The role involves designing and implementing GenAI and MLOps solutions, developing production-ready ML code, and collaborating with Data Science teams to integrate AI models. It demands hands-on experience in data processing, cloud architectures, and AI concepts.
- 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.
Top Skills
Azure
Cosmodb
Databricks
Docker
Fastapi
GCP
Genai
Hive
Kubernetes
Llm
Mlops
Pyspark
Python
Redis
Spark
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