Machine Learning Engineer (Dataiku)

Role Overview

We are seeking a AI/ML Engineer to build, scale, and operate production grade machine learning pipelines supporting Dynamic Targeting across global markets.

This role focuses on engineering standardized, reusable ML workflows using Dataiku DSS, containerized execution environments, and modern MLOps practices. The engineer will work closely with data scientists, product, and platform teams to enable repeatable localization, automated QA, and reliable deployment of Dynamic Targeting models.

Key Responsibilities (Technical)

• Engineer and maintain Dataiku projects across DEV/PROD, following standardized patterns for Dynamic Targeting pipelines.
• Build and operationalize Python based ML pipelines, converting analytical logic into reusable, production ready components.
• Package and run ML workloads using containerized environments to ensure reproducibility across markets and environments.
• Implement model deployment patterns, including saved models, standardized scoring flows, and versioned outputs.
• Enable experiment tracking, tuning, and model selection using MLOps tooling (e.g., MLflow, Optuna or equivalent).
• Develop automated data and output QA checks to validate model inputs, constraints, and call plan outputs before release.
• Support localization and scaling of Dynamic Targeting by parameterizing pipelines for new markets, brands, and geographies.
• Contribute to Git based development workflows, including code reviews, CI checks, testing, and release readiness.
• Collaborate with product, analytics, and engineering teams to resolve pipeline, data, or deployment issues.

Required Skills

• Required 4-10 years of experience in AIML or Data Science.
• Strong Python engineering experience for data and ML pipelines.
• Hands on experience with Dataiku (project design, operationalization, DEV/PROD workflows).
• Experience building and running ML workloads in containers (Docker or equivalent).
• Solid understanding of ML engineering and MLOps concepts (model lifecycle, reproducibility, monitoring, QA).
• Experience with Git/GitHub workflows, CI pipelines, and code quality practices.
• Ability to work in complex, multi market environments with evolving data and requirements.

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