Educational Background :

• B.Tech/M.Tech, Computer Science, PhD (optional)

Required Skills:

• 2 years of minimum working experience as a DevOps engineer.
• 1 year of minimum experience working closely with an ML team.
• Experience in creating, deploying, and maintaining centralized KubeFlow infrastructure on top of one or multiple kubernetes clusters
• Proficiency in creating CI/CD pipelines for microservice based architectures using Jenkins
• Proficiency in python. The candidate should be able to write production grade code in python.
• Proficiency in Git, docker and docker-compose
• Experience with kubernetes. The candidate should be comfortable with kubectl and helm.
• Experience working with tools in AWS ecosystem - particularly with Infrastructure as Code (IaC), CloudFormation, IAM, API Gateway, Lambda, Load Balancers, dynamodb, RDS, ECR, ECS and EKS.

Desired Skills:

• AWS certified developer/solution architect
• Experience in workflow orchestrations tools like Apache Airflow, Prefect, MetaFlow, Luigi etc.
• Prior experience/familiarity with machine learning frameworks e.g., PyTorch, TensorFlow, ONNX etc.
• Experience/Familiarity with model serving in ML and working with frameworks like TensorFlow Serving, TorchServe, KFServing, Seldon, BentoML etc.
• Experience working with computer vision technologies is a bonus

Responsibilities:

• Work closely with the ML team to plan, build, maintain, and improve an end- to-end MLOps platform on top of KubeFlow for research, model training, logging and model serving
• Work closely with the ML team, integration team(s) and the cloud administrators to deploy and integrate ML services into a wide range of products
• Build complex container-based workflows that include multiple data and model components for machine learning applications
• Designing and implementing CI/CD pipelines with git, Jenkins, and AWS for ML research-based projects
• Continuously improve latency, concurrency, horizontal scaling, and overall API performance for deployed applications by introducing new tools/technologies crafted for ML
• Understanding and analyzing the current development and deployment specs for the ML team and propose scopes of improvement and solutions to improve the same