ml engineer for financial visualization

TradingView is a financial analysis and charting platform used by more than 100M users across 180+ countries. It provides tools for trading and investment decisions, including advanced charting, market data, collaboration, publishing, and financial visualization features.

• Design and implement ML and AI modules from experimentation and prototyping through production integration;
• Build LLM- and ML/NLP-based solutions for data processing, generation, search, assistants, bots, agents, and automation;
• Choose and evaluate external AI providers, open-source or self-hosted models, and traditional ML/NLP methods;
• Contribute to ML solution architecture and shape technical approaches and engineering standards;
• Prepare and process data, train models, run A/B tests, and analyze results;
• Monitor and improve model performance, interpret model behavior, and analyze key metrics;
• Evaluate AI-system quality, analyze failure cases, and improve models, prompts, data, and system architecture;
• Collaborate with product managers, engineers, and analysts to clarify requirements, integrate solutions, and evaluate impact;
• Work with MLOps infrastructure, including CI/CD, monitoring, logging, and containerization.

• 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 LLM-based systems and traditional ML/NLP approaches, including classification, ranking, retrieval, semantic similarity, and information extraction;
• Track record of delivering end-to-end ML solutions from idea and data through production and support;
• Proficient Python and Go skills with experience in production-grade development;
• Experience evaluating ML/AI solutions and understanding quality, reliability, latency, and cost trade-offs;
• Solid knowledge of A/B testing and result interpretation;
• Experience in architectural decision-making and improving engineering practices;
• Familiarity with Docker, Kubernetes, CI/CD systems, monitoring tools such as Prometheus and Grafana, and logging;
• Nice to have: 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 such as MLflow, Airflow, and Kubeflow, experience evaluating the UX of AI-driven interactions.

• Flexible working hours;
• Relocation support is available;
• Private health insurance;
• Performance-based bonuses;
• TradingView Premium access;
• Learning, mentorship, and long-term career growth;
• Regular team events and company-wide meetups.