Data Scientist / Machine Learning Engineer

Quick Facts

• Role: Data Scientist / Machine Learning Engineer

Description

Build and deploy scalable AI and machine learning solutions across multiple domains, including LLM-powered knowledge graph systems, automotive document data extraction and relationship modeling, and advanced analytics for software engineering repositories.

You will design AI solution architectures, translate business problems into data science tasks, and oversee data pipelines while ensuring data quality and security. You will research and select the best AI/ML technologies, coordinate with engineering teams, and communicate findings to stakeholders.

Benefits

• Flexible work arrangements (remote or modern offices/coworking spaces)

• Career growth via knowledge sharing, language classes, and sponsored training/conferences (including Databricks training/certifications)

• Choice of cooperation model (B2B or contract of mandate) with 20 fully paid days off

• Medical and sports packages, eye care, and well-being support (including psychotherapy/coaching)

• Full work equipment (e.g., laptop and necessary devices)

• Support for personal brand growth (conferences, blog, meetups)

• Smooth onboarding with a dedicated buddy and a supportive, autonomous culture

• Team-building events and integration budget; work anniversary/birthday/milestone celebrations

Requirements

• At least 3+ years of commercial experience designing and implementing scalable AI solutions (ML, predictive modeling, optimization, NLP, computer vision, GenAI)

• Proficiency developing ML algorithms from scratch to production deployment

• Strong Python programming skills (clean code, OOP design) and deep knowledge of ML libraries (Scikit-Learn, PyTorch/TensorFlow)

• Proven cloud deployment experience (AWS or Azure)

• Knowledge applying LLMs (semantic search, prompt engineering, multimodal embeddings, RAG)

• High English proficiency (C1)

• Excellent communication skills and consulting/client interaction experience

• Working experience with SQL and NoSQL databases (MongoDB, Snowflake, Databricks)

• Knowledge of CI/CD principles using GitHub and GitHub Actions

• Familiarity with MLOps practices plus Kubernetes and Docker

• Bachelor’s or Master’s degree in CS, Data Science, Mathematics, Physics, or a related field

• Plus: ML leadership/team mentoring, Big Data tech (Spark, Hadoop, Kafka), and frontend experience (React, Node.js, Nest.js, TypeScript)