At a Glance
- Tasks: Design and deploy innovative Machine Learning solutions using Python and Azure AI services.
- Company: Leading tech firm in London with a focus on AI and collaboration.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Exciting projects with excellent career advancement potential.
- Why this job: Join a dynamic team and shape the future of AI technology.
- Qualifications: 8+ years in ML Engineering, strong Python skills, and experience with Azure services.
The predicted salary is between 80000 - 100000 € per year.
Location: London, UK (Hybrid)
Key Responsibilities
- Design, develop, and deploy scalable Machine Learning and Generative AI solutions using Python and Azure AI services.
- Build and manage end-to-end ML/LLM pipelines using Azure ML, Azure AI Foundry, Azure OpenAI, and Databricks.
- Develop and deploy production-grade LLM applications including fine-tuning, prompt engineering, inference optimization, and monitoring.
- Implement and maintain MLOps workflows, CI/CD pipelines, and model lifecycle management processes.
- Work with Azure services including AKS, ADF, Synapse, Azure Storage, and containerized deployments.
- Monitor model performance, drift detection, scalability, reliability, and operational efficiency.
- Collaborate with cross-functional teams including Data Engineering, DevOps, Product, and Architecture teams.
- Implement best practices for version control, reproducibility, governance, monitoring, and AI security.
- Troubleshoot and optimize ML/AI systems in production environments.
Required Skills & Experience
- 8+ years of experience in Machine Learning Engineering / MLOps.
- Strong programming experience in Python with ML frameworks and Azure SDKs.
- Hands-on experience with: Azure ML, Azure AI Foundry, Azure OpenAI, AKS (Azure Kubernetes Service), Databricks, Azure Data Factory (ADF), Azure Synapse, Azure Storage.
- Experience deploying and monitoring LLMs in production environments.
- Strong understanding of: Fine-tuning, Prompt Engineering, Inference Optimization, Generative AI, LLMOps.
- Experience with CI/CD pipelines using Azure DevOps and GitHub Actions.
- Strong knowledge of Docker and containerized deployments.
- Familiarity with MLOps best practices including: Version Control, Experiment Tracking, Reproducibility, Monitoring & Logging.
- Excellent problem-solving, communication, and collaboration skills.
Machine Learning Engineer employer: Pyramid Consulting, Inc
As a leading employer in the tech industry, we offer Machine Learning Engineers the opportunity to work on cutting-edge AI solutions in a dynamic and collaborative environment. Our hybrid work model in London promotes a healthy work-life balance while providing access to continuous learning and professional development opportunities. Join us to be part of a forward-thinking team that values innovation, diversity, and the growth of its employees.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry, attend meetups, and join online forums. The more people you know, the better your chances of landing that ML Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Azure and ML frameworks. 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 common ML concepts and Azure services. Practice explaining your past projects and how you tackled challenges. Confidence is key, so let your passion for ML shine through!
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for you, and applying directly can sometimes give you an edge. Let’s get you that dream job together!
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with Python, Azure services, and MLOps practices. We want to see how your skills match up with what we're looking for!
Showcase Your Projects:Include specific projects where you've designed and deployed ML solutions. We love seeing real-world applications of your skills, especially if they involve Azure or generative AI. Don't hold back on the details!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about machine learning and how you can contribute to our team. We appreciate a personal touch that shows us who you are.
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’re considered for the role. Plus, it’s super easy – just a few clicks and you’re done!
How to prepare for a job interview at Pyramid Consulting, Inc
✨Know Your Tech Stack
Make sure you’re well-versed in the specific technologies mentioned in the job description. Brush up on your Python skills, Azure services, and ML frameworks. Being able to discuss your hands-on experience with Azure ML, Databricks, and LLMs will show that you’re not just familiar but truly capable.
✨Showcase Your Projects
Prepare to talk about your previous projects, especially those involving MLOps and production-grade applications. Highlight your role in deploying and monitoring ML systems, and be ready to discuss challenges you faced and how you overcame them. Real-world examples can make a big impact!
✨Understand the Collaboration Aspect
Since the role involves working with cross-functional teams, be prepared to discuss your experience collaborating with Data Engineering, DevOps, and Product teams. Share examples of how you’ve effectively communicated technical concepts to non-technical stakeholders.
✨Ask Insightful Questions
At the end of the interview, don’t forget to ask questions! Inquire about the team’s current projects, their approach to MLOps, or how they handle model performance monitoring. This shows your genuine interest in the role and helps you gauge if it’s the right fit for you.
