Job Description

Nanonets has a vision to help computers see the world starting with reading and understanding documents. Machine Learning (ML) is no longer a futuristic concept—it's a present-day powerhouse transforming the business landscape. Nanonets is at the forefront of this transformation, offering innovative ML solutions designed to make document related processes faster than ever before. 

From automating data extraction processes to enhancing reconciliation, our solutions are designed to revolutionize workflows, optimize operations, and unlock untapped potential for our clients. Our client footprint spans across brands such as Toyota, Boston Scientific, Bill.com and Entergy to name a few enabling businesses across a myriad of industries to unlock the potential of their visual and textual data

Here's a quick 1-minute intro video.

We recently announced a series B round of $29 million in funding by Accel and are backed by the likes of existing investors including Elevation Capital & YCombinator. This infusion of capital underscores our commitment to driving innovation and expanding our reach in delivering cutting-edge AI solutions to businesses worldwide.

Read about the release here: 

https://www.forbes.com/sites/davidprosser/2024/03/12/why-enterprises-are-learning-to-love-nanonets-automation/?sh=6d79ec8f3ca1

https://techcrunch.com/2024/03/12/nanonets-funding-accel-india/amp/

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.

Responsibilities -

• Manage and optimize Kubernetes clusters (EKS) and Karpenter for efficient resource allocation and scaling.
• Improve performance and cost efficiency of GPU-heavy deep learning workloads.
• Maintain and enhance CI/CD pipelines using Jenkins and ArgoCD for seamless deployments.
• Optimize and manage AWS services (EKS, EC2, RDS, S3, OpenSearch, IAM, SQS, etc.) for reliable infrastructure.
• Enhance observability and incident response with Prometheus, ELK Stack, and Grafana to reduce downtime.

Skills Required

• Expertise in Kubernetes (EKS, Karpenter) and AWS services (mandatory).
• Experience in ML Ops for managing GPU workloads and deploying ML/LLM models (mandatory).
• Strong cloud cost optimization skills for resource-efficient scaling (preferred).
• Proficiency in CI/CD tools like Jenkins and ArgoCD (preferred).
• Strong troubleshooting, problem-solving, and observability skills with monitoring tools (preferred).

Our Tech Stack

• Kubernetes for deployments
• Jenkins for CI/CD
• AWS - EKS, RDS, S3, EC2, Lambda, Cloudfront, ECR etc
• Cassandra DB and RDS
• Prometheus for Monitoring
• Golang for API and other microservices
• Python for Machine learning (Tensorflow, Pytorch)
• React for frontend
• ELK for logging