Sr . MLOps Engineer @ Eagleview

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

• 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