ML/AI Engineer at lingarogroup

• The person we are looking for will become part of Data Science and AI Competency Center working in AI Engineering team. The key duties are: • Design, deliver and scale GenAI solutions • Practical and innovative implementations of LLM/ML/AI automation, for scale and efficiency • Working with Data Science teams to implement AI Agents and Machine Learning models into production • Design, delivery and management of industrialized processing pipelines • 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 • Defining and implementing best practices in ML models life cycle and ML operations/LLM operations • Gathering technical requirements & estimating planned work • Presenting solutions, concepts and results to internal and external clients • Creating technical documentation • At least 4+ years of Data engineering experience with last 1 year-experience in building Data processing • At least 4+ years of experience in production-ready Python code development (e.g., microservices, APIs, etc.) • At least 1+ years of experience with GenAI (various LLM models, agents, RAGs, prompt engineering, MCP, specification-driven-development) • At least 2+ years of experience in production-ready ML-related code development • Additionally for all levels: • 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 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. • Nice to have skills & knowledge: • Experience with LangGraph, FastAPI, CosmoDB, Redis, SpyGlass, Kubernetes • Experience in designing, programming ML algorithms, and data processing pipelines using Python • Experience in at least one of following domains: Data Warehouse, Data Lake, Data Integration, Data Governance, Machine Learning, Deep Learning, MLOps • Practical experience in MLOps/LLMOps tools like AzureML/AzureAI (or GCP equivalents) • Practical experience with Databricks • Practical experience in Spark/PySpark and Hive within Big Data Platforms like Databricks, EMR or similar • 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.