Senior AI ML Engineer

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.