Machine Learning Engineer (NLP/LLMs)

TL;DR

Machine Learning Engineer (NLP/LLMs): Design and develop ML modules for products including recommendation systems, AI assistants, customer support chatbots, and spam filters with an accent on end-to-end solutions from prototyping to production integration. Focus on ML solution architecture, data preparation and processing, model training, A/B testing, monitoring, and MLOps infrastructure.

Location: Hybrid work format with well-equipped offices

Company

World’s largest financial analysis platform with more than 100M users across 180+ countries, trusted by companies like Revolut, Binance, and CME Group.

What you will do

• Design and develop ML modules for various products from experimentation and prototyping to production integration
• Participate in ML solution architecture, define technical approaches, and establish engineering standards
• Prepare and process data, train models, conduct A/B tests, and analyze results
• Monitor and improve model quality, interpret model behavior and key metrics
• Collaborate with product managers, engineers, and analysts to clarify requirements, integrate solutions, and evaluate effectiveness
• Work with MLOps infrastructure including CI/CD, monitoring, logging, and containerization

Requirements

• 3+ years of experience in ML engineering and building production-grade machine learning systems
• Strong expertise in NLP, LLMs, AI assistants, spam detection, behavioral analysis, and related areas
• Proven experience delivering end-to-end ML solutions from idea and data preparation to production deployment and ongoing support
• Excellent Python skills and experience in industrial software development
• Strong understanding of A/B testing principles and result analysis
• Experience making architectural decisions and driving engineering best practices
• Hands-on experience with Docker, Kubernetes, CI/CD systems, monitoring tools (e.g. Prometheus, Grafana), and logging solutions

Nice to have

• Product mindset and understanding of business metrics
• Experience building real-time and high-load ML systems
• Familiarity with modern MLOps tools such as MLflow, Airflow, and Kubeflow

Culture & Benefits

• Flexible working hours and hybrid work format
• Global, distributed team of 500+ professionals
• Learning, mentorship, and long-term career growth
• Relocation support and private health insurance
• Performance-based bonuses
• hirify.global Premium access, regular team events, and company-wide meetups