About Cigna: Cigna Healthcare is a general health service company dedicated to transforming healthcare. With roots in the U.S. and operations in over 30 countries, we serve more than 180 million customers and patients worldwide. Ranked 13th on the Fortune 500 in 2025, Cigna is recognized as one of the most trusted and influential names in the industry. Our mission is to improve the health, well-being, and peace of mind of those we serve. Join our globally recognized brand, where trust, communication, and a positive culture are at the core of everything we do. Our leadership is consistent, approachable, and supportive-ensuring your well-being and work-life balance. We're looking for individuals who thrive in collaborative environments, are passionate about meaningful change, and want to grow in a company that puts people first. At Cigna, you'll be part of a purpose-driven team that values innovation, compassion, and impact. Whether you're shaping better care experiences or supporting customers through life's key moments, your work will matter. Grow with us-and help shape the future of healthcare.

Role Summary

We are seeking an AI Architect to drive the architecture, design, and adoption of enterprise‑grade Generative AI (GenAI) solutions across the Cigna International Health. This role will drive innovation, productivity and operational improvements, and customer experience transformation by applying modern AI architectures in a secure, scalable, and responsible manner. The AI Architect will define end‑to‑end GenAI architectures, establish enterprise standards and reference patterns and layered guardrails. The role requires strong collaboration with product, security, privacy, legal, and cloud engineering teams,



particularly in regulated enterprise environments.

Key Responsibilities

Enterprise GenAI Architecture & Strategy

- Define and maintain the blueprints, reference architectures and design patterns for LLM‑based applications, covering model access, orchestration, retrieval, prompt management, evaluation, and observability.
- Translate business and product requirements into robust AI solution architectures that balance innovation, reliability, cost efficiency, and risk management.
- Act as a GenAI subject matter expert, advising senior leadership on AI strategy, feasibility, and adoption paths.

LLM, RAG & Agentic Workflows

- Architect knowledge bases and RAG architectures integrating structured and unstructured enterprise data sources.
- Establish agentic AI patterns, enabling multi‑step reasoning, tool usage, workflow orchestration, and human‑in‑the‑loop controls.
- Guide teams on prompt engineering, context management, and response grounding to improve accuracy, relevance, and user trust.
- Assist in the evaluation of commercial LLMs and SaaS AI platforms, selecting the right tools for different workloads.

AI Platforms, Gateways & Integration

- Awareness of AI Gateway / Model Access Layer to centralize access to LLMs and AI services, including:
- Authentication and authorization - Model routing and versioning
- Rate limiting, quotas,



and cost controls - Logging, auditability, and observability
- Establish secure connectivity to LLM endpoints in the enterprise cloud, documenting DNS, routing, IAM policies, firewall rules, and network controls.
- Integrate GenAI capabilities into existing enterprise systems and digital platforms.

Responsible AI, Security & Guardrails

- Recommend layered guardrails across GenAI systems
- Embed Responsible AI principles—fairness, transparency, explainability, and accountability—into architecture and delivery practices.
- Partner closely with security, privacy, legal, and compliance teams to meet enterprise governance standards and emerging AI regulations.
- Define monitoring and evaluation frameworks to detect hallucinations, drift, misuse, and operational risks.

Innovation, Productivity & Enablement

- Drive GenAI initiatives focused on developer productivity, operational efficiency, and enhanced customer experiences.
- Stay current with emerging AI trends, tools, and architectures, assessing their applicability to enterprise use cases.
- Mentor engineers and architects, establishing best practices, architectural standards, and reusable components.
- Contribute to internal communities of practice and AI capability building.

Required Skills & Expertise

- Large Language Models (LLMs), embeddings, prompt engineering - Retrieval-Augmented Generation (RAG), Knowledge bases and Multimodal embeddings
- Agentic AI workflows and tool calling - Model Context Protocol (MCP) concepts and context management
- Knowledge on foundational models and inference profiles, regional hosting and restrictions of various models and be able to i

📌 AI Architect (Madrid)
🏢 Cigna Health And Life Insurance
📍 Madrid