NanoNetsSenior Software Engineer (Fullstack)
Nanonets is transforming the way businesses work. Our AI platform takes the manual, messy, time consuming work — that bog down industries like finance, healthcare, supply chain, and more — and turns them into seamless, automated processes. What once took hours of human effort now takes seconds with Nanonets. Our client footprint spans across 34% of Fortune 500 enabling businesses across various industries to unlock the potential of AI in automating their business processes.
More than 10,000 businesses trust Nanonets because we don’t just promise efficiency — we deliver it with unmatched accuracy, seamless integrations.
In 2024, we raised a $29M Series B led by Accel with continued backing from Elevation Capital and YCombinator, fueling our mission to reshape entire industries through intelligent automation. With revenues tripling year over year and a rapidly scaling global team, we’re not just imagining the future of work — we’re building it.
Read about the release here:
We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity.
About the Role
As a Fullstack engineer, you should be comfortable with both backend (Preferably Golang) and frontend (React, Next). You will be primarily working on shipping new features like new integrations, enabling more machine learning API’s, and building complex workflows. You will also work on optimizing response times, building features that will scale to 100’s of millions of documents we process every month.
What we expect from you -
- B.Tech/B.E from a reputed educational institute.
- Agility in shipping features
- Good at code design and architecture
- Very good communication
- Backend experience - Preferably golang, python
- Databases - Understanding of data modeling for Nosql preferably cassandra
- Strong fundamentals in OOP design patterns
- Basic understanding of Devops
- Curiosity and Willingness to learn new things while solving a challenging problem
- Learning and incorporating best practices in software development, security and design/architecture
Some of the interesting features we have shipped in backend
- Compile python code into C which could be imported into golang and then shipped as binary for on premise systems
- Autoscale GPU dependent services with kubernetes with a custom metric
- Displaying machine learning metrics in simplified ways to end users so they can act based on those metrics
- Building large number and variety of integrations with relatively generic interface like salesforce, quickbooks, RPA's, external databases
- Process large number of files in highly distributed manner in golang
Some of the interesting things we have shipped in frontend
- Ability for users to annotate documents so AI can learn which fields to extract
- Displaying machine learning metrics in simplified ways to end users so they can act based on those metrics
- Letting users build complex visual workflows around our API in our product.
- Let users visualize complex ML metrics in a very simple and intuitive way
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