Senior Machine Learning Engineer (Classical ML & MLOps)

About the Role

We are seeking a

Senior Machine Learning Engineer

with strong expertise in

classical machine learning algorithms ,

model optimization , and

end-to-end MLOps deployment .

You will design, build, and operationalize scalable ML pipelines and ensure that ML models transition smoothly from experimentation to production. The ideal candidate combines deep technical skills with hands-on experience in automating and maintaining ML systems at scale.

Key Responsibilities

Machine Learning Development

Design, train, and validate

classical ML models

(regression, tree-based models, ensemble methods, clustering, anomaly detection, etc.) for structured / tabular datasets.

Collaborate with data scientists and data engineers to translate analytical models into

production-grade code .

Perform

feature engineering, model tuning, and evaluation

using frameworks like Scikit-learn, XGBoost, LightGBM, or CatBoost.

Analyze data quality, perform exploratory data analysis (EDA), and create reusable feature stores.

MLOps & Productionization

Build and automate

end-to-end ML pipelines

for training, testing, deployment, monitoring, and retraining.

Manage

model lifecycle

using tools like

MLflow ,

Kubeflow ,

Vertex AI ,

SageMaker , or

Azure ML Studio .

Implement

CI / CD pipelines

for ML workflows using

GitHub Actions, Jenkins, or GitLab CI .

Develop

containerized ML services

using Docker and orchestrate them via

Kubernetes or ECS / EKS .

Design and monitor

model performance metrics ,

data drift , and

concept drift

in production environments.

Work closely with DevOps and Data Engineering teams to integrate ML services with backend systems and APIs.