5+ years of hands-on experience as a Machine Learning Engineer or similar role.
Expertise in visualizing and manipulating big datasets with spark.
Understanding of data modelling and software architecture and data platforms.
Experienced with modern tools for building ML pipelines, including data processing, training, inference and evaluation.Demonstrate effective coding, documentation, and communication habits
Technologies we work on:
ML Models Tabular on Xgboost, CatBoost, RF etc
Deep Learning for unstructured data. Partial RL(MAB) for online decision systems.
Distributed Model Training and Scoring on TFoS, TFJob, Spark ML etc. Big Data with PySpark pipelines.Git with CI and CD on Jenkins.
Deployment: Airflow, AWS(EMR or EKS) and Python.
Programming Languages; Python and SQL.
Responsibilities:
Data Systems : Exploring and visualizing data to gain an understanding of it. Verifying data quality, and/or ensuring it via data cleaning
Feature Engineering : Defining the pre-processing or feature engineering to be done on a given dataset. Defining data augmentation pipelines and feature quality systems.
Modelling: Design, develop, evaluate and deploy ML Systems that could be used to solve a given problem and ranking them by their success probability. Defining validation strategies. Analysing the errors of the model and designing strategies to overcome them.
Deployment and Evaluation : Develop and deploy machine learning applications according to the requirements of the production platform. Run machine learning tests and experiments. Evaluate production models for their performance, bias and drift. Focus on building ML Products than ML models.
Job Classification
Industry: Internet Functional Area: Data Science & Analytics, Role Category: Data Science & Machine Learning Role: Data Science & Machine Learning Employement Type: Full time
Education
Under Graduation: Any Graduate Post Graduation: Any Postgraduate