Platform Engineer – MLOps

🕒 Setembro 22

🇺🇸 Estados Unidos – Remoto (EUA)

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

🏗️ Engenheiro de Plataforma

👻 Score fantasma 40%

O que significa: a chance de ninguém estar realmente contratando para esta vaga.Como ler: 60% ou mais significa que achamos que é uma vaga fantasma. Pontuações mais baixas preocupam menos.Como pontuamos: tempo em aberto, republicações, vagas abertas vs tamanho do time, rapidez para fechar vagas, e se o salário está divulgado.Observação: é uma estimativa de dados públicos, não um julgamento da empresa. Vagas reais podem ter pontuação alta, então use como uma dica, não como um fato.

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Opplane

SiteLinkedInTodas as Vagas

11 - 50 funcionários

Fundada em 2018

🤖 Inteligência Artificial

💳 Fintech

☁️ SaaS

🔥 Investimento no último ano

💰 Accelerator/Incubator em 2025-12

Artificial Intelligence • Fintech • SaaS

A Opplane é uma empresa de software que ajuda organizações a aproveitar o valor dos dados por meio de conectividade, governança de dados e inteligência artificial generativa. A empresa se concentra na aplicação de soluções de dados orientadas por IA em ambientes regulados, especialmente nos setores bancário e de fintech, viabilizando conformidade, conectividade segura de dados e casos de uso de IA para serviços financeiros e empresas.

Descrição

• Build and maintain CI/CD pipelines for ML workloads, including testing, packaging, and promotion of models and pipelines • Own ML lifecycle tooling for experiment tracking, model registry, versioning, and promotion gates • Define development-to-production promotion procedures, including validation gates, rollback, and audit trails • Implement model deployment and serving patterns for batch inference and real-time endpoints • Build and maintain observability for ML workloads, covering pipeline health, model and data drift, performance, latency, and cost • Partner with data scientists to productionize notebooks and experiments into governed, reliable, repeatable pipelines • Administer and evolve Databricks workspaces, Unity Catalog metastores, and catalogs with appropriate access controls • Support AWS infrastructure underpinning the Lakehouse, particularly IAM, networking, S3, and related platform services • Apply least-privilege access and Unity Catalog governance across data and ML assets • Monitor and tune Databricks jobs and compute for cost and performance • Maintain architecture documentation and runbooks for deployment, troubleshooting, and production support • Partner with data engineering and data science teams to define and maintain platform standards, automation, and documentation • Translate ML workload requirements into reliable, governed, reusable platform capabilities

🎯 Requisitos

• 5+ years of relevant experience in platform, DevOps, MLOps, ML engineering, or related engineering roles • Hands-on experience with the ML lifecycle: experiment tracking, model registry, versioning, and deployment (MLflow or equivalent) • Experience building CI/CD pipelines using Git-based workflows for ML or data workloads • Working experience with Databricks platform capabilities, including workspaces, Unity Catalog, compute, jobs/workflows, permissions, and environment configuration • Solid Python scripting for automation and pipeline tooling • Ability to work closely with data scientists and data engineers to translate ML requirements into reliable platform capabilities • Fluency in English (written and spoken) • AWS experience, particularly IAM, networking, S3, and related platform services (nice to have) • Infrastructure as Code experience with Terraform or Terragrunt (nice to have) • Familiarity with Databricks Asset Bundles, Delta Live Tables, or Databricks Workflows (nice to have) • Exposure to feature stores or feature platforms such as Chalk, or real-time model serving (nice to have) • Experience with notebook-based data science environments such as Domino Data Lab (nice to have) • Experience with on-premises or hybrid Kubernetes environments and Git-based deployment workflows using GitLab (nice to have) • Databricks ML Associate or AWS certifications (nice to have) • Observability tooling such as CloudWatch and Prometheus/Grafana, and cost-optimization practices (nice to have)

🏖️ Benefícios

• Permanent full-time employment • Remote work • Global and multicultural environment with diverse perspectives and global collaboration • Startup energy in a fast-moving, impact-driven environment • Ownership mindset where engineers own what they build • Collaborative, friendly, open, curious, and supportive culture

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