Job Description
We are seeking a
Machine Learning Engineer with strong experience in
classical machine learning and
production-grade systems to 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) using
ML-first approaches, with experience in OCI - Generative AI.
The engineer will own solutions end-to-end, including
go-live and post-production support.
Key Responsibilities
ML Solution Development
- Design and implement classical ML models for 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 advanced feature engineering across structured and semi-structured datasets.
- Define problem statements, evaluation metrics, and success criteria aligned with business KPIs.
Production Deployment & Go-Live
- Deploy ML solutions into production environments (batch, near real-time, or real-time).
- Build and maintain scalable ML pipelines for training, scoring, retraining, and inference.
- Participate in go-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
- Provide post go-live production support for ML systems.
- Monitor model performance, data quality, and operational metrics.
- Detect and mitigate data drift, concept drift, and pipeline failures.
- Perform root cause analysis and implement long-term fixes.
- Ensure compliance with SLAs/SLOs for ML-driven services.
Required Skills & Qualifications
Machine Learning & Analytics
- 4-8yrs of experience
- Strong experience with classical ML algorithms:
- Linear and Logistic Regression
- Decision Trees, Random Forests
- Gradient Boosting (XGBoost, LightGBM, CatBoost)
- Clustering and dimensionality reduction
- Solid understanding of statistics, probability, and model evaluation techniques.
Programming & Data
- Proficiency in Python (Pandas, NumPy, Scikit-learn).
- Strong SQL skills.
- Experience working with large-scale structured datasets.
Production & MLOps
- Proven experience deploying ML models to production systems.
- Experience with monitoring, alerting, and incident resolution.
- Familiarity with MLflow or similar tools, Docker, and CI/CD pipelines.
- Experience with cloud platforms (OCI, AWS, GCP, or Azure).
Good to Have (Optimization & OR Exposure)
- Exposure to optimization and operations research techniques, such as:
- Linear Programming (LP)
- Mixed-Integer Programming (MIP)
- Network flow models
- Heuristics and metaheuristics
- Ability to combine ML outputs with optimization models for decision-making systems.
Responsibilities
We are seeking a
Machine Learning Engineer with strong experience in
classical machine learning and
production-grade systems to 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) using
ML-first approaches, with experience in OCI - Generative AI.
The engineer will own solutions end-to-end, including
go-live and post-production support.
Key Responsibilities
ML Solution Development
- Design and implement classical ML models for 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 advanced feature engineering across structured and semi-structured datasets.
- Define problem statements, evaluation metrics, and success criteria aligned with business KPIs.
Production Deployment & Go-Live
- Deploy ML solutions into production environments (batch, near real-time, or real-time).
- Build and maintain scalable ML pipelines for training, scoring, retraining, and inference.
- Participate in go-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
- Provide post go-live production support for ML systems.
- Monitor model performance, data quality, and operational metrics.
- Detect and mitigate data drift, concept drift, and pipeline failures.
- Perform root cause analysis and implement long-term fixes.
- Ensure compliance with SLAs/SLOs for ML-driven services.
Qualifications
Career Level - IC3
About Us
As a world leader in cloud solutions, Oracle uses tomorrow's technology to tackle today's challenges. We've partnered with industry-leaders in almost every sectorand continue to thrive after 40+ years of change by operating with integrity.
We know that true innovation starts when everyone is empowered to contribute. That's why we're committed to growing an inclusive workforce that promotes opportunities for all.
Oracle careers open the door to global opportunities where work-life balance flourishes. We offer competitive benefits based on parity and consistency and support our people with flexible medical, life insurance, and retirement options. We also encourage employees to give back to their communities through our volunteer programs.
We're committed to including people with disabilities at all stages of the employment process. If you require accessibility assistance or accommodation for a disability at any point, let us know by emailing [Confidential Information] or by calling +1 888 404 2494 in the United States.
Oracle is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans status, or any other characteristic protected by law. Oracle will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law.
