Position Summary :

We are seeking a skilled ML Engineer - AI/ML Platform & MLOps with 3 - 8 years of experience in building, deploying, monitoring, and scaling end-to-end Machine Learning solutions.

The ideal candidate will have expertise across the complete AI/ML lifecycle, including data engineering, feature engineering, model development, deployment, MLOps, monitoring, governance, and AI application development.

Strategic Responsibilities :

- Design and develop scalable data pipelines for structured and unstructured data to support enterprise AI initiatives.

- Build reusable feature engineering frameworks, feature stores, and data quality validation pipelines.

- Develop, train, optimize, and deploy Machine Learning models for business use cases such as demand forecasting, demand sensing, customer churn prediction, recommendation systems, price elasticity, optimization, NLP, regression, classification, and time-series forecasting.

- Build AI-powered business applications, intelligent decision-support systems, and production-grade ML services.

- Develop APIs, microservices, inference services, and scoring engines for real-time and batch model serving.

- Design and implement robust MLOps pipelines, including CI/CD workflows, automated model deployment, experiment tracking, and model versioning.

- Build automated model retraining and continuous delivery pipelines across cloud and on-premise environments.

- Implement monitoring frameworks for model drift, data drift, concept drift, explainability, fairness, bias detection, and performance degradation.

- Contribute to the development of enterprise AI/ML platforms, reusable ML components, accelerators, and governance frameworks.

- Develop monitoring dashboards, operational metrics, and governance workflows to ensure reliable AI system performance.

- Collaborate with Data Scientists, Data Engineers, Product teams, and Business stakeholders to build scalable AI solutions.

- Continuously evaluate emerging AI/ML technologies and integrate engineering best practices into platform development.

Technical Skills :

- Machine Learning : Scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow, PyTorch.

- Data Engineering : SQL, PySpark, Databricks, Apache Spark, Airflow, BigQuery.

- MLOps : MLflow, Kubeflow, SageMaker, Vertex AI, Azure Machine Learning, Databricks.

- Programming : Python, FastAPI, Flask, REST APIs.

- Cloud Platforms : Microsoft Azure, AWS, Google Cloud Platform (GCP).

- Containers & DevOps : Docker, Kubernetes, Terraform, GitHub Actions, Jenkins.

Educational Qualifications :

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related field.