Machine Learning Engineer; Dataiku

Position: Machine Learning Engineer (Dataiku)
Location: Bengaluru

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/Git Hub workflows , CI pipelines, and code quality practices.
Ability to work in complex, multi‑market environments with evolving data and requirements.