Overview We're looking for a skilled Generative AI Engineer to help build and scale a modern AI platform within a large, complex enterprise environment. This role combines hands-on Python development with DevOps practices and the integration of large language models into secure, production-grade systems. You'll be working on emerging technologies, solving real-world engineering challenges, and contributing to the evolution of AI capabilities in a regulated setting. Key Responsibilities Design, build, and maintain backend services and APIs that provide secure access to AI capabilities Develop Python-based components for prompt orchestration, output validation, and evaluation workflows Integrate LLMs into existing enterprise systems, ensuring alignment with security and observability standards Implement and manage CI/CD pipelines in line with engineering best practices Collaborate with cross-functional teams (engineering, platform, and operations) to ensure reliable deployment and scaling Support experimentation, benchmarking, and cost/performance analysis of AI models Contribute to retrieval-augmented generation (RAG) solutions and data integration patterns Establish reusable API standards, frameworks, and documentation to support wider adoption Troubleshoot and optimise distributed systems and cloud-based services Core Skills & Experience Strong background in backend engineering using Python (additional exposure to Node.js or similar is beneficial) Hands-on experience working with Generative AI and large language models Understanding of prompt engineering challenges and model evaluation techniques Solid DevOps capabilities, including CI/CD pipelines and monitoring practices Experience operating in secure, regulated environments with strict governance controls Proven track record of integrating AI into production applications or workflows Knowledge of authentication, secrets management, and secure system design Nice to Have Experience with API gateways, service meshes, or GitOps tooling Familiarity with cloud platforms and services (compute, containers, storage, messaging) Experience building RESTful APIs (FastAPI or similar) and microservices architectures Exposure to infrastructure-as-code and automated deployment workflows Knowledge of prompt evaluation tooling Experience with both SQL and NoSQL databases Understanding of vector search and retrieval-based AI patterns Ways of Working Takes initiative and solves problems without needing heavy direction Comfortable navigating ambiguity in a fast-evolving technical landscape Strong communicator who can work effectively across technical and non-technical teams Curious and proactive in exploring new AI approaches responsibly Focused on automation and efficiency to improve delivery What Good Looks Like High-quality, secure AI services delivered into production Improved team productivity through automation and streamlined processes Reliable, observable systems with performance and evaluation built in Solutions that are easy for other teams to adopt and build upon Clear, accessible documentation supporting rapid onboarding