Role Description This is a high-stakes, execution-focused role within the Transformation Office. We are looking for a "day-one" engineer to own the production lifecycle of our AI initiatives. Your mission is to build the automated infrastructure that bridges our legacy data systems with modern AWS and Azure AI services. You will be responsible for the "Ops" of AI: ensuring that LLM applications, RAG pipelines, and traditional ML models are deployable, observable, and scalable in a multi-cloud environment. Key Responsibilities - Multi-Cloud Pipeline Execution: Build and maintain automated CI/CD and CT (Continuous Training) pipelines across AWS (SageMaker/Bedrock) and Azure (AI Studio). - LLMOps Framework Implementation: Design and execute the infrastructure for Retrieval-Augmented Generation (RAG), including vector database management (OpenSearch, Pinecone, or Azure AI Search) and semantic index optimization. - Legacy Data Connectivity: Build the engineering "pipes" to securely ingest and move data from legacy systems (Mainframes, SQL Server, on-prem DBs) into cloud-native MLOps workflows. - Automated Model Evaluation: Implement systemized frameworks for LLM evaluation (LLM-as-a-judge, ROUGE, METEOR) and traditional ML validation to ensure performance before deployment. - Observability & Monitoring: Deploy real-time monitoring for model drift, hallucination detection, latency, and token consumption to manage both quality and cost. - Infrastructure as Code (IaC): Manage all AI resources using Terraform or CloudFormation, ensuring the cloud posture is reproducible, secure, and follows a "Privacy by Design" mandate. - Advanced Analytics Integration: Partner with teams using platforms like Palantir, Databricks, or Snowflake to ensure a high-fidelity data flow between analytical ontologies and production models. - IT & Security Diplomacy: Work directly with central IT and Security to navigate IAM roles, VPC peering, and firewall configurations, clearing the path for rapid transformation. - Scalable Inference Engineering: Optimize model serving endpoints for high-throughput and low-latency, utilizing containerization (Docker/Kubernetes) and serverless architectures where appropriate. - Prompt & Model Versioning: Establish rigorous version control for prompts (PromptOps), model weights, and data snapshots to ensure 100% auditability and rollback capability. - Data Science Engineering: Support the data science lifecycle by automating feature stores, feature engineering pipelines, and the transition of experimental notebooks into hardened production microservices. - Security & Compliance Hardening: Implement automated scanning and guardrails (e.g., Bedrock Guardrails or Azure Content Safety) to prevent prompt injection and data leakage. Qualifications - Education: Bachelor’s degree in computer science or a related field required; master’s degree in a quantitative discipline highly desirable. - Proven Execution: 6+ years of engineering experience, with a minimum of 3 years strictly focused on MLOps or LLMOps in a production environment. - AWS & Azure Mastery: Deep, hands-on proficiency in both ecosystems. You must be able to configure Bedrock and Azure OpenAI services, including private networking and endpoint security, on day one. - Technical Stack: Expert Python, SQL, and PySpark. Extensive experience with containerization (Docker, Kubernetes) and orchestration tools (Airflow, Kubeflow, or Step Functions). - LLM Tooling: Professional experience with evaluation and observability frameworks like LangSmith, Arize Phoenix, or WhyLabs. - Data Science Flavor: A strong understanding of statistical validation, model evaluation metrics, and the ability to partner with Data Scientists to optimize model performance. - Transformation Mindset: The ability to move at the speed of a startup while maintaining the collaborative relationships required to function within a large-scale enterprise IT landscape. Working Conditions - May on occasion be exposed to loud sounds and distracting noise levels, such as from office equipment. - The ability to lift up to 30lbs. - Use of computers and technology. Benefits - Health care and 401(k) savings plans.

• You will be one of the team's primary technical references, responsible for creating and evolving generative AI solutions for conversational products and for optimizing operational efficiency. • In addition to hands-on work in modeling, data analysis and prompt engineering, you will play an active role in defining technical standards, architectural decisions, and integration between science and engineering to scale our AI solutions with quality and impact. • We work in an agile model, constantly seeking results through experiments and short validation cycles. We are looking for someone who enjoys and is comfortable working in a dynamic, metrics-driven environment with excellent engineering and data science practices.

• Design and implement evaluation systems and tooling to validate Oura’s custom AI models and Advisor • Develop novel evaluation methods to measure grounding, reliability, and actionability of LLM and agentic systems • Build and optimize custom AI models through fine-tuning, knowledge distillation, and quantization • Partner with Product and Engineering teams to balance cutting-edge research with practical engineering impact • Lead initiatives that reduce Oura’s dependency on external AI SaaS providers and accelerate model development

ABOUT QUANTUMLOOPAI QuantumLoopAI is transforming how patients access healthcare. Our AI-powered reception system, EMMA, serves millions of patients, helping GP surgeries manage demand, reduce phone queues, and ensure patients get the right care at the right time. Backed by leading healthcare investors and working with stakeholders across England, we are building the future of primary care access. As we prepare for our Series A and expand our team, we are looking for exceptional people who want to make a genuine difference to healthcare. ABOUT THE ROLE As our AI/ML & Prompt Engineer, you will own the intelligence behind EMMA — the LLM and RAG systems that power our chatbot, voice agent and telephony services. You will design, build and optimise the AI models and prompt architectures that allow EMMA to understand patient needs, retrieve accurate clinical information, and respond naturally across text and voice channels. This is a hands-on, high-impact role where your work will directly shape patient outcomes across hundreds of GP surgeries. WHAT YOU WILL DO - Architect and deploy LLM- and RAG-based systems that ingest data sources, generate accurate responses, and power both chatbot and voice agent interactions. - Design, iterate and optimise prompts and voice-agent dialogue flows to maximise response relevance, reduce latency and ensure clinical safety across text and voice channels. - Partner with frontend (Next.js, React) and backend (NestJS, Python) teams to integrate AI and voice agent components into our Azure-hosted microservices architecture. - Instrument AI and voice-agent pipelines, analyse logs and user feedback, troubleshoot edge cases, and implement continuous-learning improvements. - Stay abreast of the latest advances in LLMs, RAG, conversational AI frameworks, and (GDPR, DTAC) to inform our technical roadmap. - Define and execute rigorous test plans in collaboration with QA to validate model accuracy, voice-agent performance and compliance with healthcare standards. - Mentor junior engineers and advocate for best practices in MLOps, prompt engineering, voice-agent design and model governance.