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- Tasks: Design and develop impactful AI solutions for national security challenges.
- Company: Leading tech and digital intelligence organisation in the UK.
- Benefits: Flexible hybrid working, 25 days holiday, competitive perks, and career development support.
- Why this job: Make a real-world impact with cutting-edge machine learning technologies.
- Qualifications: 4-5 years of hands-on ML experience and proficiency in Python frameworks.
- Other info: Mentorship opportunities and a dynamic work environment await you.
The predicted salary is between 48000 - 84000 £ per year.
Are you passionate about building impactful AI solutions and pushing the boundaries of machine learning? Do you want your work to deliver real-world value across critical national infrastructure? We are seeking a Senior Machine Learning Engineer to join a leading technology and digital intelligence organisation in the UK. In this role, you will design, develop, and deploy machine learning and generative AI solutions that address complex challenges in the national security space.
What You'll Do
- Design, develop, and iterate ML models for traditional tasks (forecasting, classification, anomaly detection) and GenAI/LLM applications.
- Lead experimentation cycles: define hypotheses, design experiments, evaluate results, and iterate rapidly.
- Transition validated experiments into production-ready solutions, collaborating with engineers and stakeholders.
- Build and optimise ML pipelines using AWS services and experiment tracking tools.
- Implement robust experiment tracking, model versioning, and reproducibility practices.
- Support production models through monitoring, performance analysis, and continuous improvement.
- Apply responsible AI practices, including model explainability and fairness assessment.
- Mentor junior colleagues and share learnings across the team.
About You
You will have experience in the following:
- 4-5 years' experience of hands-on ML experience.
- Developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow.
- AWS ML services (SageMaker, Lambda, S3) in production environments.
- Experiment design, hypothesis testing, and statistical evaluation.
- Transitioning models from experimentation to production with governance and quality controls.
- MLOps tooling such as MLflow, Weights & Biases, or DVC.
- Developing LLM/GenAI applications, including prompt engineering and RAG architectures.
- Communicating complex findings clearly to technical and non-technical audiences.
Nice-to-Haves
- Advanced LLM techniques: agents, tool use, and agentic workflows.
- Vector databases (Pinecone, Weaviate, pgvector).
- Feature stores (Feast, AWS Feature Store).
- Containerisation and orchestration (Docker, Kubernetes, ECS).
- Infrastructure as Code (Terraform, CloudFormation).
- Large-scale data processing frameworks (Spark, Dask).
- Experience in regulated industries or working with sensitive data.
Security Clearance
UKIC DV eligible required. £7,000 tax-free DV bonus on completion (TBC, paid quarterly).
Location
London-based hybrid role. Up to 3 days per week onsite at customer location once cleared. 1 day per week team day in the London office (counts as onsite).
What We Offer
- Flexible, hybrid working with support for work-life balance.
- 25 days holiday, with options to buy/sell and carry over.
- Competitive benefits including pension, cycle-to-work, and lifestyle perks.
- Career development support with dedicated managers and mentoring opportunities.
- Bonus scheme and participation in diversity and support groups.
If you are driven to deliver AI solutions that make a tangible impact and enjoy working at the cutting edge of machine learning, we want to hear from you.
Senior Machine Learning Engineer employer: Anson McCade
Contact Detail:
Anson McCade Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to people 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 refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those that highlight your experience with Python and AWS services. This will give you an edge and demonstrate your hands-on expertise.
✨Tip Number 3
Prepare for interviews by practising common ML scenarios and problem-solving questions. Be ready to discuss your past projects and how you’ve transitioned models from experimentation to production. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Senior Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the Senior Machine Learning Engineer role. Highlight your hands-on ML experience and any projects that showcase your ability to design and deploy models.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about AI solutions and how your background aligns with our mission. Share specific examples of your work in machine learning and how it has delivered real-world value.
Showcase Your Technical Skills: Don’t forget to mention your experience with Python frameworks, AWS services, and MLOps tooling. We want to see how you’ve applied these in real projects, so be specific about your contributions and outcomes.
Apply Through Our Website: We encourage you to apply directly through our website for a smoother application process. It’s the best way for us to receive your application and get you on our radar!
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, XGBoost, and PyTorch. 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 iterated based on results. This will demonstrate your ability to lead and transition validated experiments into production-ready solutions.
✨Familiarise Yourself with AWS Services
Since the role involves building ML pipelines using AWS services, brush up on your knowledge of SageMaker, Lambda, and S3. Be prepared to discuss how you've used these tools in past projects and how they can optimise ML workflows.
✨Communicate Clearly and Confidently
Practice explaining complex machine learning concepts in simple terms. You might be asked to communicate findings to both technical and non-technical audiences, so being able to articulate your thoughts clearly will set you apart from other candidates.
