Era4 develops, owns and operates AI infrastructure across the UK, powered by renewable energy. Converting legacy industrial and energy sites into modern data-centre facilities, Era4 is combining brownfield regeneration opportunities with cleaner, efficient, scalable compute capacity for healthcare, research, finance, enterprise, and public-sector organisations.

We value practical experience and demonstrated ability over credentials. If you've built production AI systems that meet enterprise standards, we want to hear from you.

Role Summary:

We're seeking an AIOps Engineer to embed artificial intelligence throughout Era4's operations. This is a unique opportunity to work at the intersection of AI infrastructure provision and AI implementation; you'll leverage our GPU resources to build intelligent systems that transform how we deliver services and run our business.

You'll have the autonomy to identify opportunities where AI can drive efficiency, quality, and innovation. From automating customer onboarding and resource optimization to building intelligent monitoring and predictive maintenance systems, you'll design and implement solutions that give Era4 a competitive edge.

Critical to this role: Our customers include government, regulated sectors, and enterprise clients with stringent security and reliability requirements. Your solutions must meet enterprise grade standards for security, auditability, data governance, and operational resilience.

Key Responsibilities:

• Embed inside Era4 to audit their workflows across every department and identify automation opportunities
• Translate unstructured, manual processes into clean, reliable automated systems, using AI, no-code, custom code, or whatever works.
• Independently identify and prioritise high-impact AI opportunities across IaaS/PaaS service delivery and corporate operations.
• Design and implement full-stack AI OPs applications from data pipeline to user interface.
• Build solutions using both our in-house GPU infrastructure and external AI services as appropriate.
• Develop intelligent systems for workload optimization, capacity planning, anomaly detection, and predictive maintenance.
• Apply AI to internal processes including contract analysis, pricing optimisation, and business intelligence.
• Ensure all solutions meet enterprise security, compliance, and reliability standards.
• Create production-grade code with comprehensive testing, monitoring, and documentation.

Essential Experience:

AIOps Engineering:

• Engineers who can distil complex problems and architect end‑to‑end AI systems, not just experiment with models.
• Strong AI Ops builders, shipping, running, monitoring, and improving AI systems in production.
• Strong foundation in AI fundamentals with hands-on experience deploying models to production.
• Proven experience implementing AI within enterprise environments (security, governance, reliability).
• AI‑native owner‑operators who identify opportunities, build solutions, and stay accountable post‑launch.
• Real practitioners with GitHub & GitLab repositories showcasing production‑grade AI/ML systems.
• Fluency across automation tooling, you know when to use no-code tools, custom code, or an AI agent and you select the right one. 

Enterprise & Infrastructure:

• Understanding of enterprise security requirements, data governance, and compliance frameworks.
• Experience building reliable, auditable systems for regulated or high-stakes environments.
• Knowledge of Linux systems, containerization (Docker, Kubernetes), and infrastructure-as-code.
• Familiarity with GPU infrastructure or HPC environments highly valuable. 

Problem-Solving & Autonomy:

• Demonstrated ability to independently identify problems and design practical solutions.
• Pragmatic approach to technology selection, build vs. buy vs. integrate.
• Strong analytical skills with attention to security and quality implications.
• Comfortable defining your own roadmap in a fast-growing organisation.
• Excellent communication skills e.g. able to explain trade-offs and limitations with both technical and non-technical stakeholders

One or more would be an advantage:

• AI/ML implementation in B2B, cloud infrastructure, or technical operations contexts (e.g. AIOps, platform engineering).
• Work with government, financial services, or other regulated sector clients.
• MLOps, AI orchestration platforms, and production ML lifecycle management.
• Understanding of enterprise requirements, data sovereignty, security clearance processes, and compliance requirements.
• Business process automation and intelligent workflow tools.

Why Join Era4:

You’ll be joining a mission-driven start-up building critical national infrastructure, where operational excellence directly enables growth. This role offers high visibility with leadership, real autonomy, and the chance to shape how a next-generation company operates at scale.

Diversity & Inclusion:

Era4 is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

How to Apply:

Please send your CV along with your Github link and a brief cover letter describing:

• An AI Ops system you've built and deployed to production, particularly in enterprise or regulated environments.
• How you approach security, reliability, and quality when implementing AI solutions.
• An example of independently identifying and solving an ambiguous problem.