Tundra Technical Solutions is looking for a Machine Learning Engineer to design, build, deploy and operate ML pipelines end-to-end in production, working closely with data engineering, cloud platform, and product teams to ship production-quality ML services.
Requirements
- Advanced Python engineering for ML solutions (production code, not notebooks)
- GCP or Azure experience with ML services and cloud infra
- MLOps / DevOps experience (CI/CD, Git workflows, IaC helpful)
- ML pipeline orchestration — Kubeflow and/or Airflow
- Docker + Kubernetes
- ETL / workflow design for ML data pipelines
- Strong Linux/Unix command line experience
Responsibilities
- Build production-quality ML services in Python and deploy them using modern MLOps principles
- Design and orchestrate ML data pipelines using tools such as Kubeflow / Airflow
- Build, maintain and extend CI/CD workflows and Git-based development flows to support iterative model delivery
- Containerize workloads (Docker) and operate services in Kubernetes-based environments
- Work with cloud ML infrastructure (Azure or GCP) to operationalize model training, inference, monitoring and scaling
- Build RESTful endpoints to expose ML models to applications / internal consumers
- Partner cross-functionally to define data needs, instrumentation, and pipeline architecture
Other
- Bachelor's degree or higher in a relevant field (not explicitly mentioned but implied)
- Travel requirements not mentioned
- Visa requirements not mentioned
- Degree requirements not explicitly mentioned but implied
- Soft skills: someone who likes shipping models, automating the ugly parts, and making ML repeatable, reliable, observable, and scalable
