote)

Format: Long-term engagement

We are looking for a Data Science specialist with experience in banking projects to build risk models (scoring for lending).

Key Areas of Responsibility

Risk Modeling Department:

- Full-cycle development of ensemble models, including data preparation and preprocessing, labeling, and splitting into training and testing datasets.  

- Selection and tuning of base models with an emphasis on diversity to improve prediction quality.  

- Development of machine learning models to forecast daily balances on corporate client accounts, incorporating time series analysis (weekly, monthly, quarterly) and additional factors (weekdays, holidays, tax periods, business cycles).  

- Training personalized models.  

- Application of model aggregation techniques (bagging, boosting, stacking) with optimized ensemble weighting.  

- Performance evaluation using accuracy, recall, and F1-score metrics to enhance prediction quality.  

- Deployment of models into production environments, ongoing monitoring, and regular parameter optimization.

Computer Vision Projects:

- Development and implementation of a biometric identity verification system, including document recognition and photo comparison modules.  

- Requirements analysis and system architecture design with a focus on high security and recognition accuracy standards.  

- Implementation of image processing algorithms to extract data from passports and compare with client selfie photos.

Acquisition Analytics:

- Comprehensive analysis of acquiring and cash management portfolio data, including collection and preprocessing of historical client behavior data.  

- Feature engineering reflecting transactional activity, financial indicators, and service usage patterns to identify key churn factors.  

- Building and training an ensemble prediction model optimized for the specifics of both products.  

- Implementation of client scoring system based on churn probability considering financial behavior and length of partnership.

Technologies and Tools: Python, SQL, Scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow/Keras, PyTorch, Random Forest, Gradient Boosting, Stacking, Pandas, NumPy, Matplotlib, Seaborn.