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