The person we are looking for will become part of Data Science and AI Competency Center working in AI Engineering team. Tasks:  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  What We're Looking For:  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: types of algorithms, machine learning frameworks, model efficiency metrics, model life-cycle, AI architectures  Good understanding of Cloud concepts and architectures, as well as working knowledge with selected cloud services, preferably Azure or GCP  Experience in at least one of following domains: 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 and support others  Taking responsibility for tasks and deliverables  Great problem-solving skills and critical thinking  Fluency in written and spoken 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, and experience in working with selected tools (preferably GitHub Actions, GitLab, or Azure DevOps)  Experience in productizing ML solutions using technologies like Spark/Databricks or Docker/Kubernetes. Missing one or two of these qualifications? We still want to hear from you! If you bring a positive mindset, we'll provide an environment where you feel valued and empowered to learn and grow.  We offer:  Stable employment. On the market since 2008, 1500+ talents currently on board in 7 global sites. “Office as an option” model. You can choose to work remotely or in the office.   Workation. Enjoy working from inspiring locations in line with our workation policy.  Great Place to Work® certified employer. Flexibility regarding working hours and your preferred form of contract.   Comprehensive online onboarding program with a “Buddy” from day 1.    Cooperation with top-tier engineers and experts.   Unlimited access to the Udemy learning platform from day 1. Certificate training programs. Lingarians earn 500+ technology certificates yearly.  Upskilling support. Capability development programs, Competency Centers, knowledge sharing sessions, community webinars, 110+ training opportunities yearly. Grow as we grow as a company. 76% of our managers are internal promotions.   A diverse, inclusive, and values-driven community.    Autonomy to choose the way you work. We trust your ideas.   Create our community together. Refer your friends to receive bonuses.   Activities to support your well-being and health. Social fund benefits for everyone. All Lingarians can apply for social fund benefits, such as vacation co-financing.  Plenty of opportunities to donate to charities and support the environment.   Modern office equipment. Purchased for you or available to borrow, depending on your location.