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- Tasks: Build and maintain data science infrastructure, automate pipelines, and collaborate with data scientists.
- Company: Join a dynamic data science team in a forward-thinking company.
- Benefits: Competitive salary, inclusive workplace, and opportunities for professional growth.
- Why this job: Make an impact by transitioning models from research to production using cutting-edge tech.
- Qualifications: Proficient in Python, Azure, and Databricks with strong software engineering skills.
- Other info: Embrace diversity and enjoy a supportive work environment.
The predicted salary is between 60000 - 70000 £ per year.
This position is for an experienced Machine Learning Engineer to join a newly established data science team. The primary focus is on building and maintaining the infrastructure to support the full data science lifecycle from data ingestion to model deployment, monitoring, and upgrades within Azure and Databricks environments. The engineer will work closely with data scientists in a collaborative, cross-functional setting, helping transition models from research into production.
Key Responsibilities:
- Own and develop deployment frameworks for data science services.
- Ownership of the deployment framework for all data science services.
- Oversight of how data will flow into the data science life cycle from the wider business data warehouse.
- Oversight of the automation of the data science life cycle (dataset build, training, evaluation, deployment, monitoring) when we move to production.
- Automate the data science pipeline (data prep to deployment).
- Collaborate with cross-functional teams to ensure smooth productionisation of models.
- Write clean, production-ready Python code.
- Apply software engineering best practices, CI/CD, TDD.
Required Skills:
- Proficiency in Python, Databricks, and Azure.
- Experience with deployment tools (e.g., AKS, managed endpoints).
- Strong software engineering background (CI/CD, VCS, TDD).
- Ability to integrate ML into business workflows.
Desirable:
- Background in quantitative disciplines (math, stats, physics).
- Experience in finance, insurance, or ecommerce.
- Familiarity with ML frameworks like TensorFlow, XGBoost, and SKLearn.
If this sounds like something you are interested in, please get in contact: thomas.deakin@spgresourcing.com
SPG Resourcing is an equal opportunities employer and is committed to fostering an inclusive workplace which values and benefits from the diversity of the workforce we hire. We offer reasonable accommodation at every stage of the application and interview process.
Machine Learning Engineer employer: SPG Resourcing
Contact Detail:
SPG Resourcing Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at local meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that Machine Learning Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Python, Databricks, and Azure. We want to see how you’ve tackled real-world problems and how you can bring that expertise to our team.
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills and understanding deployment frameworks. We recommend practicing common ML scenarios and being ready to discuss your thought process during problem-solving.
✨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 Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with Python, Databricks, and Azure, and don’t forget to showcase any relevant projects or achievements that align with the job description.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about machine learning and how your skills can contribute to our data science team. Keep it concise but impactful!
Showcase Your Projects: If you've worked on any cool projects related to ML, make sure to mention them! Whether it's a personal project or something from a previous job, we love seeing practical applications of your skills.
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep everything organised and ensures your application gets the attention it deserves!
How to prepare for a job interview at SPG Resourcing
✨Know Your Tech Stack
Make sure you’re well-versed in Python, Databricks, and Azure. Brush up on deployment tools like AKS and managed endpoints. Being able to discuss your experience with these technologies confidently will show that you're ready to hit the ground running.
✨Showcase Your Collaboration Skills
Since this role involves working closely with data scientists, be prepared to discuss examples of how you've collaborated in cross-functional teams. Highlight any projects where you helped transition models from research to production, as this will demonstrate your ability to work effectively in a team setting.
✨Demonstrate Your Problem-Solving Abilities
Prepare to talk about specific challenges you've faced in automating data science pipelines or deploying models. Use the STAR method (Situation, Task, Action, Result) to structure your answers, showcasing your analytical skills and how you overcame obstacles.
✨Emphasise Best Practices
Familiarise yourself with software engineering best practices like CI/CD and TDD. Be ready to discuss how you've applied these principles in your previous roles, as this will highlight your commitment to writing clean, production-ready code.
