ML Engineer

We are looking for a Middle ML Engineer to support and develop AI solutions, integrate LLMs, and optimize processes in a fully remote environment.

• •Support current algorithmic solutions.
• •Design and implement AI agents (LLM, GPT-like models) in various processes.
• •Develop methodology and design NLP/LLM solutions.
• •Adapt and fine-tune LLM (e.g., LLaMA, Mistral, YaLM 2) for product specifics.
• •Integrate LLM APIs (OpenAI, Claude) with fallback mechanisms.
• •Conduct research, test new ideas, assess viability and quality of models, implement R&D approaches on real business data.
• •Develop RAG pipeline for data search and processing (POI, reviews, descriptions).
• •Configure vector databases (Pinecone/Weaviate/Qdrant) and optimize caching.
• •Engage in prompt engineering — create and test prompts for various scenarios.
• •Maintain work documentation and create reports on results.
• •Monitor key performance indicators, analyze deviations and their impact on overall metrics.
• •Participate in architecture design, CI/CD, monitoring, and optimization of services.

• •Fully white salary. We focus on your wishes, experience, and skills.
• •Employment according to the Labor Code of the Russian Federation, paid vacation and sick leave from the first working day.
• •Work in a company accredited by the Ministry of Digital Development.
• •Extended DMS package including dentistry.
• •8-hour working day, remote work format.
• •Sports – corporate volleyball, running club, and esports tournaments.
• •Bright corporate life – over 400 events a year (2 corporate parties with transfer payment, English conversation club, joint museum visits, quizzes, book and movie clubs, meme chat, and much more).
• •Training and participation in professional conferences.

• •Higher education in mathematics, computer science, or related disciplines.
• •Knowledge of modern AI frameworks and libraries (LangChain, LlamaIndex, etc.).
• •Skills in creating ML pipelines and backend services.
• •Proficient in Python and PyTorch/TensorFlow, understanding of algorithm principles (GBDT, CNN, Transformers).
• •Practical experience with APIs, Docker/Kubernetes, and databases.
• •Understanding of Fine-tuning LLM processes and model quality assessment.
• •Experience in implementing RAG.
• •Ability to write unit tests.

• •Positive experience in implementing RAG agents in existing business processes.
• •Completed projects with significant user load.
• •Understanding of infrastructure for ML services (GPU, CPU).