Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Join our team to operationalise machine learning models and collaborate with data scientists.
- Company: We are a leading provider of complex business cloud solutions in the financial sector.
- Benefits: Enjoy flexible working options, competitive salary, and opportunities for professional growth.
- Why this job: Be part of an innovative culture that values creativity and impact in the tech world.
- Qualifications: Bachelor's degree and 5+ years in business analysis, preferably in banking or finance.
- Other info: Experience with Databricks MLFlow and CI/CD pipelines is a plus!
The predicted salary is between 43200 - 72000 £ per year.
Job Description:
We are seeking an experienced Machine Learning Engineer with expertise in big programmes and has contributed to the delivery of complex business cloud solutions. The ideal candidate will have a strong background in Machine Learning engineering and an expert in operationalising models in the Databricks MLFlow environment (chosen MLOps Platform).
Responsibilities:
- Collaborate with Data Scientists and operationalize the model with auditing enabled, ensure the run can be reproduced if needed.
- Implement Databricks best practices in building and maintaining economic modelling (Machine Learning) pipelines.
- Ensure the models are modular. Ensure the model is source-controlled with agreed release numbering.
- Extract any hard-coded elements and parameterise them so that the model execution can be controlled through input parameters.
- Ensure the model input parameters are version-controlled and logged to the model execution runs for auditability.
- Ensure model metrics are logged to the model runs.
- Ensure model logging, monitoring, and alerting to make sure any failure points are captured, monitored and alerted for support team to investigate or re-run of the model involves running of multiple experiments and choosing the best model (champion challenger) based on the accuracy/error rate of each experiment, ensure this is done in an automated manner.
- Ensure the model is triggered to run as per the defined schedule. If the process involves executing multiple models feeding each other to produce the final business outcome, orchestrate them to run based on the defined dependencies.
- Define and Maintain the ML Frameworks (Python, R & MATLAB templates) with any common reusable code that might emerge as part of model developments/operationalisation for future
- models to benefit.
- Where applicable, capture data drift, concept drift, model performance degradation signals and ensure model retrain.
- Implement CI/CD pipelines for ML models and automate the deployment.
- Maintain relevant documentation.
Requirements:
- Bachelor\’s degree in a relevant field.
- Minimum of 5 years of experience as a business analyst, with a focus on capturing and documenting business requirements and business processes.
- Strong understanding of banking and financial industry practices and regulations.
- Solid knowledge of Data Management process, data analysis and modelling techniques.
- Experience in monetary policy analysis (nice to have)
- Experience in time series database analysis
- Familiarity with business intelligence tools and concepts.
- Strong analytical and problem-solving skills.
- Experience in managing software development lifecycles within Agile frameworks to ensure timely and high-quality delivery.
- Excellent communication and collaboration skills.
- Ability to adapt to changing requirements and priorities in a fast-paced environment.
Machine Learning Engineer employer: Qualient Technology Solutions UK Limited
Contact Detail:
Qualient Technology Solutions UK Limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Familiarise yourself with Databricks and MLFlow, as these are crucial for the role. Consider taking online courses or tutorials to deepen your understanding of how to operationalise models within this environment.
✨Tip Number 2
Network with professionals in the machine learning field, especially those who have experience in banking and financial services. Attend industry meetups or webinars to connect with potential colleagues and learn about their experiences.
✨Tip Number 3
Showcase your problem-solving skills by discussing past projects where you successfully implemented CI/CD pipelines for ML models. Be prepared to explain your thought process and the impact of your work during interviews.
✨Tip Number 4
Stay updated on the latest trends in machine learning and data management, particularly in the context of the banking sector. This knowledge will not only help you in interviews but also demonstrate your commitment to the field.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in Machine Learning engineering, particularly with Databricks MLFlow. Emphasise any relevant projects where you've operationalised models and collaborated with Data Scientists.
Craft a Strong Cover Letter: In your cover letter, explain why you're passionate about Machine Learning and how your background aligns with the responsibilities outlined in the job description. Mention specific examples of your work with CI/CD pipelines and model monitoring.
Showcase Relevant Skills: Clearly list your technical skills related to Python, R, MATLAB, and any experience with data management processes. Highlight your understanding of banking and financial industry practices, as this is crucial for the role.
Prepare for Technical Questions: Anticipate technical questions related to model operationalisation, data drift, and performance metrics. Be ready to discuss your approach to building and maintaining ML pipelines and how you ensure model accuracy and reliability.
How to prepare for a job interview at Qualient Technology Solutions UK Limited
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Databricks MLFlow and how you've operationalised machine learning models in previous roles. Highlight specific projects where you implemented best practices in building and maintaining ML pipelines.
✨Demonstrate Collaboration Experience
Since the role involves working closely with Data Scientists, share examples of how you've collaborated in the past. Discuss any challenges faced and how you overcame them to ensure successful model delivery.
✨Understand the Business Context
Familiarise yourself with the banking and financial industry practices, as well as any relevant regulations. Being able to relate your technical skills to business outcomes will show that you understand the bigger picture.
✨Prepare for Problem-Solving Questions
Expect questions that assess your analytical and problem-solving skills. Be ready to discuss how you've handled data drift, model performance degradation, or any other challenges in your previous projects, and how you approached finding solutions.
