AI Architect - Cigna Healthcare

About Cigna:

Cigna Healthcare is a global 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:

o Authentication and authorization - Model routing and versioning

o Rate limiting, quotas, and cost controls - Logging, auditability, and observability

o Establish secure connectivity to LLM endpoints in the enterprise cloud,

documenting DNS, routing, IAM policies, firewall rules, and network controls.

o 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:

Generative AI & Core Concepts:

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 identify the right model for the right requirements.

Fine-tuning strategies, adapters, and inference optimization

AI Architecture & Tooling

Awareness of AI Gateway / model access layer design and access controls on the gateways.

Proficiency in designing and implementing AI Gateway/model access layers to facilitate secure and scalable access to AI models.

Experience in architecting solutions with vector databases and semantic search for efficient retrieval of information from large volumes of unstructured data.

Skilled in leveraging document intelligence platforms (e.g., Azure Form Recognizer, AWS Textract, Google Document AI) for document classification, data extraction, and entity recognition from varied formats including scanned documents, PDFs, and handwritten forms.

Knowledge of Intelligent Document Processing (IDP) solutions for automated ingestion, parsing, and validation of unstructured documents, ensuring accurate routing of documents to appropriate business workflows.

Familiarity with workflow orchestration frameworks (such as LangChain, LangGraph, or equivalent) to automate end-to-end document processing pipelines, including document routing, exception handling, and integration with downstream systems.

Understanding of optical character recognition (OCR), natural language processing (NLP) techniques, and entity linking for extracting actionable insights from diverse data types.

Awareness of data validation, quality checks, and document lifecycle management best practices to ensure compliance and reliability in document processing solutions.

End-to-end AI pipelines: ingestion, retrieval, generation, evaluation and rollout.

Responsible AI & Governance

Prompt-injection mitigation and jailbreak prevention

Content safety, moderation, and policy enforcement

Human-in-the-loop architectures

AI observability, monitoring, and evaluation frameworks

Familiarity with MLOps / LLMOps practices

Awareness of various data, privacy and legal standards across different geographical regions and ensuring the solution stays compliant with them.

Cloud & Enterprise Engineering

Experience architecting solutions with cloud platforms (Azure, AWS, or GCP) and their AI services

Experience in architecting end-to-end AI solutions using various cloud services integrated with AI tools and runtimes.

Secure API design, IAM, networking, and CI/CD integration

Designing systems for scale, resilience, and global deployment

Qualifications

Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, or a related

field.

10+ years of experience building and architecting large-scale software or AI systems in

medium-to-large enterprises.

5+ years of experience with AI/ML, NLP, or Generative AI

Experience working in regulated or compliance-sensitive environments (e.g., healthcare,

finance, insurance, global enterprises).

Strong communication skills with the ability to influence technical and non-technical

stakeholders.

Why This Role Matters

This role is critical to scaling Generative AI responsibly across the enterprise, ensuring that

innovation del