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.