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.