AI / ML Engineer

Project Role : AI / ML Engineer
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills : Machine Learning (ML)
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education

Summary:
As an AI / Machine Learning Engineer, a typical day involves designing and building advanced applications and systems that leverage artificial intelligence technologies and cloud-based AI services. The role requires integrating these solutions into robust production pipelines, whether hosted on cloud platforms or on-premises environments. The engineer applies generative artificial intelligence models and explores various domains such as deep learning, neural networks, conversational agents, and image analysis to create innovative and efficient solutions. Collaboration with cross-functional teams and continuous refinement of AI-driven applications are key aspects of the daily workflow.

Roles & Responsibilities:
- Develop, train, and evaluate machine learning models across a range of use cases end-to-end.
- Perform feature engineering and selection to optimise model performance and interpretability.
- Design and execute structured experimentation using MLflow to track hypotheses and outcomes.
- Apply model explainability techniques to support transparent and governed AI outputs.
- Build and maintain ML pipelines using Databricks, Spark SQL, and streaming data frameworks.
- Collaborate with data engineers and architects to ensure feature store integration and data quality.
- Participate in peer code reviews and contribute to shared engineering standards and best practices.

Professional & Technical Skills:
- Python: Strong proficiency in Python for data science and ML development.
- ML Frameworks: Hands-on experience with scikit-learn, XGBoost, and LightGBM.
- MLflow: Experience with experiment tracking, model versioning, and deployment via MLflow.
- Databricks: Proficiency in Databricks for notebook-based and pipeline-based ML development.
- Spark SQL & Streaming: Ability to work with structured and streaming data using Spark SQL.
- Feature Engineering: Demonstrated skill in designing and implementing feature transformation pipelines.
- Model Explainability: Familiarity with SHAP, LIME, or equivalent explainability frameworks.

Additional Information:
- Intellectually curious with a rigorous, hypothesis-driven approach to experimentation.
- Detail-oriented, with strong written documentation habits for models and experiments.
- Comfortable working in a collaborative team environment with shared code repositories.
- Minimum 4 years of hands-on experience in data science or ML engineering.