Remote AI Engineer

Wymagania: AI Engineer Implementing ML and AI solutions, leveraging LLMs and emerging AI technologies to build scalable, high-performance intelligence systems.   About the Role As an AI Engineer at DS Stream, you will bridge the gap between traditional data science and modern AI applications. Leveraging your strong foundation in machine learning, statistics, and data engineering, you will design, train, and deploy ML models while integrating GenAI capabilities into existing data pipelines. You will be responsible for the full ML lifecycle - from experimentation and model development to production deployment and monitoring - ensuring our AI solutions are robust, scalable, and aligned with business objectives.   Requirements Must Have: 3+ years of professional experience in Data Science or Machine Learning Engineering Strong proficiency in Python and ML ecosystem (NumPy, Pandas, PySpark, scikit-learn) Hands-on experience with deep learning frameworks (PyTorch or TensorFlow) Experience with SQL and relational/non-relational/vector databases Solid understanding of ML fundamentals (supervised/unsupervised learning, model evaluation, feature engineering) Practical experience with LLM orchestration frameworks (LangChain, LangGraph, or LlamaIndex) Understanding of GenAI integration patterns and LLM APIs Strong foundation in statistics and mathematics (linear algebra, probability, optimization) Experience building and deploying ML models to production Hands-on with at least one cloud ML platform (AWS SageMaker, Azure ML, Google Vertex AI, Databricks) Good communication skills in English (B2+) Nice to Have: Familiarity with NLP techniques and transformer architectures Experience with LLM fine-tuning and adaptation techniques Knowledge of RAG systems and embedding models Familiarity with MLOps practices (model versioning, CI/CD, monitoring) Working knowledge of data pipeline tools (Apache Spark, Airflow, or similar) Experience with experiment tracking tools (MLflow, Neptune, Weights & Biases) Docker, Kubernetes, and containerized ML deployments Experience with A/B testing and model performance optimization