Platform Engineer – MLOps

🔥 12 hours ago

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🏗️ Platform Engineer

👻 Ghost score 25%

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DKSH Portugal, Unipessoal, Lda.

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2014

🍽️ Food & Beverage

📦 Logistics

💼 Consulting

Food & Beverage • Logistics • Consulting

DKSH Portugal, Unipessoal, Lda. is the Portuguese operation of DKSH, a global market‑expansion services provider that helps multinational companies distribute, market and sell specialty chemicals, ingredients (food & beverage, personal care), pharmaceuticals and performance materials. The company offers end‑to‑end B2B services — including distribution, marketing, sales, logistics and regulatory support — to help partners grow their presence in Portugal and connect local markets with global brands.

📋 Description

• 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

🎯 Requirements

• 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)

🏖️ Benefits

• 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