Machine Learning Engineer
- 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.
Machine Learning Engineer
- 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.
Career Level - IC3
