At a Glance
- Tasks: Design and deploy cutting-edge AI/ML solutions in dynamic enterprise environments.
- Company: Join a leading consultancy driving major enterprise transformations.
- Benefits: Competitive salary, hybrid work model, and opportunities for professional growth.
- Other info: Collaborate with clients on high-impact projects in a fast-paced environment.
- Why this job: Make a real impact by delivering scalable AI solutions that transform businesses.
- Qualifications: 5+ years in AI/ML with strong GCP and Kubernetes skills.
The predicted salary is between 70000 - 90000 £ per year.
We are seeking experienced AI / ML / AI Engineering professionals to join a major enterprise transformation programme with a leading consultancy. This is a hands-on delivery role focused on building and deploying production-grade AI systems within complex enterprise environments, not research or proof-of-concept work.
You will work across large-scale client engagements, designing, building, and deploying scalable AI/ML solutions using modern cloud-native technologies, with a strong emphasis on production readiness, performance, and enterprise integration.
Responsibilities- Design, develop, and deploy AI/ML solutions into production environments
- Build and operationalise machine learning models using cloud-native architectures
- Work extensively within Google Cloud Platform (GCP) environments
- Deploy and manage workloads using Google Kubernetes Engine (GKE) and Kubernetes
- Develop scalable ML pipelines and support MLOps practices including CI/CD, monitoring, and automation
- Integrate AI solutions into wider enterprise systems and data platforms
- Work closely with enterprise clients across transformation programmes
- Contribute to solution design, architecture decisions, and engineering best practices
- 5+ years’ experience in AI / ML / Data Science / AI Engineering
- Solid experience with GCP and Kubernetes / GKE
- Strong Python and cloud engineering skills
- Experience in consulting or enterprise transformation environments
- Understanding of MLOps, model deployment, and cloud-native engineering practices
- Strong stakeholder engagement and client-facing communication skills
- Exposure to GenAI, LLMs, or agent-based AI systems
- Experience with Terraform or other Infrastructure-as-Code tools
- Experience with BigQuery, Vertex AI, Dataflow or related GCP services
- CI/CD pipeline experience for ML systems
- Experience working in regulated or large-scale enterprise environments
This is an opportunity to work on high-impact enterprise transformation programmes delivering real-world AI solutions at scale. The role suits engineers who enjoy building production systems, working closely with clients, and operating in complex cloud environments.
If you are an AI Engineer, ML Engineer, or Data Scientist with strong cloud-native experience and a consulting mindset, we would be keen to speak with you.
Senior Production AI Engineer - GCP & MLOps employer: Coltech
Join a leading consultancy in Edinburgh, where you will be part of a dynamic team dedicated to transforming enterprises through cutting-edge AI solutions. Our hybrid work culture promotes flexibility and collaboration, while our commitment to employee growth ensures you have access to continuous learning opportunities and career advancement. With a focus on impactful projects and a supportive environment, we empower our engineers to thrive and make a meaningful difference in the world of AI.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Production AI Engineer - GCP & MLOps
✨Tip Number 1
Network like a pro! Reach out to your connections in the AI and ML space, especially those who work with GCP or in consultancy. 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 AI/ML projects, especially those involving cloud-native technologies. This will give potential employers a taste of what you can do in production environments.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Be ready to discuss your experience with GCP, Kubernetes, and MLOps practices. We want to see how you can integrate AI solutions into enterprise systems!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love hearing from candidates who are genuinely excited about transforming enterprises with AI.
We think you need these skills to ace Senior Production AI Engineer - GCP & MLOps
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the role of Senior Production AI Engineer. Highlight your experience with GCP, Kubernetes, and MLOps practices. We want to see how your skills align with our needs!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about AI engineering and how your background makes you a perfect fit for our enterprise transformation programme. Let us know what excites you about the role!
Showcase Your Projects:If you've worked on any relevant projects, make sure to mention them! Whether it's deploying ML models or integrating AI solutions, we love to see real-world examples of your work. It helps us understand your hands-on experience.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It’s the best way for us to receive your application and keep track of it. Plus, it shows you're keen on joining our team!
How to prepare for a job interview at Coltech
✨Know Your Tech Inside Out
Make sure you’re well-versed in GCP, Kubernetes, and the tools mentioned in the job description. Brush up on your Python skills and be ready to discuss how you've used these technologies in real-world scenarios. Prepare examples of scalable AI/ML solutions you've built or deployed.
✨Showcase Your MLOps Knowledge
Since this role emphasises MLOps practices, be prepared to talk about your experience with CI/CD pipelines, model deployment, and automation. Share specific instances where you’ve operationalised machine learning models and how you ensured their performance and reliability in production.
✨Engage with Stakeholders
Strong communication skills are key in consulting roles. Think of examples where you’ve successfully engaged with clients or stakeholders. Be ready to discuss how you’ve translated technical concepts into layman’s terms and how you’ve collaborated with teams to achieve project goals.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities in complex environments. Practice articulating your thought process when faced with challenges in deploying AI systems. Use the STAR method (Situation, Task, Action, Result) to structure your responses effectively.
