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MLOps Engineer

Work set-up:

Full Remote

Contract:

Full time

Experience:

Mid-level (2-5 years)

Work from:

Brazil

Offer summary

Qualifications:

Experience with Kubernetes for orchestrating ML workloads., Proficiency in Python scripting and automation., Familiarity with ML workflows including data processing, training, and deployment., Strong Linux command-line skills and experience with ML metadata systems like MLflow or W&B..

Key responsibilities:

Build and maintain scalable ML pipelines for data processing, training, and evaluation.
Orchestrate batch and training jobs in Kubernetes, handling retries and resource constraints.
Collaborate with researchers to productionize experiments into robust workflows.
Implement model serving endpoints and set up monitoring, logging, and KPI tracking.

CloudWalk, Inc. SME https://www.cloudwalk.io

201 - 500 Employees

See all jobs

Job description

Who we are

CloudWalk is a fintech company reimagining the future of financial services. We are building intelligent infrastructure powered by AI, blockchain, and thoughtful design. Our products serve millions of entrepreneurs across Brazil and the US every day, helping them grow with tools that are fast, fair, and built for how business actually works. Learn more atcloudwalk.io .

Who We’re Looking For

Were looking for an MLOps Engineer to help us build ML infrastructure that scales dynamically from dozens to thousands of GPUs, reliably and efficiently.

You’ll be part of the AI R&D team, working closely with researchers and engineers to design systems for training, evaluating, and monitoring machine learning models at scale. This isn’t a research position, but your work will directly support researchers running largescale experiments. You’ll help build faulttolerant pipelines that preserve progress even when things break (like OOMs), and ensure model development flows can iterate with confidence.

Our current focus is on largescale, noninteractive workloads: batch training, datasetwide model evaluation, and metricdriven improvement loops. That said, the infrastructure you build may later support interactive tools and APIs.

Youll be contributing to system design under the guidance of senior ML researchers and infra engineers, your role is to bring modern tooling and practical engineering to a demanding, GPUheavy environment.As a Machine Learning Engineer, your mission is to design and deploy intelligent systems that power core product experiences. Youll transform rich data into models that drive automation, personalization, and smart decisionmaking at scale. This role blends engineering and applied science, focused on building robust, adaptive ML systems that evolve continuously and make a tangible impact.

Responsibilities:Build and maintain ML pipelines for data processing, training, evaluation, and model deployment.
Orchestrate batch and training jobs in Kubernetes, handling retries, failures, and resource constraints.
Design systems that scale dynamically from small GPU jobs to thousands of GPUs ondemand.
Collaborate with researchers to productionize their experiments into reproducible, robust workflows.
Implement model serving endpoints (RESTgRPC) and integrate with internal tooling.
Set up monitoring, logging, and KPI tracking for ML pipelines and compute jobs.
Automate CICD and infra provisioning for ML workloads.
Manage experiment tracking, model versioning, and metadata with tools like MLflow or W&B.
Support model serving infrastructure that may be used by internal UIs or tools in the future.

Required Skills:
Kubernetes: Strong experience orchestrating jobs, not just deploying services. You should be confident in managing training workloads, GPU scheduling, job retries, and Helmbased deployments.
Python: Comfortable writing scripts and services that glue systems together. You don’t need to be a fullstack dev, but notebooks won’t cut it. Automation is the word here.
ML Workflows: Familiarity with data preprocessing, training, evaluation, and deployment pipelines.
Model Serving: Ability to expose models via FastAPI, TorchServe, or equivalent serving stacks.
Linux: Strong CLI skills, you should know your way around debugging computeheavy jobs.
Experience with ML metadata systems (MLflow, W&B, Neptune).
Know how to work side by side with AI assistants and agents.
Ability to communicate and debate in English and Portuguese.

NicetoHave:
Experience with orchestration tools (Airflow, Argo Workflows, Prefect).
Fluency in cloud environments (GCP, AWS, Azure).
Ability to write lean and customized Dockerfiles and Helm charts that run smoothly.
Exposure to distributed training frameworks (Ray, Horovod, Dask).
Deep understanding of GPU scheduling and tuning in Kubernetes environments.
Experience supporting LLM workloads or inference systems powering internal tools.

What You’ll Need to Succeed:
Curiosity about how things fail and how to make them not.
Strong debugging chops, especially in distributed, resourceconstrained environments.
A practical mindset, you know when to patch and when to fix.
Ability to collaborate across ML, research, and backend teams.
Ownership: you care about keeping systems reliable, scalable, and clean.

Recruiting process outline:
Online assessment: an online test to evaluate your theoretical skills and logical reasoning.
Essay: a technical project for you to share your thoughts
Technical interview and Essay presentation.
Cultural interview.

If you are not willing to take an online quiz, do not apply.
Diversity and inclusion:We believe in social inclusion, respect, and appreciation of all people. We promote a welcoming work environment, where each CloudWalker can be authentic, regardless of gender, ethnicity, race, religion, sexuality, mobility, disability, or education.