Swiggy Careers 2024 | Machine Learning Engineer - I | Remote

Job Profile: Machine Learning Engineer – I

Location: Bangalore | Karnataka (Remote)

Required Experience: 0-1

ABOUT THE TEAM & ROLE:

Swiggy is India’s leading on-demand delivery platform with a tech-first approach to logistics and a solution-first approach to consumer demands. With a presence in 500 cities across India, partnerships with hundreds of thousands of restaurants, an employee base of over 5000, and a 2 lakh+ strong independent fleet of Delivery Executives, we deliver unparalleled convenience driven by continuous innovation.

Built on the back of robust ML technology and fuelled by terabytes of data processed every day, Swiggy offers a fast, seamless and reliable delivery experience for millions of customers across India.

From starting out as a hyperlocal food delivery service in 2014 to becoming a logistics hub of excellence today, our capabilities result not only in lightning-fast delivery for customers but also in a productive and fulfilling experience for our employees.

With Swiggy’s New Supply and the recent launches of Swiggy Instamart, Swiggy Genie, and Guiltfree, we are consistently making waves in the market, while continually growing the opportunities we offer our people.

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Position Overview:

We are looking for a highly skilled Machine Learning Engineer to join our team. The ideal candidate will have strong knowledge of ML fundamentals and algorithms, OOPs concepts and ETL pipeline as well as extensive experience with Spark and Python programming and business backward thinking.

Skills :

Spark, SQL, Python programming, API integration, ML fundamentals and algorithms, Cloud provider Stack: AWS/Azure/GCP , kubernetes, Scala programming, Tensorflow, Pytorch, ONXX, Langchain (Good to have)

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What will you get to do here?

• Build and deploy ML models on various platforms. We use a combination of AWS, in-house ML platform, directly integrating with services of other engineering teams
• Work with the data science team to enable robust decision-making in terms of thinking about scale, latency, throughput requirements. Create tools/systems to speed-up ML lifecycle
• Play a significant role in enabling adoption of sophisticated algorithms and data mining strategies
• Contribute to the development of data-related products and services

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