Applied Ai Data Engineer

We are looking for Applied AI Data Engineer to join our diverse team of professionals. In this role, you will help build reliable data foundations for AI use cases — implementing ingestion, transformation, and retrieval components that power RAG, analytics, and agent workflows. You’ll work primarily in Python with modern data tools to turn business needs into maintainable pipelines. Your work will enable scalable, reliable AI solutions for real-world business challenges. This position offers the opportunity to work with cutting-edge AI technologies and contribute to creating value.

Responsibilities

• Develop and maintain data ingestion and transformation workflows using tools like Python, Spark/Databricks, or Pandas.

• Create and manage essential pipelines for LLM retrieval, including vectorization, chunking, and indexing features or embeddings.

• Design and execute data models in SQL, working across data lake or warehouse layers to maintain data quality and traceability.

• Provide data access through streamlined services and APIs to support automation and application needs.

• Enhance workflow visibility by adding logging and metrics and support CI/CD processes for data operations.

• Monitor and troubleshoot data workflows to ensure reliability and performance.

• Work closely with automation engineers to connect data services with LLM endpoints and related applications.

• Work with stakeholders to understand data requirements and ensure data pipelines meet business needs.

Basic Qualifications

• 1-3 years coding experience (Python preferred)

• Basic understanding of data privacy and security best practices.

• Practical experience with SQL and building ETL/ELT pipelines.

• Familiarity with Spark/Databricks or equivalent; Pandas for smaller datasets.

• Exposure to LLMs/embeddings and vector stores (projects/labs).

• Comfortable with Git and basic cloud CLI tooling.

Preferred Qualifications

• Experience in Cloud/data stack tools (Azure, AWS, GCP, Databricks).

• Experience with version control and collaborative development (e.g., GitHub, GitLab)

• dbt, Airflow/Prefect, Kafka/Event Hubs; containerization basics (Docker).

• Vector databases/search (FAISS, Pinecone, Azure AI Search).

• RAG pipeline design; prompt/response management for data retrieval.

Professional Skills

• Builder mindset; systems thinking; quality‑minded (tests, docs, continuous improvement)

• Occasional travel may be required

• Full professional proficiency in English is required. Finnish is optional but advantageous.