Senior AI Data Engineer
Nokia (company site, opens in a new tab)On-siteDirect career pageEst. 28–45 LPA
Posted 1d ago
Reach people at Nokia
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Nokia you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Own end-to-end data engineering solutions for Q365, Technology Standards Research & Innovation, and Patenting domains, including deployment and continuous improvement.
- Build and operate scalable data ingestion, transformation, and processing pipelines for standards and strategic datasets to create AI-ready data assets.
- Develop and maintain AI/LLM-assisted data processing, metadata management, data quality, and production operations using technologies like Python, Kubernetes, Spark, PostgreSQL, Elasticsearch, and Microsoft Fabric.
Minimum Requirements
- Strong hands-on experience in production-grade data and software engineering, including automated data pipeline design in Python.
- Experience with distributed data processing, data modeling, metadata, and data quality management.
- Familiarity with PostgreSQL, Elasticsearch or comparable data/search technologies and cloud-native engineering including Kubernetes and containerization.
- Work Experience Required: Not explicitly mentioned in the JD.
Ideal Candidate Profile
- Able to translate complex or ambiguous research and business requirements into scalable, reliable, and production-ready data solutions.
- Experienced in operating in environments combining AI, LLM, and agentic applications with a strong focus on data quality, observability, and reliability.
- Comfortable providing technical guidance and knowledge sharing while remaining actively hands-on in solution delivery.