Data Scientist / ML Engineer

As a Data Scientist / ML Engineer, you will work with a team of technology experts on challenging projects across various industries, using cutting-edge technologies.

Projects

• Build a backend system for an AI-powered industrial-engineering platform that uses LLMs to transform complex technical documents into a structured knowledge graph, enabling semantic search, automated documentation, and smarter knowledge management.
• Implement, deploy, and continuously develop an automotive data solution that processes and analyzes multiple document types, particularly digital file formats; extract key information, identify complex relationships across disparate data sources, and integrate the solution with the client’s existing infrastructure.
• Develop AI solutions supporting software engineering, including advanced analysis of source-code repositories and relationships across files and documentation; create grammatical rules for unknown programming languages and automatically generate training content from those rules.

Responsibilities

• Develop, implement, and validate machine-learning solutions aligned with project objectives.
• Design AI solution architectures that address complex business goals and drive innovation.
• Research, test, and select suitable tools, technologies, and AI solutions for business requirements.
• Understand client needs and translate business problems into data-science problems.
• Oversee data-pipeline and workflow implementation, establishing data-quality and security standards.
• Coordinate with Data Engineering and Software Engineering teams on the strategy and delivery of AI applications.
• Present findings and recommendations clearly and concisely to stakeholders.
• Prioritize and manage tasks within an Agile/Scrum framework to ensure timely delivery.

What you’ll need to succeed in this role

• At least 3+ years of commercial experience designing and implementing scalable AI solutions in Machine Learning, Predictive Modeling, Optimization, NLP, Computer Vision, or GenAI.
• Proficiency developing ML algorithms from scratch through production deployment.
• Strong Python programming skills, including clean code, OOP design, and extensive knowledge of ML libraries such as Scikit-Learn and PyTorch or TensorFlow.
• Proven experience deploying solutions in AWS or Azure cloud environments.
• Knowledge of applying LLMs, including semantic search, prompt engineering, multimodal embeddings, and RAG.
• C1-level English proficiency.
• Excellent communication skills and consulting experience involving direct client interaction.
• Experience with SQL and NoSQL databases, including MongoDB, Snowflake, and Databricks.
• Good knowledge of CI/CD principles using GitHub and GitHub Actions.
• Familiarity with MLOps practices, Kubernetes, and Docker.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Physics, or a related field.

Additional preferred experience

• Leadership and team-mentoring experience in Machine Learning projects.
• Experience with Big Data technologies such as Spark, Hadoop, and Kafka.
• Practical experience with frontend frameworks and technologies, such as React, Node.js, Nest.js, and TypeScript.

Perks and benefits

• Work in a supportive team of AI and Big Data enthusiasts.
• Work on international projects with global enterprises and startups.
• Flexible remote work or access to modern offices and coworking spaces.
• Career paths, knowledge-sharing initiatives, language classes, and sponsored training or conferences, including Databricks training materials and certifications.
• Choice of B2B cooperation or a contract of mandate, with 20 fully paid days off.
• Team-building events and an integration budget.
• Recognition of work anniversaries, birthdays, and milestones.
• Medical and sports packages, eye care, and well-being support, including psychotherapy and coaching.
• Full work equipment, including a laptop and necessary devices.
• Opportunities to build a personal brand through conference speaking, blog writing, and meetups.
• Smooth onboarding with a dedicated buddy in a friendly, supportive, and autonomous culture.