Data Science Engineer (AI)

TL;DR

Data Science Engineer (AI): Developing AI-driven solutions for the telecommunications domain with an accent on NLP, Generative AI, and RAG-based systems. Focus on building chatbots, voice bots, and predictive models to enhance customer experience and operational efficiency.

Location: Remote (Poland)

Company

A global technology service provider specializing in business advisory, enterprise solutions, and custom software development.

What you will do

• Support the development of AI/ML solutions, specifically NLP and Generative AI use cases.
• Contribute to the creation of chatbots, voice bots, and RAG-based solutions.
• Develop predictive models using historical data and machine learning techniques.
• Evaluate and compare model performance using relevant metrics.
• Collaborate with data engineers and developers to deploy models into production environments.
• Transform proof-of-concepts into scalable, production-ready solutions.

Requirements

• Degree in AI, Machine Learning, Data Science, or a related field.
• Initial professional experience or a strong academic background in Data Science/AI.
• Proficiency in Python, SQL, and Git.
• Basic hands-on experience with Generative AI, LLMs, and ML libraries like scikit-learn, TensorFlow, or XGBoost.
• Solid understanding of ML fundamentals (regression, tree-based models, deep learning).
• Must be based in Poland.

Nice to have

• Experience with NLP use cases, RAG systems, or LLM-based pipelines.
• Knowledge of Docker, Airflow, MLflow, or CI/CD pipelines.
• Exposure to AWS or on-premise deployments.
• Interest or experience in the telecom domain.
• Experience with multimodal or text-to-image models.

Culture & Benefits

• Commitment to diversity, equity, and inclusion across all levels of the organization.
• Opportunity to work within a diverse, international Data Science team.
• Hands-on experience across the full lifecycle of AI solutions from PoC to production.
• Supportive environment focused on professional growth and challenging existing approaches.

Hiring process

• Application review followed by a Talent Acquisition interview.
• Technical interview to assess expertise and problem-solving approach.
• Customer interview (depending on the specific role requirements).
• Final job offer.