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- Tasks: Build real-time data pipelines and develop advanced ML models for innovative solutions.
- Company: Join a dynamic team focused on cutting-edge technology and impactful projects.
- Benefits: Enjoy flexible work options, competitive salary, and opportunities for professional growth.
- Why this job: Be part of a collaborative culture that values innovation and creativity in tech.
- Qualifications: 2+ years in ML or data engineering; strong skills in Python, SQL, and big data tools required.
- Other info: Work with cross-functional teams to align technical solutions with business goals.
The predicted salary is between 43200 - 72000 £ per year.
We’re seeking a Machine Learning Engineer with strong data engineering expertise to build scalable real-time data pipelines and develop advanced ML models. This role involves collaborating with cross-functional teams to deliver innovative solutions.
Key Responsibilities:
– Data Engineering: Build and maintain real-time data pipelines and ETL workflows. Ensure data quality and integrity.
– Machine Learning: Design, train, and optimize ML models for fraud prevention and personalization.
– MLOps: Deploy, monitor, and maintain ML models in production using tools like Docker, Kubernetes, and cloud platforms (AWS/GCP).
– Data Analysis: Preprocess data, identify trends, and derive insights using clustering, classification, and anomaly detection techniques.
– Collaboration: Work with product managers, engineers, and data scientists to align technical solutions with business goals.
What We’re Looking For:
– Experience: 2+ years in ML, data engineering, or related fields, with a focus on fraud detection or personalization.
Technical Skills:
– Proficiency in Python, SQL, and big data tools (e.g., Kafka, Spark).
– Strong knowledge of ML frameworks (TensorFlow, PyTorch).
– Experience with MLOps and cloud technologies (AWS/GCP).
– Analytical Skills: Strong understanding of statistical methods and data visualization tools (e.g., Pandas, Matplotlib).
– Mindset: Adaptable, innovative, and comfortable in a fast-paced environment.
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Machine Learning Engineer employer: Morson Talent
Contact Detail:
Morson Talent Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Familiarize yourself with the latest trends in machine learning and data engineering. Follow industry leaders on platforms like LinkedIn or Twitter to stay updated on best practices and innovative solutions that can enhance your skill set.
✨Tip Number 2
Engage in online communities and forums related to machine learning and data engineering. Participating in discussions or contributing to open-source projects can help you build a network and showcase your expertise to potential employers.
✨Tip Number 3
Consider creating a portfolio of projects that demonstrate your skills in building real-time data pipelines and developing ML models. Highlight any specific experiences related to fraud detection or personalization to make your application stand out.
✨Tip Number 4
Prepare for technical interviews by practicing coding challenges and system design problems relevant to machine learning and data engineering. Use platforms like LeetCode or HackerRank to sharpen your problem-solving skills.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Highlight Relevant Experience: Make sure to emphasize your experience in machine learning and data engineering. Include specific projects or roles where you built data pipelines, developed ML models, or worked with fraud detection and personalization.
Showcase Technical Skills: Clearly list your technical skills, especially your proficiency in Python, SQL, and big data tools like Kafka and Spark. Mention any experience with ML frameworks such as TensorFlow or PyTorch, and highlight your familiarity with MLOps and cloud platforms like AWS or GCP.
Demonstrate Collaboration: Provide examples of how you've collaborated with cross-functional teams. Highlight instances where you worked with product managers, engineers, or data scientists to align technical solutions with business goals.
Tailor Your Application: Customize your application to reflect the specific requirements mentioned in the job description. Use keywords from the listing to ensure your application resonates with the hiring team and showcases your fit for the role.
How to prepare for a job interview at Morson Talent
✨Showcase Your Data Engineering Skills
Be prepared to discuss your experience with building and maintaining real-time data pipelines. Highlight specific projects where you ensured data quality and integrity, as this is crucial for the role.
✨Demonstrate Your ML Expertise
Discuss your experience in designing, training, and optimizing ML models, especially in the context of fraud prevention and personalization. Bring examples of how you've applied clustering, classification, or anomaly detection techniques.
✨Familiarize Yourself with MLOps Tools
Make sure you can talk about your experience with deploying and monitoring ML models using tools like Docker and Kubernetes. Mention any cloud platforms you've worked with, particularly AWS or GCP.
✨Emphasize Collaboration Experience
Since this role involves working with cross-functional teams, be ready to share examples of how you've collaborated with product managers, engineers, and data scientists to align technical solutions with business goals.
