Job Description

About Simplismart
A bit about our product - Simplismart is an MLOps platform with 3 major suites:

• Training suite: Assemble and train any model, including LLMs, vision, audio, tabular, and tree models.
• Deployment suite: Most companies fail to make models production-ready. Our proprietary model deployment suite is 6x faster than HuggingFace’s enterprise suite and 12x faster than replicate.ai. Users can easily deploy (auto-scale) models trained on Simplismart (more optimised), import any model from HuggingFace, or even a Pytorch/Tensorflow artefact: Tensorflow, Pytorch, ONNX, JAX.
• Observability suite: Monitor model health, including load, latency, uptime, data drift, and concept drift.

Position Overview

As a Cloud Engineer, you will contribute to building a highly available, global, multi-cloud PaaS platform using open-source technologies to support Simplismart’s rapid growth. This system encompasses diverse environments (Kubernetes, VMs, bare metal compute) and provides a cohesive and reliable abstraction for running AI workloads. You will be able to work with cutting-edge technologies and solve complex problems.

To be successful in this role, you need to be deeply technical, possess strong communication and collaboration skills, and have experience in infrastructure-as-code. Proficiency with tools like Terraform and Ansible and strong software development fundamentals is essential. Additionally, you should have a good understanding of systems knowledge and troubleshooting abilities.

Requirements

• 5+ years of experience writing high-performance, well-tested, production-quality code and platform engineering.
• Proficiency in at least one backend programming language (Python desired; C++ is a plus)
• Demonstrated experience with high-performance or distributed cloud microservices architectures.
• Ideally, you should have experience building and operating globally using multiple cloud providers such as AWS, Azure, or GCP.
• A good understanding of low-level operating systems concepts, including multi-threading, memory management, networking and storage, performance, and scale.
• Pragmatic, methodical, well-organized, detail-oriented, and self-starting.
• Experience with Kubernetes, containerization, Terraform and Ansible.
• Experience with Pytorch or Tensorflow is a plus. (not necessary)
• Knowledge of GPU programming, NCCL and CUDA is a plus.

Responsibilities

• Designing the high-level architecture of the MLOps platform from the ground up.
• Handling formalisation of diverse GPU-based workloads.
• Developing a robust internal system for continuous deployment of various services and modules in diverse environments.
• Create frameworks for reliable and fault tolerant systems for mission-critical workloads.

Skills And Attributes

• Deep technical expertise.
• Strong communication and collaboration skills.
• Experience in infrastructure-as-code (Terraform, Ansible).
• Strong software development fundamentals.
• Good systems knowledge and troubleshooting abilities.
• Ability to work independently and as part of a team.
• Proactive and self-motivated.