ML/AI Engineer - GCP, Azure

ML/AI Engineer in Data Science and AI Competency Center working in AI Engineering team. Tasks/ Responsibilities: Working with Data Science teams to implement Machine Learning models into production Design, delivery GenAI solutions Practical and innovative implementations of LLM/ML/AI automation, for scale and efficiency Design, delivery and management of industrialized processing pipelines Defining and implementing best practices in ML models life cycle and ML operations/LLM operations Implementing AI /MLOps/LLMOps frameworks and supporting Data Science teams in best practices Gathering and applying knowledge on modern techniques, tools and frameworks in the area of ML Architecture and Operations Gathering technical requirements & estimating planned work Presenting solutions, concepts and results to internal and external clients Creating technical documentation. Must Have: At least 5+ years of Data engineering experience with last 3 years experience in building Data processing; At least 5+ years of experience in production-ready Python code development (e.g., microservices, APIs, etc.); At least 3+ years of experience in production-ready ML-related code development; At least 1+ years of experience with GenAI (ChatGPT, Gemini, RAGs, prompt engineering); Practical experience in MLOps/LLMOps tools like AzureML/AzureAI; Practical experience with Databricks; Good understanding of ML/AI concepts; Good understanding of Cloud concepts and architectures, preferably Azure or GCP; Experience in at least one of Data Warehouse, Data Lake, Data Integration, Data Governance, Machine Learning, Deep Learning, MLOps; Practical experience in Spark/PySpark and Hive within Big Data Platforms like Databricks, EMR or similar; Experience in designing and implementing data pipelines; Good communication skills; Ability to work in a team; Fluency in English. What Will Set You Apart: Experience in designing, programming ML algorithms, and data processing pipelines using Python; Good understanding of CI/CD and DevOps concepts; Experience in productizing ML solutions using Spark/Databricks or Docker/Kubernetes. We offer: Office as an option – remote or office depending on location; etc.