MLOps Engineer
Skills
Cloud ComputingInfrastructure As CodeMachine LearningNeural NetworksVertex AIFastAPIFlask
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Bachelor's or Master's degree in a quantitative field or equivalent
- 5+ years of experience as an ML engineer
- Strong understanding of core data science principles and productionizing research code
- Hands-on experience with GCP and ML model deployment, monitoring, and maintenance
- Solid Python development experience with Flask or FastAPI, OOP, and unit testing
- Strong software engineering best practices and experience with TDD
- Experience with infrastructure as code tools such as Terraform
- Hands-on experience with cloud platforms such as GCP AWS or Azure
- Familiarity with Docker and deployment orchestration
- Experience with CI/CD tools and Git-based workflows
- Understanding of API monitoring and logging
- Strong problem-solving skills and ability to work independently
- Familiarity with Agile methodologies
- Ability to communicate processes tools and technical decisions clearly
Nice to have
- Creative
- Proactive
- Logical
- Innovative
- Results driven
- Fast paced
Day to day
- Lead a newly formed ML Engineering team and build production-grade ML infrastructure in GCP and Azure.
- Develop and maintain APIs, deployment pipelines, and cloud services for real-time and batch model serving.
- Collaborate with data scientists, platform engineers, and developers to automate the ML lifecycle and improve operational excellence.