GCP AI Architect

Cognizant

Confirmed live 2 days ago High trust

Quick summary

Work type: On-site
Location: Dallas, TX
Posted: 22 days ago
Freshness: Confirmed live 2 days ago
Nearby: 99+ roles within 25 mi

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Similar $214k

$167k $273k

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This listing doesn't post a salary. Most similar roles pay $177,137–$250,625.

Based on 240 similar postings.

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About Cognizant

Cognizant is a multinational IT services and consulting company offering digital transformation, technology, and business process services. It serves clients across financial services, healthcare, manufacturing, and other industries worldwide.

Cognizant currently has 341 open roles on FindRole.

Listed pay typically runs $100,000–$133,500 across 219 roles with salary data.

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At a glance

TL;DR · GCP AI Architect

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The GCP AI Architect will design and implement scalable, modular generative AI architectures for production use. This role involves building customer-facing and internal products by translating requirements into AI-driven capabilities. Key responsibilities include developing RAG architectures, multi-agent systems, and workflow orchestration while managing low-latency inference, model routing, and fallback strategies. The candidate must utilize foundation models like GPT, Claude, and LLaMA, applying prompt engineering and agent-based frameworks. Technical requirements include proficiency in GCP, Kubernetes, CI/CD pipelines, and MLOps practices like model versioning and drift detection. The role requires expertise in data pipelines, vector databases, knowledge graphs, and unstructured data processing. Additionally, the architect must implement AI security guardrails, content filters, and responsible AI practices to ensure data privacy and compliance.

Skills

Generative AI LLMs RAG MLOps Kubernetes CI/CD GCP Vector Databases Prompt Engineering Microservices Data Pipelines Knowledge Graphs Model Versioning A/B Testing API-first Architecture Inference Optimization Feature Engineering

What you'll do

Design and implement scalable, modular Generative AI architectures for production use.
Develop RAG architectures using vector databases, semantic search, and embeddings indexing.
Build multi-agent systems and manage complex workflow orchestrations.
Integrate Generative AI capabilities into customer-facing and internal products.
Build data pipelines for unstructured data processing, document ingestion, and chunking.
Deploy and monitor models using MLOps practices, including versioning, CI/CD, and drift detection.
Implement AI guardrails, content filters, and security measures to mitigate prompt injection and data leakage.
Optimize inference performance and manage cost-control strategies in cloud-native environments.

What we're looking for

Strong understanding of Generative AI models, including LLMs, multimodal models, and embeddings.
Hands-on experience with foundation models and model adaptation techniques.
Expertise in prompt engineering, prompt orchestration, and agent-based frameworks.
Ability to design scalable, modular GenAI architectures including RAG, vector databases, and multi-agent systems.
Proficiency in data pipelines, feature engineering, and unstructured data processing.
Strong experience in cloud-native environments (GCP) with containerization, Kubernetes, and CI/CD pipelines.
Experience with MLOps practices including model versioning, deployment pipelines, and drift detection.