Assignment Description:
We are looking for a Senior Machine Learning Engineer to join our dynamic team.
What you will work on:
- Design, build, and maintain scalable ML pipelines and workflows on Google Cloud Platform (GCP).
- Implement model training, deployment, monitoring, and orchestration using Vertex AI.
- Leverage GCP services such as BigQuery, Dataflow, Cloud Storage, Cloud Functions, and Cloud Run for data processing, analytics, and model serving.
- Apply Infrastructure-as-Code (Terraform/Deployment Manager) and CI/CD practices (Cloud Build, GitHub Actions) for reliable, automated deployments.
- Ensure secure, compliant ML workflows by managing IAM roles, service accounts, and authentication methods.
- Provide knowledge transfer and technical guidance to the team, explaining best practices and scalable architecture choices.
- Monitor, log, and optimize ML systems for performance, reliability, and cost efficiency.
What you bring:
- Proven experience with GCP services and ML engineering workflows.
- Strong Python programming skills with hands-on experience in building and debugging ML pipelines.
- Deep practical knowledge of:
- Vertex AI: training, deployment, monitoring, orchestration
- BigQuery: scalable analytics and warehousing
- Cloud Storage: raw and processed data storage
- Dataflow: preprocessing and pipeline orchestration
- Compute (CPUs/GPUs/TPUs): high-performance model training
- Cloud Functions / Cloud Run: model serving and automation
- IAM: access control for secure ML workflows
- Monitoring & Logging: observability of ML systems
- Artifact Registry / Container Registry: container and artifact storage
- IaC: Terraform / Deployment Manager for GCP resources
- CI/CD: Cloud Build, GitHub Actions integration
- Auth & Access: OAuth 2.0, IAM roles, service accounts
- Excellent communication skills with the ability to explain technical concepts clearly.
- A collaborative, team-oriented mindset with a strong sense of ownership.
If this sounds interesting, please send your CV to cv@veritaz.se
