Role Purpose
The Machine Learning Engineer will be responsible for preparing data, developing and optimizing machine learning models, and collaborating with developers to integrate these models into production systems. This role bridges the gap between data science and engineering, with a focus on predictive modeling and business intelligence use cases. We also expect having experience in developing applications using .net.
Key Responsibilities
- – Data Preparation: Collect, clean, and preprocess data from internal and external sources to ensure quality and consistency for modeling.
- – Model Development: Design, train, and validate machine learning models using appropriate algorithms and techniques.
- – Model Optimization: Perform feature engineering, hyperparameter tuning, and performance evaluation to improve model accuracy and efficiency.
- – Collaboration: Work closely with data analysts, business stakeholders, and software developers to align model outputs with business needs and ensure smooth handoff for integration.
- – Documentation: Maintain clear documentation of data sources, modeling decisions, performance metrics, and integration requirements.
- – Continuous Improvement: Stay informed about new ML tools, frameworks, and best practices to enhance team capabilities.
- – Integrate the models in to .net applications.
Scope & Impact
This role directly supports the organization’s data-driven decision-making by delivering high-quality predictive models and insights. The ML Engineer will contribute to key initiatives in forecasting, customer behavior analysis, and operational optimization.
Key Interfaces
- – Software Developers
- – Business Intelligence Team
- – Functional areas of business
Required Skills & Experience
- – 2–5 years of experience in machine learning or data science roles
- – 5+ years of experience in building applications using .net technology
- – Proficiency in Python and ML libraries (e.g., Scikit-learn, XGBoost, LightGBM)
- – Strong SQL skills and experience working with relational databases
- – Solid understanding of supervised and unsupervised learning techniques
- – Experience with data wrangling, feature engineering, and model evaluation
- – Ability to communicate technical concepts to non-technical stakeholders
Preferred Skills
- – Experience with cloud platforms (AWS, GCP, Azure)
- – Familiarity with MLOps tools (e.g., MLflow, DVC, Airflow)
- – Exposure to BI tools (e.g., Tableau, Power BI)
- – Understanding of software development lifecycle and version control (Git)
- – Expert in Microsoft .Net languages/technologies including C#, ASP.NET, ADO.NET, MVC and Entity Framework.
- -Good working experience with object-oriented programming (OOPs) concepts.
- – Expert in building services using WCF/REST and good understanding of SOA.
