We're building production AI systems that automate document-heavy and knowledge-heavy workflows across the business. As an AI Engineer, you'll own intelligent document processing pipelines (OCR + LLM), build copilot-style assistants on top of enterprise data, and ship everything end-to-end on Databricks and Azure.

Nokia is a global leader in connectivity for the AI era. With expertise across fixed, mobile and transport networks, powered by the innovation of Nokia Bell Labs, we’re advancing connectivity to secure a brighter world. 

Our recruitment process

We act inclusively and respect the uniqueness of people. Our employment decisions are made regardless of race, color, national or ethnic origin, religion, gender, sexual orientation, gender identity or expression, age, marital status, disability, protected veteran status or other characteristics protected by law. We are committed to a culture of inclusion built upon our core value of respect.

If you’re interested in this role but don’t meet every listed requirement, we still encourage you to apply. Unique backgrounds, perspectives, and experiences enrich our teams, and you may be just the right candidate for this or another opportunity.

The length of the recruitment process may vary depending on the specific role's requirements. We strive to ensure a smooth and inclusive experience for all candidates. Discover more about the recruitment process at Nokia. 

• Intelligent Document Processing. Design and ship OCR + LLM pipelines that extract, classify, and validate information from scanned documents and PDFs. You'll own the full loop — from layout-aware parsing to entity extraction, confidence scoring, and feedback-driven improvement.
• Conversational AI on enterprise data. Build copilot-style assistants and query interfaces over our Databricks data products, with proper authentication, role-based access, and grounded responses.
• End-to-end delivery on Databricks and Azure. PySpark pipelines, Delta Lake, MLflow for experiment tracking and model registry, batch and real-time inference. Deploy on Azure with Azure OpenAI, Azure AI services, ADLS, Key Vault, and proper monitoring. Set up CI/CD and MLOps practices that make releases boring.
• Reusable foundations. Prompt patterns, evaluation harnesses, pipeline components, model templates — the assets that let the team move faster on the next use case.

Must have:

• Strong Python and PySpark skills, with real experience shipping ML or data products to production
• Solid foundation in classical ML (classification, clustering, similarity, anomaly detection) — you know when not to reach for an LLM
• Hands-on experience with LLMs in production: RAG, extraction, evaluation, prompt design
• Databricks delivery experience (Delta Lake, MLflow, performance tuning)
• Comfortable on Azure: identity, secrets management, networking, and the AI/data services
• Engineering discipline: Git, code review, testing, clear documentation
• Fluent with AI coding assistants (Cursor, Claude Code, Copilot, or similar) as part of your daily workflow
• Able to work directly with business stakeholders, debug across the stack, and explain trade-offs clearly.

Nice to have:

• Experience with OCR pipelines and document AI in production
• Exposure to LLM evaluation frameworks and automated testing for AI systems

• Experience with Responsible AI, security controls, and enterprise compliance

  
  

  controls, and enterprise compliance