What is a Machine Learning Operations Engineer (MLOps)?
- MLOps is the discipline of integrating ML workloads into release management, CI/CD, and operations.
- MLOps requires the integration of software development, operations, data engineering, and data science.
In this position, you will be dealing with machine learning proposals, client liaising, configuring client environments, ETL workflows, pipelines, data lakes, HPC workloads, serverless workflows and ML development environments.
- Assist with ML proposals, including defining solutions architectures for ML/HPC/Data Lake workloads based on best practices
- Estimate cloud usage costs and identify operational cost control mechanisms per project
- Migrate on-premises workloads to edge and cloud for clients
- Configure client environments, including pipelines (i.e. CI/CD, Data, Inference) using IaC (Infrastructure as Code) tools (i.e. Terraform) and programming languages (i.e. Python/Go/Bash).
- Configure data lakes for storing data (i.e. raw, transformed, training/validation/test sets and inference results)
- Develop Glue ETL workflows using Python scripts for data transformations
- Develop Lambda/Step functions as part of serverless workflows for triggering pipelines and transforming datasets using NodeJS/Python.
- Knowledge with Linux operating systems including:
• Ubuntu/AWS Linux CIS
• SSH connections and tunnelling
• Bash scripts.
- Proficient in a scripting language (such as Python, NodeJS, Golang, Bash etc)
- Basic understanding of CI/CD pipelines
- Basic understanding of IaC (i.e. CloudFormation, Terraform)
- Proficient in version control systems (i.e. GitHub, BitBucket)
- Proficient in Jupyter Notebooks and JupyterLab
- Basic understanding of ML algorithms and when to use which.
- Proficient in Python packages such as Pandas/NumPy
If this sounds like you and you're keen to explore the AI industry further, apply!