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For Companies At a Glance
- Tasks: Design and develop innovative machine learning models to enhance AI adoption.
- Company: Join a leading organisation focused on improving UK security through technology.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Why this job: Make a real impact in AI while working with cutting-edge technologies.
- Qualifications: Experience in Python ML frameworks and AWS services is essential.
- Other info: Dynamic team environment with mentorship opportunities and career advancement.
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 own 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 who makes a huge impact to 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
About You
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)
- Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures
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)
- Familiarity with Infrastructure as Code (Terraform, CloudFormation)
- Experience with data processing frameworks (Spark, Dask) for large-scale workloads
- Understanding of data governance and compliance frameworks
Machine Learning Engineer in Slough employer: Anson McCade
Contact Detail:
Anson McCade Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in Slough
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those using Python and AWS. This will give you a leg up when chatting with hiring managers and help them see your practical experience.
✨Tip Number 3
Prepare for interviews by brushing up on your experiment design skills and be ready to discuss your past projects. Practice explaining complex concepts in simple terms, as you'll need to articulate your findings to stakeholders.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace Machine Learning Engineer in Slough
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 that showcase your skills. We want to see how you can make an impact!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're excited about this role and how your background aligns with our mission. Let us know how you can contribute to our AI adoption journey.
Showcase Your Projects: If you've worked on any cool ML projects, don’t hold back! Include links or descriptions of your work, especially if it involves LLM applications or experiment tracking. We love seeing practical examples of your skills.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of your application and ensures you don’t miss out on any important updates. We can’t wait to hear from you!
How to prepare for a job interview at Anson McCade
✨Know Your ML Models Inside Out
Make sure you can discuss your experience with various machine learning models, especially those mentioned in the job description like scikit-learn and TensorFlow. Be ready to explain how you've designed, developed, and deployed these models in real-world scenarios.
✨Showcase Your Experimentation Skills
Prepare to talk about your approach to experimentation cycles. Highlight specific examples where you've defined hypotheses, designed experiments, and evaluated results. This will demonstrate your ability to lead and iterate effectively while adhering to governance requirements.
✨Familiarise Yourself with AWS Services
Since the role involves using AWS ML services, brush up on your knowledge of SageMaker, Lambda, and S3. Be prepared to discuss how you've used these tools in production environments and any challenges you faced during deployment.
✨Communicate Clearly with Stakeholders
Practice articulating your findings and production outcomes in a way that highlights their operational and strategic value. Being able to present complex technical information clearly will set you apart and show that you can engage with non-technical stakeholders.
