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.

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.