Lead AWS AI Engineer @ Algomine

• Higher education (computer science, data science, mathematics, econometrics, quantitative methods or related)
• Very good knowledge of AWS, especially AWS SageMaker and Bedrock
• Minimum 5–7 years of experience in implementing and deploying AI projects, including at least 3 years working with LLM
• Experience in designing architecture for AI/LLM solutions on an enterprise level
  
• Familiarity with Python and practical experience with AI/ML frameworks
  
• In-depth knowledge of: RAG, prompt engineering, fine-tuning, and adapters (LoRA, PEFT), training and evaluating language models (NLP, LLM, multimodal), orchestrating agents and workflow AI
  
• Experience in preparing data for AI solutions (ETL, SQL, data pipelines, embeddings, cleaning text and multimodal data)
• Ability to select suitable LLM models for specific tasks based on benchmarks, quality metrics, costs, and business requirements
  
• Familiarity with best engineering practices: CI/CD, code review, testing, GitLab/GitHub
  
• Ability to think analytically and translate complex AI concepts into simple terms for business
  
• Very good knowledge of the English language (C1)

• Participation in diverse and advanced technological Data Science and GenAI projects
• Modern technology stack and openness to implementing new solutions
• Work in a competency-based Data Science/AI team
  
• Elastic working hours and the possibility of 100% remote work
  
• Integration meetings and unforgettable company events

, [Designing and overseeing complex AI/LLM solutions in cloud and on-premise environments, Building and deploying advanced generative solutions (GenAI), including: RAG (Retrieval-Augmented Generation) architectures, agent-based architectures, and orchestrating agents (LangChain, LangGraph, Haystack, Semantic Kernel), Multimodal solutions (text, image, sound, video), Designing and leading end-to-end projects – from analysis and PoC to deployment in a production environment, Consulting and advising business clients – identifying use cases for AI, creating business cases, evaluating business value, Leading evaluations of new AI/LLM technologies (open-source and commercial, e.g. Hugging Face, OpenAI, Anthropic, Meta, Mistral, Google, Cohere), Implementing and optimizing MLOps/LLMOps processes (monitoring models, automating retraining, champion-challenger), Mentoring and supporting engineering teams implementing AI solutions, mentoring and supporting decision-making],