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# GenAI Architect - Generative AI & Agentic AI

- **Company:** Cognizant Technology Solutions Corporation
- **Location:** Charlotte, NC, United States
- **Experience:** Expert
- **Salary:** $117,000.0 - $135,000.0
- **Contract:** Permanent contract
- **Skills:** AI Evaluation, Application Programming Interfaces (APIs), Artificial Intelligence, Applications Architecture, Application Frameworks, Software Applications, Cloud Engineering, Software Quality, Continuous Integration, DevOps, Distributed Systems, Knowledge Management, Productivity Software, Cloud Services, Microsoft Copilot, Search Technologies, Software Deployment, Software Engineering, Enterprise Software Applications, GitHub Copilot, Retrieval-Augmented Generation, Large Language Models, Claude Code, Prompt Engineering, Model Validation, Generative AI, Agentic-AI, Build Management, AI Platforms, Kubernetes, Amazon Bedrock, Machine Learning Operations, Virtual Agents, Microservices
- **Published:** November 5, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=07934b33d0f0e2f4

## About the Role

* 10+ years of experience in solution architecture, application architecture, software engineering, AI/ML, or related technology disciplines.
 * Hands-on experience architecting and delivering enterprise Generative AI and LLM-based applications.
 * Proven experience building AI agents, copilots, RAG solutions, and LLM-powered workflows.
 * Strong knowledge of LLMs, prompt engineering, embeddings, vector search, retrieval strategies, agent orchestration, and model evaluation.
 * Experience with AWS cloud services, preferably Amazon Bedrock, and cloud-native application architectures.
 * Expertise with APIs, microservices, containers, CI/CD pipelines, DevOps practices, and distributed systems.
 * Strong understanding of AI security, responsible AI, governance, observability, and production monitoring.
 * Excellent communication and stakeholder management skills with the ability to influence technical and business teams., * Experience with Palantir, enterprise AI platforms, vector databases, or enterprise knowledge management platforms.
 * Experience designing and deploying production-grade Agentic AI systems using agent frameworks and orchestration platforms.
 * Knowledge of LLMOps, MLOps, AI evaluation frameworks, observability, and AI governance practices.
 * Hands-on experience with GitHub Copilot, Claude Code, or other AI-enabled developer productivity tools.
 * Experience creating AI reference architectures, reusable frameworks, or enterprise AI accelerators.
 * Background supporting highly regulated industries such as financial services, banking, insurance, or healthcare.
 * Experience leading AI transformation initiatives and promoting AI-first software engineering practices.

We're excited to meet people who share our mission and can make an impact in a variety of ways. Don't hesitate to apply, even if you only meet the minimum requirements listed. Think about your transferable experiences and unique skills that make you stand out as someone who can bring new and exciting things to this role.

## Description

As a GenAI Architect, you will make an impact by designing and delivering enterprise-scale Generative AI and Agentic AI solutions that accelerate business innovation and transform software engineering practices. You will be a valued member of our AI and Architecture team and collaborate closely with application engineering, cloud, data, security, DevOps, and business stakeholders to bring AI solutions from concept through production deployment., * Architect and deliver scalable Generative AI, LLM, Agentic AI, and Retrieval-Augmented Generation (RAG) solutions for enterprise applications.

 * Design and build AI-powered applications, intelligent agents, orchestration workflows, reusable services, APIs, and accelerators.
 * Develop architecture patterns for prompt engineering, context management, grounding, guardrails, model evaluation, and structured outputs.
 * Lead the adoption of AI-assisted software engineering tools such as GitHub Copilot, Claude Code, and similar platforms to improve developer productivity and software quality.
 * Establish best practices for responsible AI, LLMOps, observability, security, governance, CI/CD, and production monitoring while mentoring engineering teams and driving enterprise AI standards.