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- Tasks: Build predictive models using AI for market trends and investment opportunities.
- Company: Join a dynamic Private Equity firm focused on portfolio optimisation and risk management.
- Benefits: Enjoy fully remote work with potential for contract extension and competitive rates.
- Why this job: Be at the forefront of AI innovation in finance, driving impactful decisions and growth.
- Qualifications: Experience in machine learning, Python, and Azure is essential; familiarity with Docker and Kubernetes is a plus.
- Other info: This role offers a chance to work with cutting-edge technology in a collaborative environment.
The predicted salary is between 36000 - 60000 £ per year.
Job Title: Machine Learning Engineer
Location: Fully Remote UK
Job Type: 6 Month Contract + chance for extension
Interview Process: Video Interviews held remotely
Rate: DOE Outside IR35
A Private Equity firm are seeking a Machine Learning Engineer to join on an initial 6-month contract to assist in the firms portfolio optimisation, risk management, and predictive modelling. You will be working alongside them through one of our consultancy partners who have recently won the bid for the project.
The end point client operate primarily in an Azure environment hence demonstratable experience in Azure is a must.
Machine Learning Engineer Key Responsibilities:
- Use generative AI to build predictive models for market trends, asset valuation, and investment opportunities.
- Leverage AI algorithms for portfolio optimisation, risk analysis, and asset allocation strategies.
- Automate data extraction and analysis from financial reports, news, and alternative data sources to support investment decisions.
- Use AI to simulate different market conditions and generate optimal exit strategies.
- Help in the adoption of AI tools to optimise operations, reduce costs, and drive growth through automation and data-driven insights.
Machine Learning Engineer Key Skills Required:
- Comprehensive understanding of the full machine learning lifecycle, from development to production.
- Experience deploying machine learning models using frameworks like Scikit-learn, TensorFlow, or PyTorch.
- Proficiency in Python and adherence to software engineering best practices.
- Strong technical expertise in cloud architecture, security, and deployment, with experience in Azure.
- Hands-on experience with containers, particularly Docker and Kubernetes.
- Solid foundation in probability, statistics, and common supervised and unsupervised learning techniques.
If you think this could be an exciting opportunity for you then please apply now!
Contact Detail:
X4 Technology Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Familiarise yourself with Azure, as it's a key requirement for this role. Consider taking an online course or certification to boost your knowledge and demonstrate your commitment to mastering the platform.
✨Tip Number 2
Showcase your experience with generative AI and predictive modelling in your discussions. Prepare specific examples of projects where you've successfully implemented these technologies to solve real-world problems.
✨Tip Number 3
Brush up on your knowledge of machine learning frameworks like Scikit-learn, TensorFlow, and PyTorch. Be ready to discuss how you've used these tools in past projects, especially in deploying models into production.
✨Tip Number 4
Prepare to discuss your understanding of cloud architecture and security, particularly in relation to Azure. Highlight any hands-on experience you have with containers like Docker and Kubernetes, as this will set you apart from other candidates.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, particularly with Azure and the frameworks mentioned like Scikit-learn, TensorFlow, or PyTorch. Use specific examples to demonstrate your skills in predictive modelling and portfolio optimisation.
Craft a Strong Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Mention how your background aligns with their needs, especially your experience with generative AI and automating data extraction. Be sure to convey your understanding of the financial sector.
Showcase Relevant Projects: If you have worked on projects that involved machine learning lifecycle management or cloud architecture, include these in your application. Briefly describe the challenges faced and how you overcame them, focusing on results achieved.
Proofread Your Application: Before submitting, carefully proofread your CV and cover letter for any spelling or grammatical errors. A polished application reflects attention to detail, which is crucial in a technical role like this one.
How to prepare for a job interview at X4 Technology
✨Showcase Your Azure Experience
Since the role requires demonstrable experience in Azure, make sure to highlight any relevant projects or tasks you've completed using this platform. Be prepared to discuss specific tools and services you've used within Azure that relate to machine learning.
✨Discuss Your Machine Learning Lifecycle Knowledge
The interviewers will be keen to understand your grasp of the entire machine learning lifecycle. Prepare to explain how you've taken models from development to production, including any challenges you faced and how you overcame them.
✨Prepare for Technical Questions
Expect technical questions related to frameworks like Scikit-learn, TensorFlow, or PyTorch. Brush up on your knowledge of these tools and be ready to solve problems or discuss algorithms during the interview.
✨Demonstrate Your Problem-Solving Skills
Given the focus on portfolio optimisation and risk management, think of examples where you've used AI to solve complex problems. Be ready to explain your thought process and the impact of your solutions on previous projects.
