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- Tasks: Design and manage AI systems, ensuring they run smoothly and securely.
- Company: Globally renowned organisation with a focus on AI and data innovation.
- Benefits: Up to £85,000 salary, hybrid work model, and full-time hours.
- Other info: Collaborative environment with opportunities for continuous improvement and growth.
- Why this job: Join a dynamic team and shape the future of AI operations.
- Qualifications: Experience in AI systems, CI/CD pipelines, and cloud infrastructure.
The predicted salary is between 51000 - 85000 £ per year.
Salary: Up to £85,000
Location: Hybrid - 3 days per week onsite in Derby
Working Hours: Full time - Monday to Friday
A globally renowned organisation is seeking an AI Ops Engineer to join a growing AI and data function, taking ownership of the operational backbone that enables AI systems to run reliably, securely and at scale in production environments. This AI Ops Engineer role sits at the intersection of machine learning, cloud infrastructure and DevOps, supporting the full lifecycle of AI solutions from deployment through to ongoing optimisation.
The AI Ops Engineer position is well suited to an engineer who enjoys building resilient platforms, improving operational maturity and working closely with data scientists and software engineers to deliver production-grade AI capability.
Responsibilities for the AI Ops Engineer:
- Design, build and operate deployment pipelines for AI models, prompts and supporting artefacts
- Own lifecycle management including versioning, promotion, rollback and retirement of AI solutions
- Implement monitoring and observability covering performance, usage, drift and data quality
- Ensure AI systems meet security, compliance and governance requirements
- Optimise inference performance, scalability and cost efficiency
- Manage infrastructure supporting training and inference including cloud platforms, containers and GPU resources
- Enable reproducibility through experiment tracking and artefact management
- Support incident response, root-cause analysis and resolution of AI-related failures
- Collaborate with data scientists and software engineers to design scalable, reliable machine learning infrastructure
- Develop and maintain CI/CD pipelines for machine learning workloads
- Maintain standards for version control, testing and technical documentation
- Work with cross-functional teams to integrate AI solutions into existing platforms and workflows
- Stay current with advancements in MLOps, DevOps and AI operations, driving continuous improvement
Essential Skills for the AI Ops Engineer:
- Strong experience operating machine learning or AI systems in production environments
- Hands-on experience with CI/CD pipelines for data or ML workloads
- Experience managing cloud-based infrastructure for AI workloads
- Solid understanding of monitoring, observability and operational resilience
- Strong collaboration skills with the ability to work across engineering and data teams
- Experience supporting secure, compliant and well-governed systems
Desirable Skills for the AI Ops Engineer:
- Experience integrating Python-based services with modern front-end frameworks
- Familiarity with MLOps practices for deploying, monitoring and managing AI systems
- Exposure to large-scale enterprise data environments or knowledge management systems
- Understanding of Agile delivery practices and collaborative tooling
- Knowledge of data security, compliance and responsible AI principles
- Domain exposure within engineering or manufacturing environments
If you are an AI Ops Engineer looking to take ownership of AI operations within a complex, production-focused environment, please apply in the immediate instance.
Ai Ops Engineer - Up To 85,000 Benefits - Hybrid - Derby employer: Involved Solutions
Contact Detail:
Involved Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Ai Ops Engineer - Up To 85,000 Benefits - Hybrid - Derby
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups or webinars, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects related to AI Ops. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common AI Ops scenarios and challenges. Think about how you would handle lifecycle management or optimise performance in real-world situations. Practice makes perfect!
✨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 seeing candidates who are proactive about their job search.
We think you need these skills to ace Ai Ops Engineer - Up To 85,000 Benefits - Hybrid - Derby
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the AI Ops Engineer role. Highlight your experience with CI/CD pipelines, cloud infrastructure, and any hands-on work with machine learning systems.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about AI operations. Share specific examples of how you've improved operational resilience or collaborated with data scientists and engineers in the past.
Showcase Your Technical Skills: Don’t forget to mention your technical proficiencies! Whether it’s your experience with monitoring tools or your understanding of MLOps practices, make sure we see what you bring to the table.
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 this exciting opportunity!
How to prepare for a job interview at Involved Solutions
✨Know Your AI Ops Inside Out
Make sure you brush up on your knowledge of AI systems, especially in production environments. Be ready to discuss your hands-on experience with CI/CD pipelines and cloud infrastructure, as these are crucial for the role.
✨Showcase Your Collaboration Skills
This role requires working closely with data scientists and software engineers. Prepare examples of how you've successfully collaborated in the past, highlighting your ability to communicate effectively across teams.
✨Demonstrate Problem-Solving Abilities
Be ready to tackle hypothetical scenarios related to incident response or root-cause analysis. Think about past challenges you've faced and how you resolved them, particularly in relation to AI-related failures.
✨Stay Current with Industry Trends
Familiarise yourself with the latest advancements in MLOps, DevOps, and AI operations. Showing that you're proactive about continuous improvement will impress interviewers and demonstrate your commitment to the field.