Вакансія Machine Learning Engineer

Skills

• Python
• LLM
• OpenAI
• Claude
• Gemini
• Llama
• LangChain
• Hugging Face
• PyTorch
• TensorFlow
• Pinecone
• Weaviate
• Qdrant
• SQL
• AWS
• GCP
• Azure
• Docker
• MLOps

Опис вакансії

We are seeking a self-motivated AI/ML Engineer to join our team. In this role, you will conduct R&D activities, develop concepts, engineer prompts, train models, and implement AI solutions. The ideal candidate is proactive, can work independently, and stays current with emerging AI technologies.

Responsibilities:

• Conduct R&D in the field of artificial intelligence and machine learning;
• Prepare and develop AI concepts and prototypes to solve business challenges;
• Design and engineer effective prompts for large language models;
• Train, fine-tune, and optimize machine learning models for specific use cases;
• Develop and implement RAG (Retrieval-Augmented Generation) solutions;
• Create and integrate AI-powered bots, virtual assistants, and chatbots;
• Work with various LLM APIs (OpenAI, Anthropic Claude, Google Gemini, LLaMA, etc.);
• Implement ML models for data analysis, NLP tasks, and content generation;
• Build data processing pipelines and workflows;
• Form and test hypotheses to improve product metrics and user engagement;
• Collaborate with product teams to implement AI features that solve real user problems.

Requirements:

• 2+ years of experience with Python programming;
• Experience working with LLM models and APIs (OpenAI, Claude, Gemini, LLaMA, etc.);
• Practical knowledge of AI libraries and frameworks (LangChain, Hugging Face, PyTorch, TensorFlow);
• Experience with vector databases (Pinecone, Weaviate, Qdrant, etc.);
• Understanding of prompt engineering techniques and best practices;
• Basic experience with model fine-tuning and training pipelines;
• SQL skills and experience with databases.

Nice to Have

• Experience with cloud platforms (AWS, GCP, Azure) for AI/ML deployment;
• Knowledge of Docker and containerization for model deployment;
• Experience with MLOps practices and model monitoring;
• Understanding of embeddings and semantic search techniques;
• Experience with unstructured data processing (text, images, audio);
• Knowledge of data visualization tools and techniques;
• Experience implementing AI solutions in production environments.