Machine Learning Engineer
Description
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