Role: ML engineer

Location: Bengaluru

Experience: 3-6 years

Cloud Platform: Preferably Google Cloud Platform, AWS and Azure will also work

Role Summary

Seeking ML engineers to design, build, and deploy production-grade pricing, demand-forecasting, promotion-effectiveness, and competitor-pressure models for retail/CPG clients. The role will turn data science prototypes into reliable, automated systems that deliver recommendations to improve revenue, margin, price competitiveness, promotional effectiveness, and customer value.

The ML engineer will work closely with data scientists to deploy and operate models built on transaction history, product hierarchies, prices, promotions, inventory, competitor prices, customer behaviour, seasonality, and external factors. Responsibilities include building training and inference pipelines, managing model versioning and retraining, and monitoring performance, drift and data quality in production

Mandatory Skills

- Robust Python and SQL skills.
- Proficiency in Pandas, NumPy, Scikit-learn, statistical modelling, and visualization.
- Experience with MLOps practices: model deployment (batch and real-time), CI/CD. Experiment tracking, model versioning and retraining automation (e.g., MLflow, Docker, Airflow)
- Strong foundation in probability, hypothesis testing, confidence intervals, regression,



experimentation, and predictive modelling.
- Hands on experience productionizing models including scalable feature engineering pipelines, automated model validation and hyperparameter tuning.
- Understanding of ML models performance and latency trade-offs in production.
- Ability to collaborate with data scientists, engineers and commercial stakeholders, and to document and communicate model behaviour, performance and limitations clearly.
- Experience working with large datasets on BigQuery, Spark, or another cloud data platform.

Preferred Experience

- Prior experience building production models for retail/CPG domain.
- Hands on experience with Vertex AI (Pipelines, Model registry, endpoints), BigQuery ML, or GCP-based model-deployment and orchestration.
- Experience setting up model and data monitoring (performance, drift, data quality) and surfacing it through Power BI, Tableau, Looker or Grafana dashboards.
- Ability to translate pricing and commercial concepts (price elasticity, demand response, promotion uplift, and commercial performance metrics) into robust, reusable model features and evaluation frameworks.
- Knowledge of Logistic and Linear Regression, Random Forest, XGBoost, LightGBM, clustering, and ensemble methods.

📌 Ml Engineer (Bengaluru)
🏢 Straive
📍 Bengaluru