MLOps Instructor DevOps Machine Learning OperationsRemote
MLOps Instructor DevOps Machine Learning OperationsRemote
Posted on :
23-05-2025
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1 Vacancy
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Job Description
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:
- Strong knowledge of Python (NumPy Pandas Scikit-Learn TensorFlow PyTorch).
Experience in ML model development training and evaluation.
- MLOps & CI/CD Pipelines:
- Expertise in MLflow Kubeflow TFX and DVC for experiment tracking and model versioning.
- Hands-on experience with CI/CD for ML using GitHub Actions Jenkins and Docker.
Infrastructure & Model Deployment:
- Strong knowledge of Kubernetes KServe TensorFlow Serving and Apache Airflow for automating ML workflows.
- Experience in deploying models on-premise and in cloud environments (AWS SageMaker Azure ML GCP AI Platform).
Monitoring & Automation:
- Proficiency in Prometheus Grafana and Evidently AI for monitoring ML models in production.
- Understanding of model drift detection retraining automation and feedback loops.
Cloud & DevOps for ML:
- Experience in AWS (S3 EC2 Lambda SageMaker) Azure ML and GCP AI solutions.
- Knowledge of cost optimization serverless inferencing and multi-cloud strategies.
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
Employment Type
Full Time
Key Skills
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