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Hands on experience with Azure Machine Learning, Databricks, and Azure DevOps.
Strong knowledge of MLOps, including data drift, model drift, and model performance monitoring using MLflow.
Expert in DevOps practices, including CI CD pipelines and automation.
In-depth experience with Kubernetes and containerized applications.
Familiarity with data engineering concepts and ETL processes.
Experience managing data science projects and collaborating with cross-functional teams.
Proficient in Python, Bash, or PowerShell for scripting and automation.
Hands on experience with public cloud platforms, preferably Azure.
Strong background in managing AI/ML environments and optimizing performance using MLflow.
ears of experience 7-10
The MLOps Engineer is responsible for ensuring that ML engineers can scale the machine learning models across the entire organization. They are responsible for building and maintaining the infrastructure that will allow this scaling to occur.
LLMS Knowledge is required.
They also ensure that data scientists can use these models without having to worry about how they are built or maintained.
An MLOps Engineer is a person who designs, builds, and runs machine learning systems at scale.
They are responsible for maintaining the infrastructure that supports the models and algorithms that power the products of their company, including:
Monitoring the performance of these systems
Identifying ways to improve their performance
Investigating issues when they arise
They also monitor the performance of your models, and they need to be able to troubleshoot any errors or bugs that may occur.
In addition to these responsibilities, an MLOps Engineer might be tasked with improving your model's accuracy by tweaking its parameters or updating the data it uses for training.
MLOps Engineer Skills
MLOps Engineers are the bridge between machine learning and operations. They ensure that the machine learning models are being deployed and updated correctly, not causing any problems.
MLOps needs to have the following skills:
Have experience working in an agile environment
Be a problem solver and quick learner
Understand the importance of continuous learning and personal development
Demonstrate knowledge of at least one programming language, preferably Python or Java.
They also need to interpret the results of their models, which means they need to be able to read data on a fundamental level and understand how it relates to the problem being solved by the model.
The technical skills you need to be an MLOps engineer are:
Data Science
Statistical modeling
Python/R programming
Machine learning ML
SQL
Linux/Unix shell scripting
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