Nino – Python, Ruby, Ruby on Rails

A global contractor management platform that streamlined operations for distributed engineering teams. It supported end-to-end workflows including onboarding, HR processes, scheduling, internal training, and performance tracking—helping organizations efficiently scale and manage remote tech talent.

Nino's achievements within this project included the following:

• developed and maintained scalable backend APIs using Flask for client's internal platforms;
• designed platform-specific features to support operational workflows and internal toolsets;
• integrated backend systems with CI/CD and automation pipelines to streamline deployments;
• improved reliability and maintainability of internal services through modular API design;
• helped reduce manual overhead by automating key processes within internal platforms.

The client was an industrial refrigeration company focused on reducing spoilage and improving operational efficiency through predictive technology. Their AI-powered platform leveraged LSTM models to forecast temperature fluctuations in industrial freezers based on real-time sensor data. By translating noisy environmental inputs into actionable insights, the system enabled more reliable cold storage management and informed decision-making in critical supply chain operations.

Nino's main responsibilities included the following:

• developed the service using Python/Django, React and PostgreSQL as main stack;
• designed and deployed machine learning model for time-series prediction;
• built backend pipeline to process and clean real-world sensor data;
• implemented robust data validation and monitoring tools;
• integrated model outputs into production API for real-time use;
• optimized model performance to run efficiently on limited infrastructure;
• collaborated closely with stakeholders to align predictions with operational needs.

The client was a customer engagement platform specializing in in-app chat, voice, and video solutions for businesses. Their AI-powered customer service suite enabled real-time, omnichannel communication to enhance user support experiences. The project focused on modernizing the legacy architecture into a microservices-based system, boosting reliability, scalability, and deployment efficiency across messaging channels.

Nino's duties included, but were not limited to:

• migrated AI customer service system from legacy architecture to modern stack;
• decomposed monolithic codebase into scalable microservices;
• designed APIs for seamless communication between new services;
• improved system reliability and deployment speed;
• ensured zero-downtime migration through phased rollout strategy;
• collaborated with cross-functional teams to align architecture with product needs.

The client was a community management platform that empowers organizations to create and manage branded online communities. Their white-label solution supports alumni networks, professional associations, and corporate groups. The work focused on developing the community SDK and enhancing the mobile API, ensuring seamless integration and personalized mobile experiences for clients across education, nonprofit, and enterprise sectors.

Among others, Nino's contributions included the following:

• developed and maintained a robust community SDK to streamline mobile app integration;
• developed and deployed a mobile API from scratch to support dynamic content delivery and user interactions;
• collaborated with cross-functional teams to ensure consistent branding and functionality across platforms;
• implemented performance optimizations, resulting in improved load times and user engagement;
• contributed to the scalability and flexibility of Hivebrite’s mobile solutions, aligning with the company’s commitment to customizable community engagement tools.

The client was a remote-first technology consultancy that builds custom software solutions for fast-moving companies. Their internal platform supports end-to-end project and team management for a fully distributed engineering workforce. The work focused on developing and maintaining internal tools for onboarding, time tracking, and collaboration, combining backend architecture design with front-end development using React.

Scope of resposibilities involved the following:

• architected and developed internal tools using Ruby on Rails, React and PostgreSQL as the main stack;
• implemented complex business logic for project workflows, onboarding, and permissions;
• contributed to both backend APIs and frontend UI components for a seamless developer experience;
• optimized performance and reliability for tools used daily by a global remote team;
• collaborated closely with design and product teams to translate requirements into intuitive features;
• led technical discussions and reviewed PRs to maintain high code quality across the platform.

The client was a fintech company focused on empowering retail traders with AI-driven trading tools. They developed a machine learning–based Support & Resistance algorithm designed to identify key market levels in FOREX trading. Built with a Python stack, the solution translated volatile price data into statistically grounded decision points, offering retail investors greater confidence and accuracy in their trading strategies.

Nino's contributions included the following:

• architected and developed with Python/Django, React and PostgreSQL as main stack;
• developed proprietary ML algorithm for Support & Resistance detection in financial markets;
• preprocessed and modeled large volumes of historical price data with custom feature engineering;
• evaluated and benchmarked model accuracy against human trader-defined levels;
• implemented a Python-based backend pipeline for data ingestion, model training, and real-time forecasts;
• deployed model outputs in a web-facing application used by retail traders;
• improved trader confidence and accuracy by automating a highly subjective task with measurable consistency.