Limassol, Tbilisi, Paphos
We develop and implement machine learning and AI solutions across a range of products: AI assistants, customer support chatbots, intelligent data processing, generative AI features, recommender systems, spam detection, search and other ML-powered modules.
Our goal is to build reliable ML and AI systems that solve real product problems — from experimentation and prototyping to production integration and continuous improvement.
Responsibilities
- Designing and implementing ML and AI modules for various products, from experimentation and prototyping to production integration
- Building solutions based on LLMs and other ML/NLP approaches for data processing, generation, search, assistants, bots, agents and automation
- Choosing and evaluating appropriate approaches — including external AI providers, open-source or self-hosted models, as well as traditional ML/NLP methods
- Contributing to the architecture of ML solutions, shaping technical approaches and engineering standards
- Preparing and processing data, training models, running A/B tests, and analyzing results
- Monitoring and improving model performance, interpreting model behavior and key metrics
- Evaluating AI-system quality, analyzing failure cases and improving models, prompts, data and system architecture
- Collaborating with product managers, engineers, and analysts to clarify requirements, integrate solutions, and evaluate their impact
- Working with MLOps infrastructure: CI/CD, monitoring, logging, containerization
What makes you the perfect fit
- 3+ years of experience in ML engineering and building production-ready ML systems
- Strong practical expertise in NLP, LLMs, AI assistants and related AI/ML areas
- Experience with both modern LLM-based systems and traditional ML/NLP approaches such as classification, ranking, retrieval, semantic similarity and information extraction
- Strong track record of delivering end-to-end ML solutions — from idea and data to production and support
- Proficient Python/Go skills and experience with production-grade development
- Experience evaluating ML/AI solutions and understanding their quality, reliability, latency and cost trade-offs
- Solid knowledge of A/B testing and result interpretation
- Experience in architectural decision-making and a drive to improve engineering practices
- Familiarity with tools like Docker, Kubernetes, CI/CD systems, monitoring (e.g. Prometheus, Grafana), and logging
Will be a plus
- Product-oriented mindset and understanding of business metrics
- Experience building real-time and high-load ML systems
- Experience with open-source or self-hosted models
- Familiarity with modern MLOps tools (e.g. MLflow, Airflow, Kubeflow)
- Experience evaluating the UX of AI-driven interactions
What we offer you
- Flexible working hours and a hybrid work format
- Well-equipped offices for focused and collaborative work
- A global, distributed team of 500+ professionals
- Learning, mentorship, and long-term career growth
- Relocation support and private health insurance
- Performance-based bonuses
- TradingView Premium access
- Regular team events and company-wide meetups
