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For Companies At a Glance
- Tasks: Design and optimise machine learning models, owning the entire ML lifecycle.
- Company: Join a leading tech firm focused on innovation and collaboration.
- Benefits: Competitive salary, remote work options, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on continuous improvement and learning.
- Why this job: Make an impact in AI by working with cutting-edge technologies and mentoring others.
- Qualifications: Experience in ML model development, Python proficiency, and strong problem-solving skills.
The predicted salary is between 60000 - 80000 £ per year.
What you will do as a Senior ML Engineer:
- Design, build, and optimise machine learning models, including NLP, computer vision, and predictive analytics.
- Own the ML lifecycle from data preparation through training, evaluation, and deployment.
- Implement and maintain MLOps workflows for continuous integration and delivery of ML models.
- Collaborate with Data Engineers and DevOps teams to ensure production readiness and scalability.
- Contribute to architecture decisions for ML pipelines and data flows.
- Apply secure coding and configuration practices in line with compliance standards.
- Mentor junior engineers and share best practices across the team.
- Support innovation by researching emerging ML techniques and tools.
What you’ll bring:
- Proven experience developing and deploying machine learning models in production environments.
- Proven experience with the OpenCV framework and various object detection models, including YOLO, RCNN, and Vision models, along with a clear understanding of when to apply each model.
- Proficiency with object detection concepts. Experience in video analysis, particularly optical flow and object tracking.
- Solid knowledge of Optical Character Recognition (OCR) models, with the ability to fine-tune these models using custom datasets.
- An understanding of how to measure the accuracy of text extractions through metrics like Character Error Rate (CER) and Word Error Rate (WER) is also crucial.
- Strong proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch).
- Understanding of ML architectures, hyperparameter tuning, and performance optimisation.
- Experience with MLOps tools and CI/CD pipelines.
- Familiarity with data engineering concepts (ETL, data pipelines, SQL).
- Ability to analyse complex data and communicate insights effectively.
- Strong problem-solving skills and attention to detail.
- Excellent collaboration and stakeholder engagement skills.
Core areas (must have):
- ML Development Expertise: Hands-on experience building and deploying ML models.
- Lifecycle Ownership: Ability to manage ML workflows from design to production.
- Tool Proficiency: Skilled in Python, ML frameworks, and MLOps tooling.
- Data Engineering Awareness: Understanding of data pipelines, warehousing, and integration.
- Governance & Compliance: Familiarity with secure coding and quality assurance standards.
- Collaboration & Mentoring: Ability to work across teams and support junior engineers.
- Continuous Improvement: Commitment to learning and applying emerging ML techniques.
- Experience with cloud platforms (AWS) and containerisation (such as Docker, Podman, Kubernetes).
- Exposure to big data technologies (Spark, Hadoop) and Apache tools.
- Knowledge of NLP, computer vision, and deep learning architectures.
- Familiarity with Agile and DevOps practices.
- STEM degree or equivalent experience in AI, Data Science, or related fields.
- Industry certifications (e.g., TensorFlow Developer, AWS Machine Learning Specialty).
- Experience working in secure or regulated environments.
- SC clearance is mandatory for this role.
Senior Machine Learning Engineer in London employer: LHH
Contact Detail:
LHH Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the ML field, attend meetups, and engage in online forums. 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 involving NLP and computer vision. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your experience with MLOps and how you've optimised models in production. Practice common ML interview questions to boost your confidence!
✨Tip Number 4
Don’t forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Tailor your application to highlight your relevant experience and passion for continuous improvement in ML.
We think you need these skills to ace Senior Machine Learning Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with machine learning models, especially in NLP and computer vision. We want to see how you've owned the ML lifecycle and any MLOps workflows you've implemented.
Showcase Your Projects: Include specific projects where you've developed and deployed ML models. If you've worked with OpenCV or any object detection models like YOLO or RCNN, let us know! We love seeing real-world applications of your skills.
Highlight Collaboration Skills: Since collaboration is key for us, mention any experiences where you've worked with Data Engineers or DevOps teams. Show us how you’ve contributed to architecture decisions and mentored junior engineers.
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 get to know you better. Don’t forget to double-check for any typos before hitting send!
How to prepare for a job interview at LHH
✨Know Your Models Inside Out
Make sure you can discuss the machine learning models you've worked with, especially those mentioned in the job description like YOLO and RCNN. Be ready to explain when to use each model and share specific examples of your experience with them.
✨Showcase Your MLOps Knowledge
Since this role involves managing the ML lifecycle, brush up on your MLOps workflows. Be prepared to talk about your experience with CI/CD pipelines and how you've implemented them in past projects. Highlight any tools you've used, like Docker or Kubernetes.
✨Demonstrate Collaboration Skills
This position requires working closely with Data Engineers and DevOps teams. Think of examples where you've successfully collaborated across teams. Emphasise your communication skills and how you've mentored junior engineers in the past.
✨Stay Updated on Emerging Techniques
Research the latest trends and techniques in machine learning, especially in NLP and computer vision. Be ready to discuss any new tools or methods you've explored and how they could benefit the company. Showing a commitment to continuous improvement will impress your interviewers.