At a Glance
- Tasks: Design and build AI workflows while ensuring scalable and secure models.
- Company: Join a forward-thinking tech company at the forefront of AI innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on responsible AI practices.
- Why this job: Be part of groundbreaking AI projects that shape the future of technology.
- Qualifications: Expertise in Python, SQL, and experience with MLOps tools.
The predicted salary is between 60000 - 80000 £ per year.
Currently looking for a ML Engineer. A hands-on role bridging AI research and production. You will design agentic workflows and LLM architectures while building the MLOps "factory" to ensure models are scalable, secure, and monitorable.
Core Responsibilities
- AI Design: Build autonomous agents using LangGraph or CrewAI and implement GraphRAG/Agentic RAG pipelines.
- Model Tuning: Fine-tune LLMs using LoRA/QLoRA and optimize inference with vLLM or Quantization.
- Data Engineering: Manage Vector DBs (Pinecone, Milvus) and architect ETL/ELT pipelines via Airflow or AWS.
- MLOps & DevOps: Deploy via Docker/Kubernetes using CI/CD (GitHub Actions, ArgoCD).
- Lifecycle & Safety: Track drift with W&B or MLflow and ensure Responsible AI (bias detection, red teaming).
Technical Profile
- Languages: Python (Expert), SQL, and Bash.
- Frameworks: PyTorch, Transformers, LlamaIndex, and LangChain.
- Infrastructure: Kubernetes, Helm, and Cloud (AWS/GCP/Azure).
Key Skills: RAG optimization, Multi-agent orchestration, and TDD.
Machine Learning Engineer in London employer: Randstad Digital
As a leading innovator in the AI space, our company offers Machine Learning Engineers a dynamic and collaborative work environment where creativity and technical expertise thrive. Located in a vibrant tech hub, we provide competitive benefits, continuous learning opportunities, and a culture that values diversity and inclusion, ensuring that every team member can grow and contribute meaningfully to cutting-edge projects.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with other ML enthusiasts. 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 involving LLMs or MLOps. 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 Python and SQL skills. Practice coding challenges and be ready to discuss your approach to model tuning and data engineering tasks.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented ML Engineers, and applying directly can give you a better chance of landing that dream role.
We think you need these skills to ace Machine Learning Engineer in London
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your experience with Python, SQL, and any relevant frameworks like PyTorch or Transformers. We want to see how your skills align with the role, so don’t hold back!
Tailor Your Application:Take a moment to customise your application for this specific role. Mention your experience with MLOps, Docker, and Kubernetes, and how you’ve tackled similar challenges in the past. It’ll make you stand out!
Be Clear and Concise:When writing your application, keep it straightforward. Use clear language and avoid jargon unless it’s relevant. We appreciate a well-structured application that gets straight to the point.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. We can’t wait to hear from you!
How to prepare for a job interview at Randstad Digital
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description. Brush up on Python, SQL, and Bash, and be ready to discuss your experience with frameworks like PyTorch and Transformers. Being able to talk confidently about your hands-on experience will impress the interviewers.
✨Showcase Your MLOps Knowledge
Since this role involves building an MLOps factory, be prepared to discuss your experience with Docker, Kubernetes, and CI/CD pipelines. Have examples ready of how you've deployed models in the past and any challenges you faced. This will demonstrate your practical understanding of the role.
✨Prepare for Technical Questions
Expect technical questions that test your knowledge of model tuning and data engineering. Review concepts like LoRA/QLoRA and how to manage Vector DBs. Practising coding problems or system design scenarios related to these topics can give you a leg up during the interview.
✨Emphasise Responsible AI Practices
With a focus on lifecycle and safety, be ready to discuss how you ensure responsible AI in your projects. Talk about your experience with bias detection and monitoring model drift. Showing that you prioritise ethical considerations in AI will resonate well with the interviewers.
