Data Intelligence Platform Engineer
Platform engineer building and scaling an internal ML/MLOps platform used by 300+ data scientists across 60+ teams at Philip Morris International. Day to day involves implementing Databricks MLOps (environments, CI/CD, monitoring), maintaining the AWS SageMaker platform, and automating infrastructure with Python and Terraform.
- Compensation
- PLN 17.6K / mo
- Location
- Buenos Aires, Argentina
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 4+ years
- Category
- Devops
- Posted
Skills
Must have
- 4-6 years of experience building production infrastructure for ML or data workloads
- Deep knowledge of Databricks or AWS SageMaker (Databricks strongly preferred)
- Strong AWS fundamentals (IAM, networking, compute) beyond console clicking
- Confident Python automation skills
- Terraform / Infrastructure-as-Code without relying on basic syntax searches
- Understand the full ML lifecycle: training, versioning, deployment, monitoring
- Ability to design and ship solutions independently
- Delivered non-trivial CI/CD pipelines to production
- Experience with workflow orchestration and CI/CD tools such as Apache Airflow and Jenkins
- Hands-on experience with Kubernetes (EKS)
Nice to have
- Apache Airflow
- OAuth / Amazon Cognito, API Gateway
- Monitoring tools such as Prometheus, Grafana, New Relic
- Cloud cost optimisation experience
- GitOps tooling (FluxCD, ArgoCD)
Full description
About The Role
You will join a team responsible for
About The Role
You will join a team responsible for building and scaling the ML platform used by 300+ data scientists and ML engineers across 60+ teams. Your work will directly impact how machine learning models are deployed to production — reliably, securely, and at scale.
You’ll spend most of your time implementing and standardising Databricks MLOps (environments, CI/CD pipelines, model monitoring) while also contributing to the evolution of a battle‑tested AWS SageMaker platform.
You Day-to-day
- Design, build, and scale a production-grade ML / MLOps platform
- Implement Databricks MLOps (environments, CI/CD pipelines, monitoring)
- Maintain and improve the AWS SageMaker ML platform
- Automate infrastructure and workflows using Python and Infrastructure‑as‑Code (Terraform)
- Design solutions for a large-scale multi-tenant architecture (60+ teams, 3 AWS accounts)
- Partner closely with data scientists and ML engineers to enable production model deployments
- Ensure platform reliability, security, observability, and cost efficiency
Who We’re Looking For
- Have 4–6 years of experience building production infrastructure for ML or data workloads
- Know Databricks or AWS SageMaker deeply (Databricks experience is strongly preferred)
- Are strong in AWS fundamentals (IAM, networking, compute - beyond console clicking)
- Can confidently write Python automation and Terraform / IaC without relying on basic syntax searches
- Understand the full ML lifecycle: training, versioning, deployment, monitoring
- Can design and ship solutions independently, without constant hand‑holding
- Have delivered non‑trivial CI/CD pipelines to production
- Experience with workflow orchestration and CI/CD tools such as Apache Airflow and Jenkins
- Have hands‑on experience with Kubernetes (EKS)
Nice To Have
- Apache Airflow
- OAuth / Amazon Cognito, API Gateway
- Monitoring tools such as Prometheus, Grafana, New Relic
- Cloud cost optimisation experience
- GitOps tooling (FluxCD, ArgoCD)
Come and join us!
For Poland: In this position you will earn no less that 17 600 PLN gross monthly