AI Architect

Cognizant; United States; Hybrid; USD 180,000 - 240,000; Full time

Job summary

Cognizant in Nashville, TN, seeks a senior AI Architect to lead design and deployment of scalable Agentic AI platforms. You will bridge client teams and engineering pods, owning the architectural blueprint, security, and production readiness on GCP.

Responsibilities include setting end-to-end architectures, aligning EA and AI CoE with cloud engineering and security boards, and guiding multi-agent workflows with offshore teams. Remote involvement with travel may be required.

Qualifications

• Production Agentic AI experience in live enterprise production environments.
• Deep GCP expertise (Vertex AI, Cloud Run/Functions, IAM).
• Strong enterprise IT governance, security, and compliance knowledge.
• Excellent communication and executive presence with client stakeholders.
• Solid software engineering background with CI/CD for ML and observability.

Responsibilities

• Technical Leadership & Blueprinting: Design and own the end-to-end architecture for production-grade Agentic AI systems, transitioning prototypes into secure, highly available enterprise applications.
• Stakeholder & Partner Alignment: Serve as the primary onsite technical liaison interfacing directly with the client’s Enterprise Architecture (EA) teams, AI Center of Excellence (AI CoE), Cloud Engineering, and Information Security boards.
• Cross-Partner Collaboration: Collaborate with external technology partners to establish best practices, integrate third-party APIs, and align on shared integration patterns.
• GCP Infrastructure Design: Architect robust AI infrastructure leveraging Vertex AI, Gemini models, Cloud Run, Cloud Storage, and secure deployment pipelines.
• Agentic Workflow Design: Guide offshore engineering pods in multi-agent orchestration, RAG pipelines, and automated evaluation frameworks.

Job description

Location: Nashville, Tennessee (Client Office) or Remote with travel on need basis

Role Overview:

We are seeking a highly experienced, client-facing AI Architect to lead the technical design and enterprise-wide deployment of scalable Agentic AI platforms. In this high-visibility role, you will act as the primary technical bridge between our engineering pods and the client’s internal technology teams. You will be responsible for defining the architectural blueprint, ensuring enterprise-grade security and reliability, and guiding complex multi-agent workflows into production on Google Cloud Platform (GCP).

Key Responsibilities:

• Technical Leadership & Blueprinting: Design and own the end-to-end architecture for production-grade Agentic AI systems, transitioning prototypes into secure, highly available enterprise applications.
• Stakeholder & Partner Alignment: Serve as the primary onsite technical liaison interfacing directly with the client’s Enterprise Architecture (EA) teams, AI Center of Excellence (AI CoE), Cloud Engineering, and Information Security boards.
• Cross-Partner Collaboration: Collaborate with external technology partners and marketplace vendors to establish best practices, integrate third-party APIs, and align on shared integration patterns.
• GCP Infrastructure Design: Architect robust AI infrastructure leveraging the GCP ecosystem, including Vertex AI, Gemini models, Cloud Run, Cloud Storage, and secure deployment pipelines.
• Agentic Workflow Design: Guide offshore engineering pods in the development of complex multi-agent orchestration (e.g., LangGraph, LangChain), RAG pipelines, and automated evaluation frameworks.

Required Qualifications:

• Production Agentic AI Experience: Proven, hands-on track record of successfully architecting, building, and deploying Agentic AI / Generative AI systems into live enterprise production environments (not just prototypes or hackathons).
• Deep GCP Cloud Expertise: Extensive experience with Google Cloud Platform, specifically Vertex AI, serverless deployments (Cloud Run/Functions), and IAM/Security configurations.
• Enterprise Architecture Acumen: Strong understanding of enterprise IT governance, network security, and compliance guardrails required to deploy AI in highly regulated environments.
• Communication & Executive Presence: Exceptional ability to translate complex AI engineering concepts into strategic business value for executive stakeholders, while negotiating technical trade-offs with client EAs and security officers.
• Core Engineering Background: Solid foundation in modern software engineering practices, CI/CD for machine learning (MLOps/LLMOps), API design, and observability (e.g., LangSmith).