AI Engineer (Langchain, PyTorch, AzureML)

O projekcie: Designing and operating AI-driven pipelines that automatically ingest, analyze, and classify operational incidents and tickets. The system leverages LLMs with structured prompt logic and few-shot prompting (using historically similar tickets) to deliver consistent, scalable triage — replacing manual classification and accelerating resolution. Wymagania: - 4+ 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 Codzienne zadania: - 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.