At a Glance
- Tasks: Transform AI research into scalable solutions and build robust frameworks for intelligent systems.
- Company: Join a forward-thinking tech company focused on ethical AI development.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Dynamic work environment with a focus on innovation and ethical AI practices.
- Why this job: Make a real impact by developing cutting-edge AI technologies that shape the future.
- Qualifications: Experience in machine learning, data processing, and strong collaboration skills.
The predicted salary is between 60000 - 80000 € per year.
We are looking for a Machine Learning Engineer who excels at turning AI research into scalable, production-grade reality. You will be responsible for the 'heavy lifting' building the frameworks that allow our AI models to reason, the pipelines that feed them, and the infrastructure that ensures they are fast, ethical, and cost-efficient. You will bridge the gap between Data Science prototypes and enterprise-scale deployment.
Key Responsibilities
- AI Model design and build: Work closely with data scientists and business to design and implement AI algorithms, frameworks and architectures.
- AI model Data Preprocessing: Design, build, and maintain robust ETL/ELT pipelines to ingest, transform, and load data from various sources.
- AI model Feature Engineering: Integrate structured and unstructured data from internal and external systems into centralized data platforms.
- Performance Tuning of AI models: Optimize data workflows and queries for performance, scalability, and cost-efficiency.
- Building Agentic Systems: Developing intelligent AI agents that can reason, plan, and execute tasks autonomously using LLMs and other tools.
- LLM application Development: LLM fine-tuning adapting pretrained LLMs for specific tasks using techniques like parameter efficient fine-tuning (PEFT) (e.g., LoRA, QLoRA). Implementing Retrieval-Augmented Generation pipelines to enhance the knowledge and accuracy of LLMs. Utilizing vector databases for efficient storage and retrieval of embeddings generated by LLMs. Crafting effective prompts to elicit desired responses from LLMs. Connecting LLMs and generative models with other systems and APIs to create comprehensive solutions.
- Communicate findings: Collaborate extensively with data scientists and business during model development and deployment. Maintain updated documentation with details of all aspects of model development lifecycle.
- Responsible AI: Build AI systems which are trustworthy and beneficial considering ethical principles such as fairness, transparency, accountability, privacy and reliability. Implement quantifiable metrics detecting bias, explainability and adherence to regulatory compliance.
ML Engineer in City of London employer: Randstad Digital
At Randstad Technologies, we pride ourselves on fostering a dynamic and inclusive work culture that empowers our employees to innovate and excel. As a Machine Learning Engineer, you will have access to cutting-edge technology and resources, alongside opportunities for professional growth and development in a collaborative environment. Our commitment to ethical AI practices ensures that your work contributes to meaningful advancements in the field, all while being part of a supportive team located in a vibrant tech hub.
StudySmarter Expert Advice🤫
We think this is how you could land ML Engineer in City of London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with ML professionals on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to AI model design and performance tuning. Having tangible examples of your work can really set you apart during interviews.
✨Tip Number 3
Prepare for technical interviews by brushing up on your knowledge of ETL/ELT pipelines and feature engineering. Practise coding challenges and be ready to discuss how you've tackled real-world problems in your past roles.
✨Tip Number 4
Don’t forget to apply through our website! We love seeing applications from passionate candidates who are eager to turn AI research into reality. Plus, it’s a great way to ensure your application gets noticed!
We think you need these skills to ace ML Engineer in City of London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the ML Engineer role. Highlight your experience with AI model design, data preprocessing, and performance tuning. We want to see how your skills align with our needs!
Showcase Your Projects:Include any relevant projects or experiences that demonstrate your ability to turn AI research into production-grade reality. We love seeing practical applications of your skills, so don’t hold back!
Be Clear and Concise:When writing your cover letter, keep it clear and concise. Explain why you’re passionate about AI and how you can contribute to our mission at StudySmarter. We appreciate straightforward communication!
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to see what you bring to the table!
How to prepare for a job interview at Randstad Digital
✨Know Your AI Models Inside Out
Make sure you’re well-versed in the AI models and algorithms relevant to the role. Brush up on your understanding of frameworks, architectures, and the latest trends in machine learning. Being able to discuss specific projects or experiences where you've implemented these models will show your expertise.
✨Showcase Your Data Pipeline Skills
Be prepared to talk about your experience with ETL/ELT processes. Highlight any specific tools or technologies you've used to build robust data pipelines. Discuss how you've handled data preprocessing and feature engineering, as this is crucial for the role.
✨Demonstrate Ethical AI Practices
Since responsible AI is a key focus, be ready to discuss how you ensure fairness, transparency, and accountability in your work. Share examples of how you've implemented metrics to detect bias or ensure compliance with regulations in your previous projects.
✨Communicate Effectively with Stakeholders
Collaboration is key in this role, so practice articulating your thoughts clearly. Prepare to explain complex technical concepts in a way that non-technical stakeholders can understand. This will demonstrate your ability to bridge the gap between data science and business needs.
