Senior ML Engineer (MLOps) at Makro PRO

Bangkok, , Thailand -

Full Time

Start Date

Immediate

Expiry Date

30 Sep, 26

Salary

0.0

Posted On

02 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

MLOps, Python, PySpark, SQL, MLflow, Databricks, CI/CD, Model Registry, Drift Detection, Feature Store, Spark, Cloud Computing, Model Governance, Causal Inference, Scikit-learn, XGBoost

Industry

Software Development

Description

The Senior ML Engineer (MLOps) owns the transition from ad-hoc ML deployments to a registered, monitored, governed ML platform — the lifecycle every data scientist and ML practitioner across the company uses. The role also curates a wrapper layer over open-source classical ML libraries (forecasting, causal, recommender, tabular) so retail algorithms ship on company-standard adapters rather than per-team reinventions. Key Responsibilities: Define and document the end-to-end MLOps lifecycle (experiment → train → register → approve → deploy → monitor → retrain) and enforce it via CI/CD gates. Stand up and operate the Model Registry (MLflow / Databricks Unity Catalog Models) as the single source of truth; ensure 100% of production models are registered, versioned, and tagged with model cards. Implement data drift, prediction drift, and performance-degradation monitoring with appropriate alerting and retraining triggers. Lead build / buy evaluation for a Feature Store; deploy a POC and eliminate train / serve skew end-to-end. Audit existing production ML models; register, document, and migrate each into the standard lifecycle; retire or consolidate models that cannot be justified. Curate and own a company-standard wrapper layer over open-source classical ML libraries (Prophet, statsmodels, DoWhy / EconML, LightFM, scikit-learn, XGBoost, LightGBM) with standard interfaces, lineage hooks, eval-harness integration, and CI/CD templates. Partner with Data Governance on the model-governance gate in the deployment pipeline; support audit and compliance evidence. Mentor data scientists on engineering discipline (reproducibility, lineage, rollback) and lead incident response for degraded production models. Bachelor's or Master's degree in Computer Science, Statistics, Applied Mathematics, or a related discipline. 5+ years building and operating ML systems in production (not only notebooks). Deep MLOps experience: model registry, experiment tracking, CI/CD for training and serving, versioning, approval gates. Built or operated drift detection for data and predictions in production; understands the difference and the right alert thresholds. Strong Python and Spark / PySpark; SQL fluency; cloud and Databricks (or equivalent lakehouse) production experience. Comfortable designing train / serve parity patterns and feature pipelines. Experience with at least one major MLOps stack (MLflow, Kubeflow, Vertex AI, SageMaker). Can write runbooks, lead incident response, and translate business KPIs into model SLOs. Preferred Qualifications Feature store production experience (Databricks Feature Store, Feast, Tecton). Retail ML use cases — demand forecasting, pricing optimisation, assortment, recommender, churn, uplift modelling. Causal inference and experimentation (A/B, switchback, geo-experiments) using DoWhy or EconML. Vendor or industry certifications such as Databricks Machine Learning Professional or Azure AI Engineer Associate.

Responsibilities

The role focuses on transitioning ad-hoc ML deployments to a governed, monitored MLOps platform and creating standardized wrapper layers for classical ML libraries. Key duties include managing the model registry, implementing drift monitoring, and establishing end-to-end CI/CD gates for the ML lifecycle.