ML/AI Engineer

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.