At a Glance
- Tasks: Build and productionise ML pipelines, optimising model performance in a fast-paced environment.
- Company: Join a major asset management firm with a focus on data-driven initiatives.
- Benefits: Competitive contract rate, hybrid working, and potential for extension.
- Other info: Ideal for engineers passionate about the full ML lifecycle and robust systems.
- Why this job: Make a real impact by deploying cutting-edge machine learning models in production.
- Qualifications: Strong Python skills and experience with MLOps and ML pipelines.
The predicted salary is between 60000 - 80000 € per year.
DW Search are partnering on a high-impact contract opportunity within a fast-paced, data-driven environment supporting portfolio company initiatives for a major asset management firm. This role sits at the intersection of Data Engineering and Machine Learning Engineering, with a strong focus on building and productionising end-to-end ML pipelines. You will be working on real-world applications of neural networks, enabling scalable feature engineering, model training, and inference in production.
Key Responsibilities
- Build and productionise feature engineering pipelines for ML models (neural networks)
- Develop and manage training and inference workflows at scale
- Deploy and monitor machine learning models in production environments
- Collaborate with data scientists and engineering teams to optimise model performance and reliability
- Contribute to best practices across MLOps and pipeline orchestration
Required Experience
- Strong Python fluency
- Proven experience building production-grade data and ML pipelines
- Solid understanding of MLOps principles
- Experience working with machine learning models
- Some project experience with Databricks
- Cloud experience - open to Azure/ GCP/ AWS
This is a strong fit for engineers who operate across the full ML lifecycle and enjoy taking models from development into robust, production systems. This is an initial 6 month contract, outside IR35 with high likelihood of extension. A rate guidance is provided but the focus is on the right person so open to contractors outside of this range.
Senior Machine Learning Engineer employer: DW Search
Join a leading asset management firm in London as a Senior Machine Learning Engineer, where you will thrive in a dynamic, data-driven environment that champions innovation and collaboration. With a strong emphasis on employee growth, you will have the opportunity to work on cutting-edge ML projects while enjoying a hybrid working model that promotes work-life balance. Our culture fosters continuous learning and best practices in MLOps, making it an ideal place for engineers eager to advance their careers in machine learning.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Machine Learning Engineer
✨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 Senior Machine Learning Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving ML pipelines and MLOps. 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 brushing up on your technical knowledge and problem-solving skills. Practice common ML scenarios and be ready to discuss your experience with Python and cloud platforms like Azure or AWS.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might just be the perfect fit for you. Plus, it’s a great way to get noticed by hiring managers who are looking for talent like yours.
We think you need these skills to ace Senior Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with building and productionising ML pipelines. We want to see your Python fluency and any relevant MLOps principles you've applied in past projects.
Showcase Your Projects:Include specific examples of your work with neural networks and data engineering. We love seeing real-world applications, so don’t hold back on the details that demonstrate your skills!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Tell us why you’re excited about this role and how your experience aligns with our needs. Make it personal and engaging – we want to get to know you!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity!
How to prepare for a job interview at DW Search
✨Know Your ML Pipelines
Make sure you can talk confidently about your experience with building and productionising ML pipelines. Be ready to discuss specific projects where you've developed feature engineering workflows or deployed models in production. This will show that you understand the end-to-end process.
✨Brush Up on MLOps Principles
Since this role has a strong focus on MLOps, it’s crucial to demonstrate your understanding of its principles. Prepare examples of how you've optimised model performance and reliability in past projects, and be ready to discuss best practices in pipeline orchestration.
✨Showcase Your Python Skills
As strong Python fluency is required, be prepared to discuss your coding experience in detail. You might even want to bring along some code snippets or projects that highlight your ability to build production-grade data and ML pipelines.
✨Familiarise Yourself with Cloud Platforms
Since the job mentions experience with cloud platforms like Azure, GCP, or AWS, make sure you’re up to speed on these technologies. Be ready to discuss any relevant projects you've worked on in the cloud, as this will demonstrate your versatility and readiness for the role.
