Oracle

Systems Analyst 3-Support

Posted On

Dec. 17, 2025

Location

Bengaluru, Karnataka

Experience Level

Professional

Job Type

Permanent

Apply By

June 15, 2026

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.

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