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For Companies At a Glance
- Tasks: Design, train, and deploy a machine learning model to enhance user engagement.
- Company: Join a dynamic social media platform ready to leverage data for growth.
- Benefits: Competitive pay, flexible work options, and opportunities for professional development.
- Why this job: Make a real impact by transforming data into actionable insights and improving user experience.
- Qualifications: 6-8 years in Machine Learning Engineering with proven deployment experience.
- Other info: Exciting opportunity to lead a project from concept to production in a fast-paced environment.
The predicted salary is between 54000 - 90000 £ per year.
Business Overview: 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.
The Challenge: 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 behaviour 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 personalise the user experience, predict churn, or recommend relevant content, which hinders our growth and competitiveness.
Proposed Method: 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.
Required Experience: 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.
Required Expertise:
- 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.
Sample Work Required: 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.
Freelancer Proposal: 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.
Design, Train, and Deploy a Machine Learning Model into Production in London employer: Featmate
Contact Detail:
Featmate Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Design, Train, and Deploy a Machine Learning Model into Production in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry, attend meetups, and engage in online forums. The more people you know, the better your chances of landing that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your previous projects, especially those related to machine learning. This will give potential employers a taste of what you can do and set you apart from the competition.
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios specific to machine learning. We recommend doing mock interviews with friends or using online platforms to get comfortable with the process.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace Design, Train, and Deploy a Machine Learning Model into Production in London
Some tips for your application 🫡
Show Off Your Experience: Make sure to highlight your 6-8 years of experience in Machine Learning Engineering. We want to see your journey, so include specific projects where you've designed, trained, and deployed models, especially in production environments.
Be Clear and Concise: When writing your proposal, keep it straightforward. We appreciate clarity, so avoid jargon unless necessary. Break down your approach to model development and MLOps in a way that’s easy for us to follow.
Include a Case Study: Don’t forget to attach a case study or documentation of a previous MLOps project. We’re keen to see the details of your model, deployment pipeline, and how you monitored performance in production.
Apply Through Our Website: Remember, the best way to get your application in front of us is through our website. It streamlines the process and ensures we receive all the necessary information to consider your proposal.
How to prepare for a job interview at Featmate
✨Know Your Stuff
Make sure you brush up on your machine learning concepts and tools. Be ready to discuss your experience with Python, Scikit-learn, TensorFlow, and MLOps principles. The more you can demonstrate your expertise, the better!
✨Showcase Your Projects
Prepare a case study or two from your previous work that highlights your ability to design, train, and deploy ML models. Focus on the challenges you faced, how you overcame them, and the impact your work had on the project.
✨Understand Their Needs
Familiarise yourself with the company’s goals and challenges. They’re looking to leverage data for predictive modelling, so think about how your skills can help them personalise user experiences and improve engagement.
✨Ask Smart Questions
Prepare insightful questions about their current infrastructure and future plans for ML deployment. This shows your genuine interest in the role and helps you gauge if the company is the right fit for you.
