AI/ML Engineers:

Python: Experience with ML Libraries and at least 5 years of coding experience of building pipelines in Python/PySpark (libraries like NumPy, pandas, scikit-learn).
Modelling Experience: Building Data science models (XGBoost, Regression etc)
Data preprocessing: Cleaning, transforming, and augmenting datasets to make them “ML-ready.” Experience with AWS GIS (Glue Interactive) and AWS Sagemaker
Feature engineering: Identifying and creating relevant features that improve model accuracy and interpretability.
Exploratory data analysis (EDA): Using tools (matplotlib, seaborn, etc.) to visualize data and gain insights.
Version control: Proficiency with Git and version control systems.
Code efficiency & optimization: Ability to write clean, optimized code for production settings (e.g., vectorization, concurrency where needed).
Containerization: Familiarity with Docker or similar for reproducible environments.
CI/CD pipelines: Experience integrating ML models into continuous integration/continuous delivery workflows. We use Git based CI/CD methods mostly.
Cloud services: Knowledge of AWS Lambda, AWS API Gateway, AWS Glue, Athena, S3 and Iceberg and Azure AI Studio for model hosting, GPU/TPU usage, and scalable infrastructure.

Will be good to have good communication skills and domain knowledge of Retail Investment.

LatentView Analytics

Latest Jobs