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Design, Train, and Deploy a Machine Learning Model into Production
Job summary
A social media platform is looking for a senior Machine Learning Engineer to develop and deploy predictive models based on user engagement data. This role involves data preprocessing, model development, deployment using MLOps practices, and monitoring model performance. Ideal candidates should have 6-8 years of experience and proficiency in Python, machine learning libraries, and cloud platforms like AWS or Azure. Competitive compensation is offered for this freelance position.
Qualifications
- At least 6-8 years of experience in Machine Learning Engineering or a related field.
- Proven track record of deploying ML models in a live production environment.
Responsibilities
- Clean and prepare data for model training.
- Build, train, and evaluate a robust ML model.
- Deploy the model as a microservice or an API.
- Implement monitoring to track model performance and data drift in production.
Skills
Sep 28, 2025 - Senior
$3,450.00 Fixed
We are a social media platform. We have a backlog of valuable data but lack the infrastructure and expertise to use it for predictive modeling.
We have a wealth of data but no infrastructure to train and deploy machine learning models. We need to develop a model that can predict user engagement based on their behavior and content consumption. The challenge is not just building the model but deploying it reliably and scalably in a production environment.
The inability to leverage our data for predictive modeling is a significant missed opportunity. We cannot personalize the user experience, predict churn, or recommend relevant content, which hinders our growth and competitiveness.
We need a senior Machine Learning Engineer to own this project from end-to-end. The freelancer will be responsible for:
- Data Preprocessing: Cleaning and preparing the data.
- Model Development: Building, training, and evaluating a robust ML model.
- Deployment: Using MLOps best practices to deploy the model as a microservice or an API.
- Monitoring: Implementing monitoring to track the model's performance and data drift in production.
At least 6-8 years of experience in Machine Learning Engineering or a related field. The freelancer must have a proven track record of deploying ML models in a live production environment.
- Expertise in Python and machine learning libraries (e.g., Scikit-learn, TensorFlow, PyTorch).
- Experience with cloud platforms (AWS, GCP, or Azure) for ML.
- Strong knowledge of MLOps principles and tools (e.g., Kubeflow, SageMaker, MLflow).
- Ability to work with large datasets and distributed systems.
Please provide a case study or documentation for a previous MLOps project you executed, detailing the model, the deployment pipeline, and the performance metrics in production.
The freelancer should submit a detailed technical proposal outlining their approach to model development and a robust MLOps plan for deployment and monitoring. The proposal must also include a risk assessment.
* The salary benchmark is based on the target salaries of market leaders in their relevant sectors. It is intended to serve as a guide to help Premium Members assess open positions and to help in salary negotiations. The salary benchmark is not provided directly by the company, which could be significantly higher or lower.