Primary Skills:Speech Recognition Apache Airflow Data modeling Artificial Intelligence Hadoop Kubernetes Machine Learning Deep Learning SQL
Secondary Skills:Hadoop Data Science Docker Spark Computer Vision Python
Job Location:
Bangalore/Bengaluru
Posted Date:
385 days ago
Job Description
The Flipkart Science team is looking for a passionate data engineer with machine learning experience to help build and test customer-facing, ML-powered systems. You will work with data scientists and engineers on a variety of online and offline machine learning use cases including:
Data modeling to support machine learning model training and inference workflows and pipelines.
Build data pipelines to feed machine learning models for real-time and large-scale offline use cases.
Work closely with data scientists to scale model training and explore new data sources and model features.
Minimum Qualifications
Bachelors Degree in computer science
5+ years of industry experience in software development, data engineering, ML, business intelligence, data science, or a related field
Strong core computer science and problem solving skills
Fundamentals of Data Science and proven track record ini building ML systems.
Experience in model tracking and assessment principles with expertise in ML model lifecycle platforms like MLFlow.
Expertise in Python and general data science frameworks and libraries
Knowledge of data management fundamentals and data storage principles
Expert-level SQL skills and production experience with one or more major SQL databases
Ability to write good quality, maintainable, explainable, production level ETL
Good communication, collaboration, analytical and problem solving skills
Development expertise in Unix-based environments.
Preferred Qualifications
Expertise in building and consuming RESTful services
Experience with Spark, Hadoop, Apache Airflow and similar applications.
Experience with Docker, Kubernetes, or any other PaaS systems
Instrumentation of Continuous Integration and Delivery (CI/CD) pipelines
Knowledge of software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations
Infrastructure deployment and automation with any public cloud provider(AWS/Azure/GCP...)