We are seeking aMachine Learning Engineerwith strong experience inclassical machine learningandproduction-grade systemsto build, deploy, and support data-driven optimization solutions. The role involves solving complex business problems (e.g., store operations, supply chain, pricing, planning, or resource optimization) usingML-first approaches, with experience in OCI - Generative AI.
The engineer will own solutions end-to-end, includinggo-live and post-production support.
Key Responsibilities
ML Solution Development
- Design and implementclassical ML modelsfor regression, classification, clustering, forecasting, and anomaly detection.
- Apply ML techniques to optimization-driven use cases such as:
- Demand and capacity forecasting
- Inventory and replenishment planning
- Pricing and promotion effectiveness
- Resource or space allocation
- Operational performance optimization
- Perform advancedfeature engineeringacross structured and semi-structured datasets.
- Define problem statements, evaluation metrics, and success criteria aligned with business KPIs.
Production Deployment & Go-Live
- Deploy ML solutions intoproduction environments(batch, near real-time, or real-time).
- Build and maintainscalable ML pipelinesfor training, scoring, retraining, and inference.
- Participate ingo-live readiness, including production validation, rollout planning, and controlled releases.
- Collaborate with data engineering, platform, and business teams to ensure reliable delivery.
Post Go-Live Support & Reliability
- Providepost go-live production supportfor ML systems.
- Monitor model performance, data quality, and operational metrics.
- Detect and mitigatedata drift, concept drift, and pipeline failures.
- Performroot cause analysisand implement long-term fixes.
- Ensure compliance withSLAs/SLOsfor ML-driven services.
Required Skills & Qualifications
Machine Learning & Analytics
- 4-8yrs of experience
- Strong experience withclassical ML algorithms:
- Linear and Logistic Regression
- Decision Trees, Random Forests
- Gradient Boosting (XGBoost, LightGBM, CatBoost)
- Clustering and dimensionality reduction
- Solid understanding ofstatistics, probability, and model evaluation techniques.
Programming & Data
- Proficiency inPython(Pandas, NumPy, Scikit-learn).
- StrongSQLskills.
- Experience working withlarge-scale structured datasets.
Production & MLOps
- Proven experience deploying ML models toproduction systems.
- Experience withmonitoring, alerting, and incident resolution.
- Familiarity withMLflow or similar tools, Docker, and CI/CD pipelines.
- Experience withcloud platforms(OCI, AWS, GCP, or Azure).
Good to Have (Optimization & OR Exposure)
- Exposure tooptimization and operations research techniques, such as:
- Linear Programming (LP)
- Mixed-Integer Programming (MIP)
- Network flow models
- Heuristics and metaheuristics
- Ability to combineML outputs with optimization modelsfor decision-making systems.
We are seeking aMachine Learning Engineerwith strong experience inclassical machine learningandproduction-grade systemsto build, deploy, and support data-driven optimization solutions. The role involves solving complex business problems (e.g., store operations, supply chain, pricing, planning, or resource optimization) usingML-first approaches, with experience in OCI - Generative AI.
The engineer will own solutions end-to-end, includinggo-live and post-production support.
Key Responsibilities
ML Solution Development
- Design and implementclassical ML modelsfor regression, classification, clustering, forecasting, and anomaly detection.
- Apply ML techniques to optimization-driven use cases such as:
- Demand and capacity forecasting
- Inventory and replenishment planning
- Pricing and promotion effectiveness
- Resource or space allocation
- Operational performance optimization
- Perform advancedfeature engineeringacross structured and semi-structured datasets.
- Define problem statements, evaluation metrics, and success criteria aligned with business KPIs.
Production Deployment & Go-Live
- Deploy ML solutions intoproduction environments(batch, near real-time, or real-time).
- Build and maintainscalable ML pipelinesfor training, scoring, retraining, and inference.
- Participate ingo-live readiness, including production validation, rollout planning, and controlled releases.
- Collaborate with data engineering, platform, and business teams to ensure reliable delivery.
Post Go-Live Support & Reliability
- Providepost go-live production supportfor ML systems.
- Monitor model performance, data quality, and operational metrics.
- Detect and mitigatedata drift, concept drift, and pipeline failures.
- Performroot cause analysisand implement long-term fixes.
- Ensure compliance withSLAs/SLOsfor ML-driven services.
Required Skills & Qualifications
Machine Learning & Analytics
- 4-8yrs of experience
- Strong experience withclassical ML algorithms:
- Linear and Logistic Regression
- Decision Trees, Random Forests
- Gradient Boosting (XGBoost, LightGBM, CatBoost)
- Clustering and dimensionality reduction
- Solid understanding ofstatistics, probability, and model evaluation techniques.
Programming & Data
- Proficiency inPython(Pandas, NumPy, Scikit-learn).
- StrongSQLskills.
- Experience working withlarge-scale structured datasets.
Production & MLOps
- Proven experience deploying ML models toproduction systems.
- Experience withmonitoring, alerting, and incident resolution.
- Familiarity withMLflow or similar tools, Docker, and CI/CD pipelines.
- Experience withcloud platforms(OCI, AWS, GCP, or Azure).
Good to Have (Optimization & OR Exposure)
- Exposure tooptimization and operations research techniques, such as:
- Linear Programming (LP)
- Mixed-Integer Programming (MIP)
- Network flow models
- Heuristics and metaheuristics
- Ability to combineML outputs with optimization modelsfor decision-making systems.
Career Level - IC3
