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- Tasks: Design and deploy cutting-edge machine learning models and infrastructure.
- Company: Join a forward-thinking team focused on predictive modelling and innovation.
- Benefits: Enjoy fully remote work and flexible hours for a better work-life balance.
- Why this job: Be at the forefront of technology, making impactful contributions in a dynamic environment.
- Qualifications: Proven experience with ML models, AWS SageMaker, and time series data is essential.
- Other info: Work with advanced tools like MLflow and Docker in a collaborative setting.
The predicted salary is between 43200 - 72000 £ per year.
Permanent Opportunity
Fully Remote Working
Job Overview:
We're seeking a forward-thinking Machine Learning Engineer to drive our predictive modeling capabilities.
Key Responsibilities
- Design and deploy advanced machine learning models using AWS SageMaker
- Architect robust ML infrastructure and production pipelines
- Implement model management strategies with MLflow/Metaflow
- Develop sophisticated time series forecasting solutions
- Leverage containerization technologies to ensure scalable, efficient deployments
Required Expertise
- Proven experience deploying ML models in production environments
- Experience having used ML pipeline orchestration tools, like Airflow, Metaflow, Prefect, Dagster, BentoML - Essential
- Advanced skills in AWS SageMaker, cloud-based ML infrastructure
- Expert-level understanding of time series data modeling
- Proficiency in containerization (Docker)
- Strong capabilities with AWS CDK (or Terraform) and workflow management tools
Tech Stack: AWS SageMaker, MLflow, Metaflow, Docker, Time Series Modeling
Senior Machine Learning Engineer (M/F/D) employer: InterQuest Group
Contact Detail:
InterQuest Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer (M/F/D)
✨Tip Number 1
Familiarise yourself with AWS SageMaker and its features. Since this role heavily relies on deploying machine learning models using SageMaker, having hands-on experience or projects showcasing your skills can set you apart.
✨Tip Number 2
Brush up on your knowledge of time series forecasting techniques. Given the emphasis on developing sophisticated solutions in this area, being able to discuss relevant methodologies and past experiences will demonstrate your expertise.
✨Tip Number 3
Get comfortable with ML pipeline orchestration tools like Airflow or Metaflow. Being able to articulate how you've used these tools in previous projects will show that you're ready to hit the ground running.
✨Tip Number 4
Showcase your experience with containerization technologies, particularly Docker. Discussing specific instances where you've implemented Docker for scalable deployments can highlight your technical capabilities.
We think you need these skills to ace Senior Machine Learning Engineer (M/F/D)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with AWS SageMaker, MLflow, and time series forecasting. Use specific examples of projects where you've deployed machine learning models in production.
Craft a Compelling Cover Letter: In your cover letter, express your passion for machine learning and how your skills align with the company's needs. Mention your experience with containerization technologies and orchestration tools, as these are essential for the role.
Showcase Relevant Projects: If you have worked on relevant projects, consider including a portfolio or links to your work. Highlight any advanced machine learning models you've designed and deployed, especially those using AWS services.
Proofread Your Application: Before submitting, carefully proofread your application for any errors or typos. A polished application reflects your attention to detail, which is crucial for a Senior Machine Learning Engineer.
How to prepare for a job interview at InterQuest Group
✨Showcase Your Technical Skills
Be prepared to discuss your experience with AWS SageMaker and other tools mentioned in the job description. Bring examples of projects where you've successfully deployed machine learning models, especially focusing on time series forecasting.
✨Demonstrate Problem-Solving Abilities
Expect scenario-based questions that assess your problem-solving skills. Think about challenges you've faced in previous roles and how you overcame them, particularly in relation to ML infrastructure and production pipelines.
✨Familiarise Yourself with the Tech Stack
Make sure you understand the technologies listed in the job description, such as MLflow, Metaflow, and Docker. Being able to discuss these tools confidently will show your preparedness and enthusiasm for the role.
✨Prepare Questions for the Interviewers
Have insightful questions ready about the company's approach to machine learning and their future projects. This not only shows your interest but also helps you gauge if the company aligns with your career goals.
