Platform Engineer – MLOps

🕒 il y a 1 mois

🇺🇸 États-Unis – Télétravail

⏰ Temps Plein

🟡 Intermédiaire

🟠 Senior

🏗️ Ingénieur Plateforme

👻 Score fantôme 38%

Ce que ça veut dire : la probabilité que personne ne recrute vraiment pour cette offre.Comment le lire : 60 % ou plus, on pense que c'est une offre fantôme. Un score plus bas est moins inquiétant.Comment on la calcule : ancienneté, republications, postes ouverts vs taille de l'équipe, rapidité de clôture, et si le salaire est indiqué.À noter : une estimation à partir de données publiques, pas un jugement sur l'entreprise. De vraies offres peuvent avoir un score élevé, alors prenez-le comme un indice, pas comme un fait.

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DKSH Portugal, Unipessoal, Lda.

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11 - 50 employés

Fondée en 2014

🍽️ Alimentation et boissons

📦 Logistique

💼 Conseil

Food & Beverage • Logistics • Consulting

DKSH Portugal, Unipessoal, Lda. est la filiale portugaise de DKSH, un prestataire mondial de services d'expansion de marché qui aide les entreprises multinationales à distribuer, commercialiser et vendre des produits chimiques spécialisés, des ingrédients (alimentaires et boissons, soins personnels), des produits pharmaceutiques et des matériaux de performance. La société offre des services B2B de bout en bout — y compris la distribution, le marketing, les ventes, la logistique et le soutien réglementaire — pour aider ses partenaires à accroître leur présence au Portugal et connecter les marchés locaux avec des marques mondiales.

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

🎯 Exigences

• 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)

🏖️ Avantages

• 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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