MLOps Instructor DevOps Machine Learning OperationsRemote

Are you passionate about education and technology Ready to be part of a dynamic startup that s transforming how people learn

About Skillfyme

Skillfyme is a fast-growing platform dedicated to helping individuals upskill in tech fields like DevOps Cloud and Data Science. Our courses which include real-world projects and certification opportunities empower professionals to advance in their careers and succeed in competitive markets. As a part of our team youll have the opportunity to make a real impact in the lives of our learners guiding them through our programs and ensuring a seamless experience from enrollment to completion.

Job Overview MLOps Instructor (Remote)

Skillfyme is seeking a knowledgeable and passionate MLOps Instructor to join our team in a remote capacity. In this role you will be responsible for delivering high-quality training on MLOps principles CI / CD for ML model deployment and cloud-based ML workflows . You will guide learners through hands-on projects real-world case studies and best practices ensuring they gain practical expertise in building deploying and monitoring ML models at scale .

This role is ideal for an experienced MLOps professional who is passionate about teaching mentoring and helping learners bridge the gap between machine learning and production deployment . If you have a strong background in MLOps tools cloud computing (AWS Azure) Kubernetes and model monitoring we d love to have you on board!

Key Responsibilities (KRA) MLOps Instructor

• Deliver MLOps Training Sessions :
• Conduct live and recorded sessions covering MLOps workflows CI / CD for ML data versioning model training automation and deployment .
• Explain concepts like model drift detection hyperparameter tuning and feedback loops with hands-on exercises.
• Hands-On Implementation & Practical Training :
• Guide learners in setting up ML pipelines using Apache Airflow Kubernetes and DVC .
• Teach distributed model training on Kubernetes and cloud-based MLOps implementation (AWS SageMaker Azure ML) .
• Model Deployment & Serving :
• Train students in deploying models using KServe TensorFlow Serving and batch inferencing architectures .
• Explain real-time vs batch inferencing use cases and their deployment strategies.
• Monitoring & Automation :
• Implement model monitoring dashboards using Prometheus & Grafana .
• Teach best practices for detecting model drift and automating retraining workflows using Evidently AI .
• Capstone Project & Real-World Applications :
• Supervise an end-to-end MLOps project where students integrate data preprocessing training deployment and monitoring .
• Provide technical guidance and feedback to ensure industry-aligned learning.
• Cloud & Infrastructure Management :
• Deliver training on AWS & Azure MLOps pipelines cost optimization strategies and multi-cloud deployment.
• Help learners implement serverless inferencing using AWS Lambda and Azure Functions .
• Student Mentorship & Support :
• Resolve learner queries related to MLOps implementation troubleshooting and best practices .

Skills Required for MLOps Instructor :

Machine Learning & Python :

Experience in ML model development training and evaluation.

Infrastructure & Model Deployment :

Monitoring & Automation :

Cloud & DevOps for ML :

azure ml,ci / cd pipelines,training,numpy,jenkins,ml model development,dvc,gcp ai platform,github actions,pandas,mlflow,tfx,apache airflow,pytorch,python,evidently ai,cloud,ml,mlops,aws sagemaker,scikit-learn,kubeflow,kubernetes,aws,docker,prometheus,grafana,ci / cd,machine learning,gcp ai solutions,tensorflow,kserve

Key Skills

Industrial Maintenance,Machining,Mechanical Knowledge,CNC,Precision Measuring Instruments,Schematics,Maintenance,Hydraulics,Plastics Injection Molding,Programmable Logic Controllers,Manufacturing,Troubleshooting

Employment Type : Full Time

Experience : years

Vacancy : 1