Dataiku DSS consultants and enterprise AI developers

Dataiku DSS consultants configure data flows when spreadsheet preparation or fragmented pipelines delay analysis. They review existing projects and identify which steps need code or visual recipes.

• Data engineering: Build SQL and Python recipes, configure database connections, and implement incremental processing with explicit dependency checks.
• Business data preparation: Translate reporting definitions into reusable datasets and validation rules. Configure project permissions so access matches business responsibilities.

Existing flows can also be reviewed for slow queries and unnecessary data movement before extending them.

Machine learning architects assess whether a proposed use case has suitable data and a measurable acceptance criterion. They define training and serving architecture around latency requirements and infrastructure constraints.

Data scientists develop forecasting or classification models in Dataiku, using visual machine learning or Python where custom logic is needed. Work includes leakage checks and evaluation against a baseline. Specialists can review an existing experiment before production investment, documenting where its results may fail to represent real operating conditions.

Enterprise AI developers connect Dataiku projects to applications through prediction APIs or batch interfaces. They write custom Python components where visual recipes cannot express the required logic.

• Application integration: Implement authenticated API calls and error handling between DSS workloads and business systems. Validate request contracts before release.
• Generative AI: Build retrieval-augmented generation workflows using Dataiku LLM Mesh where supported by the installed version. Configure model connections and evaluate answers against approved test cases, with checks on document access and sensitive data exposure.

Dataiku engineers maintain scheduled workloads and investigate failures across Automation nodes and API nodes. They configure scenarios and alerts so failed runs reach the responsible team with useful diagnostic information.

MLOps specialists package project bundles and validate releases between environments. They establish model performance checks and investigate drift against agreed thresholds. For DSS upgrades, platform engineers review compatibility and test code environments before release. Support can cover incident recovery or ongoing maintenance within your existing operational responsibilities.