This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
As a Data & AI Engineer at NTT DATA, you will design and implement scalable data solutions on Azure and Databricks. This role requires 3-7 years of experience in data/AI engineering, with a strong focus on Python, PySpark, and SQL. A bachelor's or master's degree in Computer Science or a related field is preferred, along with relevant certifications.
Job Responsibility:
Build reliable batch/stream pipelines in Databricks (Python/PySpark, SQL) with Delta Lake and Unity Catalog
Implement best practices for code quality, testing, documentation, lineage, and cost‑efficient performance
Design and deliver GenAI solutions using Azure AI Foundry (projects, prompt flow/evaluation), Azure OpenAI (chat/completions, tool/function calling), and Azure AI Search (indexing, vector search, semantic ranking) with robust RAG patterns
Instrument evaluation, grounding, safety checks, and quality metrics for AI features
Use MLflow for experiment tracking, model packaging, and deployment
standardize environments and feature stores where relevant
Build CI/CD for data and AI workloads (e.g., Azure DevOps/GitHub Actions) and implement monitoring & observability (logs, metrics, drift)
Provision Azure and Databricks resources with Terraform (modular design, workspaces/state, policies, service principals, Key Vault)
Apply RBAC, secrets management, data masking, and governance (Unity Catalog / Purview) with privacy‑by‑design and compliance best practices
Partner with product owners, architects, and SMEs to translate use‑cases into simple, secure, cost‑aware solutions
Contribute to reference architectures, reusable components, and engineering standards
Requirements:
3–7+ years in data/AI engineering (or equivalent impact) delivering production systems on Azure
Python (production code, packaging, testing)
PySpark
SQL
Databricks (Repos/Workflows, Delta Lake, Unity Catalog, MLflow)
Azure AI Foundry (projects, prompt/eval flows, model endpoints)
Azure AI Search (index design, vector embeddings, skillsets/indexers, semantic search)