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- Tasks: Develop and deploy cutting-edge AI models using Python and collaborate with innovative teams.
- Company: Join a forward-thinking tech company in the heart of London.
- Benefits: Enjoy a hybrid work model, competitive salary, and opportunities for professional growth.
- Why this job: Be at the forefront of AI technology and make a real impact on future solutions.
- Qualifications: Strong Python skills and experience with ML libraries and frameworks.
- Other info: Dynamic team environment with excellent career advancement opportunities.
The predicted salary is between 43200 - 72000 £ per year.
We are looking for a talented AI Engineer with strong Python expertise to join our team and help build intelligent, scalable AI solutions.
Key Responsibilities:
- Develop, train, and deploy ML/DL models using Python.
- Work with TensorFlow / PyTorch / Scikit‑learn for model building.
- Build data pipelines and perform data preprocessing & feature engineering.
- Integrate AI models into applications via APIs (Flask/FastAPI).
- Optimize models, run experiments, and improve performance.
- Collaborate with data engineers, product, and software teams.
- Follow best practices in MLOps, model versioning & monitoring.
Required Skills:
- Strong knowledge of Python and ML libraries (NumPy, Pandas, Scikit‑learn).
- Experience with deep learning frameworks (TensorFlow or PyTorch).
- Solid understanding of ML algorithms, NLP, and data processing.
- Experience with cloud platforms (Azure / AWS / GCP).
- Knowledge of SQL/NoSQL databases.
Good to Have:
- Experience with LLMs / Generative AI / model fine‑tuning.
- Understanding of MLOps tools (MLflow, Kubeflow, Airflow).
- Experience with Docker/Kubernetes.
AI Engineer with Python employer: Market Cloud
Contact Detail:
Market Cloud Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Engineer with Python
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and Python community on LinkedIn or at local meetups. 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 projects, especially those involving ML/DL models. Share it on GitHub and make sure it's easy for potential employers to see what you can do.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and ML knowledge. Practice coding challenges and be ready to discuss your past projects in detail. We want to see how you think and solve problems!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are genuinely interested in joining our team. Don’t miss out!
We think you need these skills to ace AI Engineer with Python
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your Python expertise and experience with ML libraries. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Tell us why you’re passionate about AI and how your background makes you a perfect fit for our team. Keep it engaging and personal.
Showcase Your Projects: If you've worked on any cool AI projects, make sure to mention them! Whether it's a personal project or something from 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 don’t miss out on any important updates from our team!
How to prepare for a job interview at Market Cloud
✨Know Your Python Inside Out
Make sure you brush up on your Python skills before the interview. Be ready to discuss your experience with libraries like NumPy, Pandas, and Scikit-learn, as well as any projects where you've used them. Practising coding challenges can also help you demonstrate your proficiency.
✨Familiarise Yourself with ML Frameworks
Since the role requires knowledge of TensorFlow or PyTorch, take some time to review key concepts and recent projects you've worked on using these frameworks. Be prepared to explain how you've built and deployed models, and any challenges you faced along the way.
✨Showcase Your MLOps Knowledge
Understanding MLOps is crucial for this position. Brush up on best practices in model versioning and monitoring. If you've used tools like MLflow or Kubeflow, be ready to share specific examples of how they improved your workflow.
✨Prepare for Technical Questions
Expect technical questions related to machine learning algorithms, data preprocessing, and feature engineering. Think about how you would approach building a data pipeline or integrating an AI model into an application. Practising these scenarios can help you articulate your thought process clearly.