FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Platform Engineer – MLOps
DKSH Portugal, Unipessoal, Lda.. Build and maintain CI/CD pipelines for ML workloads, including testing, packaging, and promotion of models and pipelines .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and maintaining CI/CD pipelines for ML workloads, with a strong focus on ML lifecycle tooling, observability, and collaboration with data science and engineering teams. Proficient in managing Databricks environments and AWS infrastructure to ensure reliable and governed ML platform capabilities.
Highest-signal resume keywords
CI/CD Pipeline DevelopmentML Lifecycle ManagementDatabricks Platform ExpertisePython Scripting for AutomationAWS Infrastructure Management
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
CI/CD Pipeline DevelopmentML Lifecycle ManagementPython ScriptingDatabricks AdministrationAWS IAMGit-based WorkflowsInfrastructure as CodeModel DeploymentExperiment TrackingVersioning
Soft Skills
Collaboration with Data ScientistsCommunication Skills
Tools & Technologies
DatabricksAWSTerraformGitLabCloudWatchPrometheusGrafana
Certifications & Qualifications
Databricks ML AssociateAWS Certifications
Industry Keywords
MLOpsMachine LearningData EngineeringObservabilityModel Serving
Tech Stack
Tools & technologiesAWSGrafanaKubernetesPrometheusPythonTerraformUnity
About the role
Key responsibilities & impact- 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 with validation gates, rollback, and audit trails
- Implement model deployment and serving patterns for batch inference and real-time endpoints
- Build and maintain observability for pipeline health, model/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
What you’ll need- 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
- Hands-on 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
Comp & perks- Permanent full-time employment
- Hybrid work arrangement in Lisboa
- Global and multicultural environment
- Global collaboration across US, Portugal, India and Singapore offices
- Startup energy in a fast-moving, impact-driven environment
- Ownership mindset—engineers own what they build
- Collaborative, friendly, open, curious, and supportive culture