Role overview
We’re looking for a hands-on Machine Learning Engineer with strong experience building and deploying real-world AI systems. This role focuses on designing scalable ML and LLM-powered solutions for processing large volumes of unstructured data and turning them into structured, actionable insights.
You’ll work at the intersection of machine learning, LLMs and data pipelines – owning systems end-to-end, from experimentation to production.
Responsibilities
- Design and build scalable ML/LLM pipelines for processing large-scale document and text data
- Develop and optimize LLM-based extraction workflows (classification, entity extraction, summarization, linking)
- Integrate LLMs into production systems, including prompt design, evaluation, and orchestration
- Work with structured and unstructured data, ensuring data quality and consistency across pipelines
- Implement entity resolution and data linking logic across multiple data sources
- Collaborate on building knowledge-oriented systems (e.g. search, semantic layers, or lightweight graph structures)
- Optimize models and pipelines for performance, cost, and reliability in production environments
- Contribute to system design, including scalability, fault tolerance, and maintainability
- Work closely with cross-functional teams to deliver and iterate on AI-driven features
Basic qualifications
- 5+ years of experience in Machine Learning, Applied AI, or Data Engineering
- Strong Python skills and experience with ML/LLM ecosystems
- Hands-on experience deploying ML or LLM systems to production
- Experience with data processing frameworks (e.g. Spark, Beam, or similar)
- Solid understanding of ML fundamentals and NLP concepts
- Experience working with large datasets and building data pipelines
- Familiarity with LLM frameworks and tools (e.g. LangChain, LlamaIndex, or similar)
- Experience with SQL and data modeling
- Understanding of system design and scalable architectures
- Experience with Docker and cloud platforms (AWS, GCP, or Azure)
Preferred qualifications
- Experience with knowledge graphs or graph databases
- Experience building evaluation frameworks for LLM outputs
- Familiarity with vector databases and semantic search
- Experience working with on-prem or privacy-sensitive environments
- Exposure to backend development (e.g. APIs, microservices)
- Strong ownership mindset and ability to work independently
- Pragmatic approach to building production-ready AI systems
- Ability to balance experimentation with delivery
- Comfortable working in fast-moving, ambiguous environments
- Real-world AI systems (not just PoCs)
- High ownership and impact
- Modern ML + LLM stack
- Opportunity to shape scalable AI solutions from the ground up
- Covered vacation period: 20 business days and 5 days off
- Free English classes
- Flexible working schedule
- Truly friendly and supporting atmosphere
- Working remotely or in one of our offices
- Medical insurance for employees from Ukraine
- Legal support
Tags & focus areas
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