MLOps

• Knowledge of programming languages (Python & GO preferred). Python is a must

• 2-3 Years experience in Dev-Ops development

• Excellent team working and communication skills

• Excellent organizational and time management skills with the ability to work

on multiple projects in parallel

• Experience with all aspects of software architecture, including security,

scalability, monitoring, computing, and throughput

• Container and orchestration experience with Docker and Kubernetes (AKS,

EKS, GKE)

• Working experience with cloud providers – AWS, GCP, Azure

• Awareness of DevOps and Agile principles

• Strong problem-solving skills

• Good attention to detail

• A Start-up spirit

Good to have:

• Familiarity with modern DevOps practices and ability to establish CI/CD

processes

• Deploying production-grade AI/deep learning solutions in a cloud

environment

• Experience with Peer-to-Peer networking technologies such as WebRTC

and client-server networking

• Experience with : ArgoCD, CircleCI, Jenkins, Github Actions, JFrog,

Customize, Helm, Terraform

Job Responsibilities

As our DevOps engineer you will be part of leading and working closely with software

developers and researchers.

Responsibilities include:

• Maintain and support Python-based pipelines running on KubeFlow

• Deploy, manage and administrate existing / new CICD tools

• Building configuration and automation tools to support the application

delivery process as well as ML pipelines

• Orchestrate the delivery pipeline of development environments and

eventually production

• Work on ways to automate and improve development and release process

Department/Project Description:

Great opportunity to join the small tight knit team of a well-funded startup taking

part in the design and development of the backbone infrastructure and delivery

pipeline of our first software product.

Our client is building an ML-based solution used in the Sports industry. ML algorithms can extract valuable information to the footage taken during sports events. Whether it is a basketball match or a football match you can calculate the number of goals, find interesting moments, and create beautiful highlights automatically.

Details:

We build an iOS (iPhone) application for camera usage and video captures with real-time object detection as well as uploading to AWS videos for further processing. We need to work with the camera frames and timestamps to have good enough information for the ML processing. We also have to build the interface for the mask creation in real-time. The solution will detect players and ball in real-time.

The data is being analyzed on the AWS cloud.