Posted 18 May, 2026
563241 | ML Engineer / ML Engineering Specialist | IC | Pan India
ClifyX
Bengaluru,Karnataka,India
Full Time
Reference: 365_594563_26-04341
Role Overview
In this role, you will design, build, and deploy scalable machine learning solutions that support key commercial use cases including pricing optimization, product portfolio optimization, and consumption forecasting.
You will work closely with data scientists, data engineers, and commercial stakeholders to translate business problems into robust machine learning systems deployed in production. The ideal candidate combines strong machine learning expertise, software engineering best practices, and experience building scalable cloud-based ML systems.
What You Will Do
1. Design, develop, and deploy machine learning models and solutions that support pricing optimization, product portfolio optimization, and consumption forecasting initiatives within Revenue Growth Management.
2. Build and maintain production-grade ML pipelines for model training, evaluation, inference, and monitoring.
3. Perform data transformation and feature engineering to create reliable and scalable input features for machine learning models.
4. Implement model monitoring and drift detection frameworks to track data drift, feature drift, and model performance degradation in production.
5. Develop scalable APIs and ML services using frameworks such as FastAPI to integrate ML models into business applications.
6. Apply strong software engineering principles, including object-oriented programming, modular design, and code maintainability.
7. Implement unit and integration tests using frameworks such as pytest to ensure reliability and maintainability of ML systems.
8. Deploy and manage ML solutions in cloud environments, preferably Microsoft Azure.
9. Work with large-scale enterprise data platforms such as Snowflake and collaborate with data engineering teams to build reliable data pipelines.
10. Optimize model training and performance using distributed computing frameworks such as Ray and Dask.
11. Use Optuna or similar tools for hyperparameter tuning and model optimization.
12. Explore and implement Large Language Model (LLM) based solutions to address business problems such as knowledge retrieval, decision support, and automation of analytical workflows.
13. Participate in code reviews, system design discussions, and continuous improvement of engineering standards.
14. Translate business requirements into scalable machine learning solutions and communicate results effectively to both technical and non-technical stakeholders.
15. Collaborate closely with cross-functional teams including commercial, analytics, data engineering, and technology teams to deliver high-impact solutions.
Qualifications
1. Bachelor's degree in Computer Science, Machine Learning, Data Science, Statistics, or a related quantitative field.
2. 4+ years of experience building and deploying machine learning models and systems in production environments.
3. Strong proficiency in Python for machine learning and software development.
4. Strong understanding of object-oriented programming (OOP) and software design principles.
5. Experience building APIs using frameworks such as FastAPI or similar Python web frameworks.
6. Experience implementing unit testing using frameworks such as pytest.
7. Strong understanding of data transformation, feature engineering, and feature pipeline development for machine learning systems.
8. Experience implementing model monitoring, drift detection, and model performance tracking in production environments.
9. Experience working with cloud platforms, preferably Microsoft Azure.
10. Experience working with data lake or modern data platforms such as Snowflake.
11. Strong experience with machine learning frameworks such as scikit-learn, PyTorch, or TensorFlow.
12. Familiarity with Large Language Models (LLMs) and their application to solve business problems.
Preferred Qualifications
1. Experience with distributed computing frameworks such as Ray or Dask.
2. Experience with Optuna or similar hyperparameter optimization tools.
3. Experience with containerization technologies such as Docker.
4. Prior experience in Consumer Packaged Goods (Client) analytics.
5. Experience working on commercial analytics or Revenue Growth Management (RGM) use cases such as pricing, portfolio optimization, or consumption forecasting.
Soft Skills
1. Strong communication and presentation skills, with the ability to explain complex technical concepts to non-technical stakeholders.
2. Ability to collaborate effectively across cross-functional teams including business, analytics, and engineering.
3. Strong problem-solving mindset and ownership of end-to-end solutions.
4. Ability to manage multiple priorities in a fast-paced, collaborative environment.
5. Demonstrated ability to work with stakeholders to translate business problems into analytical solutions.
In this role, you will design, build, and deploy scalable machine learning solutions that support key commercial use cases including pricing optimization, product portfolio optimization, and consumption forecasting.
You will work closely with data scientists, data engineers, and commercial stakeholders to translate business problems into robust machine learning systems deployed in production. The ideal candidate combines strong machine learning expertise, software engineering best practices, and experience building scalable cloud-based ML systems.
What You Will Do
1. Design, develop, and deploy machine learning models and solutions that support pricing optimization, product portfolio optimization, and consumption forecasting initiatives within Revenue Growth Management.
2. Build and maintain production-grade ML pipelines for model training, evaluation, inference, and monitoring.
3. Perform data transformation and feature engineering to create reliable and scalable input features for machine learning models.
4. Implement model monitoring and drift detection frameworks to track data drift, feature drift, and model performance degradation in production.
5. Develop scalable APIs and ML services using frameworks such as FastAPI to integrate ML models into business applications.
6. Apply strong software engineering principles, including object-oriented programming, modular design, and code maintainability.
7. Implement unit and integration tests using frameworks such as pytest to ensure reliability and maintainability of ML systems.
8. Deploy and manage ML solutions in cloud environments, preferably Microsoft Azure.
9. Work with large-scale enterprise data platforms such as Snowflake and collaborate with data engineering teams to build reliable data pipelines.
10. Optimize model training and performance using distributed computing frameworks such as Ray and Dask.
11. Use Optuna or similar tools for hyperparameter tuning and model optimization.
12. Explore and implement Large Language Model (LLM) based solutions to address business problems such as knowledge retrieval, decision support, and automation of analytical workflows.
13. Participate in code reviews, system design discussions, and continuous improvement of engineering standards.
14. Translate business requirements into scalable machine learning solutions and communicate results effectively to both technical and non-technical stakeholders.
15. Collaborate closely with cross-functional teams including commercial, analytics, data engineering, and technology teams to deliver high-impact solutions.
Qualifications
1. Bachelor's degree in Computer Science, Machine Learning, Data Science, Statistics, or a related quantitative field.
2. 4+ years of experience building and deploying machine learning models and systems in production environments.
3. Strong proficiency in Python for machine learning and software development.
4. Strong understanding of object-oriented programming (OOP) and software design principles.
5. Experience building APIs using frameworks such as FastAPI or similar Python web frameworks.
6. Experience implementing unit testing using frameworks such as pytest.
7. Strong understanding of data transformation, feature engineering, and feature pipeline development for machine learning systems.
8. Experience implementing model monitoring, drift detection, and model performance tracking in production environments.
9. Experience working with cloud platforms, preferably Microsoft Azure.
10. Experience working with data lake or modern data platforms such as Snowflake.
11. Strong experience with machine learning frameworks such as scikit-learn, PyTorch, or TensorFlow.
12. Familiarity with Large Language Models (LLMs) and their application to solve business problems.
Preferred Qualifications
1. Experience with distributed computing frameworks such as Ray or Dask.
2. Experience with Optuna or similar hyperparameter optimization tools.
3. Experience with containerization technologies such as Docker.
4. Prior experience in Consumer Packaged Goods (Client) analytics.
5. Experience working on commercial analytics or Revenue Growth Management (RGM) use cases such as pricing, portfolio optimization, or consumption forecasting.
Soft Skills
1. Strong communication and presentation skills, with the ability to explain complex technical concepts to non-technical stakeholders.
2. Ability to collaborate effectively across cross-functional teams including business, analytics, and engineering.
3. Strong problem-solving mindset and ownership of end-to-end solutions.
4. Ability to manage multiple priorities in a fast-paced, collaborative environment.
5. Demonstrated ability to work with stakeholders to translate business problems into analytical solutions.
