At a Glance
- Tasks: Design and implement AI solutions using Azure technologies to solve real business challenges.
- Company: Join a forward-thinking tech company focused on innovative AI solutions.
- Benefits: Enjoy competitive pay, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with excellent career advancement potential.
- Why this job: Be at the forefront of AI technology and make a meaningful impact in various industries.
- Qualifications: Experience in AI/ML solutions on Azure and strong Python skills are essential.
The predicted salary is between 60000 - 80000 £ per year.
In This Role, Your Responsibilities Will Be:
As an Azure AI Engineer, you will be responsible for architecting and implementing end-to-end AI solutions using Azure Machine Learning, Azure AI Studio, Azure AI Foundry, Cognitive Services, and the Power Platform. You will leverage both prebuilt AI models and custom machine learning workflows to solve real business problems, integrate AI into enterprise systems, and ensure robust governance, monitoring, and lifecycle management.
Who You Are:
An Azure AI Engineer who can design, build, and operate intelligent solutions using Microsoft Azure’s AI and machine learning ecosystem. The ideal candidate combines strong technical expertise with a practical understanding of scalable, secure, and responsible AI development. While not every listed skill is mandatory, a solid mix of the capabilities below will position you for success in this role.
Key Responsibilities- AI Solution Design & Development
- Build, train, and deploy machine learning models using Azure Machine Learning, Python, Azure AI Foundry and Azure ML SDKs.
- Develop scalable AI pipelines using Azure ML pipelines, AutoML, and cloud-native best practices.
- Integrate Azure AI capabilities—Vision, Language, Speech, and Document Intelligence—into enterprise applications.
- Design and automate AI-driven solutions using the Power Platform (Power Apps, Power Automate, Power Virtual Agents) integrated with Azure AI Services.
- Utilize Azure AI Studio, Azure AI Foundry to rapidly prototype and deliver generative AI, agentic AI and cognitive solutions.
- Cross-Functional Collaboration
- Partner with data engineers, analytics teams, and business stakeholders to translate business needs into impactful AI solutions.
- Collaborate with Power Platform developers to embed AI models and cognitive services into low-code applications and workflows.
- Contribute to architecture decisions ensuring scalability, maintainability, and alignment with enterprise cloud standards.
- Communicate technical concepts clearly to both technical and non-technical audiences.
- Responsible AI, Security & Governance
- Apply Responsible AI guidelines, ensuring fairness, explainability, and ethical use of machine learning models.
- Enforce data privacy, compliance, and security standards across all AI workloads.
- Support model versioning, governance, and lifecycle management.
- MLOps, Monitoring & Optimization
- Implement monitoring and observability for AI workloads using Azure Monitor, Application Insights, and MLflow (where applicable).
- Optimize model performance, reliability, and cost efficiency across training and inference workloads.
- Contribute to CI/CD workflows for ML and Power Platform integrations using Azure DevOps or GitHub Actions.
For This Role, You Will Need:
- Bachelor’s degree in computer science, Computer Engineering, IT, or related discipline.
- Hands-on experience designing and deploying AI/ML solutions on Microsoft Azure.
- Experience with Azure AI Studio, Azure AI Foundry, Azure ML Designer, AutoML, and Prompt engineering Flow.
- Strong proficiency in Python and Azure SDKs for ML and Cognitive Services.
- Solid understanding of machine learning concepts: data preparation, model development, evaluation, and deployment.
- Experience with cloud-native development and CI/CD for ML pipelines.
- Strong communication, collaboration, and problem-solving skills.
Preferred Qualifications That Set You Apart:
- Exposure to Microsoft Power Platform and integrating Azure AI models/services into Power Apps, Power Automate, and Copilots.
- Familiarity with MLOps practices and tools such as MLflow, Azure DevOps, or GitHub Actions.
- Microsoft certifications (e.g., Azure AI Engineer Associate, Azure Data Scientist Associate) are an added advantage.
- Working knowledge of Power BI for embedding or visualizing AI-driven insights.
Manager Azure AI Engineer in Tipton employer: Saur Energy International
As a leading employer in the tech industry, we offer an innovative work environment where creativity and collaboration thrive. Our commitment to employee growth is evident through continuous learning opportunities, mentorship programmes, and access to cutting-edge technology. Located in a vibrant area, we provide a supportive culture that values diversity and encourages employees to make a meaningful impact through their work in AI solutions.
StudySmarter Expert Advice🤫
We think this is how you could land Manager Azure AI Engineer in Tipton
✨Tip Number 1
Network like a pro! Reach out to your connections in the AI and Azure space. Attend meetups, webinars, or even 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 AI projects, especially those using Azure. This could be anything from machine learning models to Power Platform applications. A strong portfolio can really set you apart during interviews.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Azure and AI knowledge. Practice common interview questions related to AI solutions and be ready to discuss your past projects. We recommend doing mock interviews with friends or using online platforms.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search. So, get that application in and let’s get you on board!
We think you need these skills to ace Manager Azure AI Engineer in Tipton
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the role of Azure AI Engineer. Highlight your experience with Azure Machine Learning, Python, and any relevant projects that showcase your skills in AI solution design and development.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about AI and how your background aligns with our needs. Mention specific technologies like Azure AI Studio and Power Platform to show you know your stuff!
Showcase Your Projects:If you've worked on any AI projects, whether in a professional or personal capacity, make sure to include them. We love seeing real-world applications of your skills, especially if they involve integrating AI into enterprise systems.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Saur Energy International
✨Know Your Azure AI Stuff
Make sure you brush up on your knowledge of Azure Machine Learning, Azure AI Studio, and the Power Platform. Be ready to discuss how you've used these tools in past projects, as well as any specific AI solutions you've architected or implemented.
✨Showcase Your Collaboration Skills
Since this role involves working with data engineers and business stakeholders, prepare examples that highlight your teamwork. Think about times when you translated complex technical concepts into layman's terms for non-technical audiences.
✨Emphasise Responsible AI Practices
Familiarise yourself with Responsible AI guidelines and be prepared to discuss how you've applied them in your work. Highlight your understanding of fairness, explainability, and ethical considerations in AI development.
✨Demonstrate Your Problem-Solving Abilities
Be ready to tackle hypothetical scenarios during the interview. Think through how you would approach designing scalable AI solutions or optimising model performance, and articulate your thought process clearly.
