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- Tasks: Build robust ML infrastructure and pipelines for cutting-edge AI solutions.
- Company: Dynamic tech company focused on innovative AI applications.
- Benefits: Competitive salary, hybrid work model, and opportunities for professional growth.
- Why this job: Join a team that transforms experimental models into high-performance systems.
- Qualifications: 5+ years in software or data engineering with ML deployment experience.
- Other info: Collaborative environment with a focus on automation and security.
The predicted salary is between 84000 - 120000 £ per year.
We are looking for a Senior Machine Learning Engineer to build the infrastructure and pipelines that power our AI solutions. In this role, you will move beyond experimental notebooks to create robust, scalable, and automated systems. You will be responsible for the "factory" that allows our models to live, breathe, and perform in high-pressure production environments across various industries.
Key Responsibilities
- ML Systems Architecture: Design and implement the end-to-end lifecycle of ML models, including automated retraining, model versioning, and monitoring systems.
- Productionalization: Take validated prototypes from our Data Science team and re-engineer them into high-performance, maintainable codebases that integrate seamlessly with client ecosystems.
- Infrastructure & MLOps: Build and maintain CI/CD pipelines specifically for machine learning, ensuring that deployments are predictable, reproducible, and secure.
- Performance Optimization: Profile and optimize model inference latency and memory usage to ensure applications remain responsive under heavy load.
- Collaboration: Work at the intersection of Data Science and DevOps, ensuring that the "science" translates into a stable "product."
Technical Profile
- Software Excellence: Mastery of Python with a heavy focus on software engineering best practices (Design Patterns, Clean Code, Unit Testing). Familiarity with C++ or Go is a significant plus.
- Tooling & Orchestration: Deep experience with containerization (Docker, Kubernetes) and orchestration tools like Kubeflow, Airflow, or Metaflow.
- Cloud Architecture: Expertise in deploying ML workloads on AWS, GCP, or Azure, utilizing managed services like SageMaker or Vertex AI.
- Data Foundations: Strong understanding of data modeling, feature stores, and distributed computing (e.g., Spark, Dask).
Qualifications & Experience
- Experience: 5+ years in a software or data engineering role, with at least 3 years specifically focused on the deployment and scaling of Machine Learning models.
- Education: A degree in Computer Science, Software Engineering, or a related technical field.
- Systems Thinking: You think in terms of "pipelines" rather than "scripts." You anticipate edge cases, data drift, and system failures before they happen.
- Problem Solving: A proven track record of troubleshooting complex distributed systems and optimizing resource-intensive algorithms.
What We Value
- Reliability: You believe that a model is only as good as its uptime and the quality of its serving layer.
- Automation Obsession: If a task has to be done twice, you believe it should be scripted.
- Security Mindset: You understand the unique security vulnerabilities inherent in AI/ML systems and build with "security by design" principles.
Senior ML Engineer employer: Anson McCade
Contact Detail:
Anson McCade Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior ML Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those that highlight your ability to build robust systems. This is your chance to shine beyond the CV.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice coding challenges and system design questions to impress the interviewers.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets the attention it deserves. Plus, we love seeing candidates who are proactive!
We think you need these skills to ace Senior ML Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Senior ML Engineer role. Highlight your expertise in Python, MLOps, and any relevant cloud architecture experience to catch our eye!
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about machine learning and how your background makes you a perfect fit for our team. Share specific examples of your past projects that demonstrate your problem-solving skills and systems thinking.
Showcase Your Projects: If you've worked on any interesting ML projects, make sure to include them in your application. We love seeing real-world applications of your skills, especially those that involve building robust pipelines or optimising model performance.
Apply Through Our Website: We encourage you to apply directly through our website for the best chance of getting noticed. It helps us keep track of your application and ensures you’re considered for the role as quickly as possible!
How to prepare for a job interview at Anson McCade
✨Know Your ML Fundamentals
Brush up on your machine learning concepts and be ready to discuss them in detail. Understand the end-to-end lifecycle of ML models, as well as the importance of automated retraining and monitoring systems. This will show that you’re not just familiar with the theory but can also apply it practically.
✨Showcase Your Coding Skills
Since mastery of Python is crucial for this role, prepare to demonstrate your coding abilities. You might be asked to solve a problem on the spot or discuss your previous projects. Make sure you can explain your code clearly and highlight best practices like clean code and unit testing.
✨Discuss Your Experience with CI/CD
Be ready to talk about your experience with building and maintaining CI/CD pipelines for machine learning. Share specific examples of how you've ensured deployments are predictable and secure. This will help illustrate your understanding of MLOps and its significance in production environments.
✨Prepare for System Design Questions
Expect questions that assess your systems thinking and ability to design robust architectures. Think through potential edge cases and how you would handle data drift or system failures. This will demonstrate your proactive approach and problem-solving skills, which are key for a Senior ML Engineer.
