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AI Architect
The Cigna Group. Define and maintain blueprints, reference architectures, and design patterns for LLM-based applications .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in architecting AI solutions using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques, while ensuring compliance with data privacy and legal standards. Proficient in integrating AI capabilities into enterprise systems and mentoring teams on best practices in AI architecture and implementation.
Highest-signal resume keywords
Large Language Models (LLMs)Retrieval-Augmented Generation (RAG)AI Gateway/Model Access LayerPrompt EngineeringExperience with Azure, AWS, or GCP
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Prompt EngineeringContext ManagementFine-Tuning StrategiesDocument Intelligence PlatformsAI ObservabilityEnd-to-End AI PipelinesSecure API DesignNLP TechniquesKnowledge of Foundational ModelsExperience with Vector Databases
Soft Skills
Strong Communication Skills
Tools & Technologies
Azure Form RecognizerAWS TextractGoogle Document AILangChainLangGraph
Certifications & Qualifications
Advanced Degree in Computer ScienceAdvanced Degree in Artificial IntelligenceAdvanced Degree in Machine Learning
Industry Keywords
Generative AIIntelligent Document Processing (IDP)Compliance-Sensitive EnvironmentsHealthcareFinanceInsurance
Tech Stack
Tools & technologiesAWSAzureCloudDNSGoogle Cloud Platform
About the role
Key responsibilities & impact- Define and maintain blueprints, reference architectures, and design patterns for LLM-based applications
- Translate business and product requirements into robust AI solution architectures
- Advise senior leadership on AI strategy, feasibility, and adoption paths
- Architect knowledge bases and RAG architectures using structured and unstructured enterprise data
- Establish agentic AI patterns with multi-step reasoning, tool usage, workflow orchestration, and human-in-the-loop controls
- Guide teams on prompt engineering, context management, and response grounding
- Evaluate commercial LLMs and SaaS AI platforms
- Establish AI Gateway/model access layer capabilities, including authentication, authorization, routing, versioning, rate limiting, quotas, cost controls, logging, auditability, and observability
- Establish secure connectivity to LLM endpoints in the enterprise cloud and document DNS, routing, IAM, firewall, and network controls
- Integrate GenAI capabilities into enterprise systems and digital platforms
- Recommend layered guardrails and embed Responsible AI principles into architecture and delivery
- Partner with security, privacy, legal, and compliance teams on governance and AI regulations
- Define monitoring and evaluation frameworks for hallucinations, drift, misuse, and operational risks
- Drive GenAI initiatives for developer productivity, operational efficiency, and customer experience
- Assess emerging AI trends, tools, and architectures for enterprise use cases
- Mentor engineers and architects and establish best practices, standards, and reusable components
- Contribute to communities of practice and AI capability building
Requirements
What you’ll need- 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 of foundational models and inference profiles, including regional hosting and model restrictions
- Fine-tuning strategies, adapters, and inference optimization
- Awareness and proficiency in designing and implementing AI Gateway/model access layers and access controls
- Experience architecting solutions with vector databases and semantic search
- Experience with document intelligence platforms such as Azure Form Recognizer, AWS Textract, or Google Document AI
- Knowledge of Intelligent Document Processing (IDP) solutions
- Familiarity with workflow orchestration frameworks such as LangChain or LangGraph
- Understanding of OCR, NLP techniques, and entity linking
- Awareness of data validation, quality checks, and document lifecycle management
- End-to-end AI pipelines covering ingestion, retrieval, generation, evaluation, and rollout
- 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 data, privacy, and legal standards across geographical regions
- Experience with Azure, AWS, or GCP and their AI services
- Experience architecting end-to-end AI solutions using 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
- 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 in regulated or compliance-sensitive environments such as healthcare, finance, insurance, or global enterprises
- Strong communication skills with technical and non-technical stakeholders
Benefits
Comp & perks- Competitive salary
- Multicultural and hybrid working environment
- Private Medical Insurance
- Employee Wellbeing Benefits
- Educational Development Program