


autonomous world.Rich data in large volumes is getting collected at the edge (outside a datacenter) in use cases like autonomous vehicles, smart manufacturing, satellite imagery, smart retail, smart agriculture etc.These datasets are characterized by being unstructured
(images/videos), large size (Petabytes per month), distributed (across edge, on-prem and
cloud) and form the input for training AI models to get to higher degrees of automation.
Akridata is engaged with building products that solve these unique challenges and be at the forefront of this edge data revolution.
The company is backed by prominent VCs and has it’s entire software engineering team
based out of India and provides ample opportunities for from-scratch design and
development.
Role:
This role is an individual contributor role with key responsibilities in developing web server
backends for Akridata management plane software that provides a ‘single pane of glass’ for users to manage assets, specify and monitor large volume data pipelines at scale involving 10s of petabytes of data.
This role involves:
1. Working with tech leads and the rest of the team on the feature design activities and
picking appropriate tools and techniques for implementation.
2. Be a hands-on developer able to independently make correct implement choices, follow
sound development practices to ensure an enterprise grade application.
3. Guide and mentor junior team members.
What we are looking for:
1. A Bachelor’s or Master’s degree in computer science with strong CS fundamentals and
problem solving.
2. 5+ years of hands-on experience with software development with 3+ years on web
backend development.
3. A good understanding of backend application interactions with relational databases like
MySQL, Postgres etc
4. Knowledge of web server development frameworks preferably on Python.
5. Enthusiastic to work in a dynamic, fast paced startup environment.
Good to have:
1. Hands-on experience with designing database schema and implementing and debugging SQL queries for optimal performance for large datasets
2. Experience working with applications deployed on Kubernetes clusters.
3. Experience with working on a product from early stages of it’s development typically in a
startup environment.

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