Machine Learning Engineer

1 день назад

Киев, Киев, Украина Sigma Software Полный рабочий день 60 000 ₴ - 120 000 ₴ в год
Company Description

We're looking for a Machine Learning Engineer to join our team 

If you're excited about applied AI and agentic systems and want to develop intelligent tools that truly collaborate with people, we'd love to have you join our team. Join us and help shape the future of AI-powered workplace assistants. 

We're building an advanced AI assistant that helps teams work smarter. By combining semantic search, agentic AI, and state-of-the-art language models, the system enhances internal operations through intelligent, context-aware support and personalized interactions. 

It's not a chatbot — it's a system that understands, adapts to, and collaborates with humans. 

Job Description
  • Develop and integrate ML and NLP models to power intelligent assistant features 
  • Build agentic workflows using LangChain, LangGraph, or similar frameworks 
  • Prototype user interfaces and internal tools using Streamlit or Gradio 
  • Collaborate with the engineering and product teams to plan and deliver ML-driven features 
  • Work with Docker to manage development and runtime environments 
  • Use Git for version control and write clean, maintainable code 
  • Query structured data using SQL 
  • Contribute to model deployment and operations in a cloud environment (primarily Azure) 
Qualifications
  • Strong proficiency in Python 
  • Experience with agentic AI frameworks such as LangChain or LangGraph 
  • Solid understanding of machine learning and NLP fundamentals 
  • Hands-on experience with ML frameworks (e.g., PyTorch, TensorFlow) 
  • Familiarity with prototyping tools such as Streamlit or Gradio 
  • Knowledge of engineering best practices: Git, Docker, cloud basics, and task estimation 
  • Practical knowledge of SQL 
  • Upper-Intermediate level of English (both written and spoken) 

WOULD BE A PLUS

  • Experience with other agentic or LLM orchestration tools 
  • Experience with MLOps or model deployment 
  • Comfortable working in Linux terminal environments 

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