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For Companies At a Glance
- Tasks: Design and develop machine learning models for innovative AI applications.
- Company: Join a leading organisation enhancing the UK's security through AI.
- Benefits: Competitive salary, professional development, and opportunities to make a real impact.
- Why this job: Be at the forefront of AI technology and contribute to meaningful projects.
- Qualifications: Experience in Python ML frameworks and AWS services is essential.
- Other info: Mentorship opportunities and a dynamic work environment await you.
The predicted salary is between 36000 - 60000 £ per year.
This is an exciting time to join a team to help pioneer both customer's and an AI adoption journey. Not only will you be directly making a huge impact through the solutions you develop, you’ll be doing it for an organisation that makes a huge impact on the security of the UK.
Core Duties
- Design and develop machine learning models for traditional ML use cases (forecasting, classification, anomaly detection) and GenAI/LLM applications
- Lead experimentation cycles: define hypotheses, design experiments, evaluate results, and iterate rapidly while adhering to governance requirements
- Transition validated experiments into production-ready solutions, working closely with other engineers on deployment and monitoring
- Build and optimise ML pipelines using AWS services and experiment tracking tools
- Develop and integrate LLM-powered solutions for tracing, evaluation, and production monitoring
- Implement robust experiment tracking, model versioning, and reproducibility practices with full audit trails
- Design feature engineering approaches and contribute to feature store development
- Support production models through monitoring, performance analysis, and continuous improvement
- Apply responsible AI practices, including model explainability and fairness assessment
- Present experiment findings and production outcomes to stakeholders, articulating operational and strategic value
- Mentor junior colleagues and share learnings across the team
You will have experience in many of the following:
- Hands-on experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow
- Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments
- Strong experiment design skills: hypothesis formulation, A/B testing methodology, and statistical evaluation
- Proven track record transitioning models from experimentation to production with appropriate governance and quality controls
- Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, Data Version Control)
It Would Be Great If You Also Had Experience In Some Of These, But If Not We’ll Help You With Them
- Experience with advanced LLM techniques: agents, tool use, and agentic workflows
- Experience with vector databases (Pinecone, Weaviate, pgvector) for RAG applications
- Experience with feature stores (Feast, AWS Feature Store)
- Experience with containerisation (Docker) and orchestration (Kubernetes, ECS)
Machine Learning Engineer in City of London employer: Anson McCade
Contact Detail:
Anson McCade Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in City of 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 machine learning projects. Whether it's a GitHub repo or a personal website, having tangible examples of your work can really set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining your projects and the impact they had. Remember, it’s not just about what you did, but how you can communicate it effectively!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, it gives you a better chance to stand out and be noticed by our hiring team.
We think you need these skills to ace Machine Learning Engineer in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with ML models, AWS services, and any relevant projects. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how you can contribute to our mission. Be sure to mention any specific experiences that relate to the job description.
Showcase Your Projects: If you've worked on any cool ML projects, don’t hold back! Include links to your GitHub or any other platforms where we can see your work. We love seeing practical applications of your skills!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Anson McCade
✨Know Your ML Models Inside Out
Make sure you can discuss the machine learning models you've worked on in detail. Be ready to explain your design choices, the frameworks you used, and how you transitioned them into production. This shows not only your technical skills but also your ability to communicate complex ideas clearly.
✨Showcase Your Experimentation Skills
Prepare to talk about your experience with hypothesis formulation and A/B testing. Bring examples of experiments you've designed, the results you achieved, and how you iterated based on those findings. This will demonstrate your analytical mindset and your commitment to responsible AI practices.
✨Familiarise Yourself with AWS Services
Since the role involves using AWS ML services, brush up on your knowledge of SageMaker, Lambda, and S3. Be ready to discuss how you've used these tools in past projects, particularly in building and optimising ML pipelines. This will show that you're not just a theorist but someone who can apply their knowledge practically.
✨Prepare to Present Your Findings
Think about how you would present your experiment findings and production outcomes to stakeholders. Practice articulating the operational and strategic value of your work. This is crucial as it reflects your ability to communicate effectively and mentor others, which is a key part of the role.
