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# GCP AI Architect (Remote)

- **Company:** Cognizant Technology Solutions Corporation
- **Location:** Dallas, TX, United States
- **Salary:** $160,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Cloud Computing, Information Leak Prevention, Graph Database, Machine Learning, Metadata, Inference Optimization, Search Technologies, Unstructured Data, Workflow Management Systems, AI Infrastructure, Data Logging, Feature Engineering, Retrieval-Augmented Generation, Delivery Pipeline, Large Language Models, Multi-Agent Systems, Prompt Engineering, IT Architecture, Generative AI, Indexer, Knowledge Engineering, Containerization, Kubernetes, Machine Learning Operations, Claude, Drift Detection, Api Design, Prompt Orchestration, GPT, Software Version Control, Data Pipelines, Unsupervised Learning, Microservices
- **Published:** October 26, 2026
- **Apply:** https://www.dallasjobsite.com/job.asp?id=3466850140&tx=TT434THZ&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Strong understanding of Generative AI models (LLMs, multimodal models, embeddings).
 * Hands-on experience with foundation models (e.g., GPT-style, Claude-style, LLaMA-style) and model adaptation techniques.
 * Expertise in prompt engineering , prompt orchestration , and agent-based frameworks .
 * Solid grounding in machine learning fundamentals , including supervised/unsupervised learning, evaluation metrics, and inference optimization.

AI Architecture & System Design

 * Ability to design scalable, modular GenAI architectures for production use.
 * Experience with:
 * RAG (Retrieval-Augmented Generation) architectures
 * Vector databases (semantic search, embeddings indexing)
 * Multi-agent systems and workflow orchestration
 * Strong understanding of low-latency inference , model routing , and fallback strategies .
 * Knowledge of event-driven , microservices , and API-first architectures .

Product Engineering & Integration

 * Experience integrating GenAI capabilities into customer-facing and internal products .
 * Ability to translate product requirements into AI-driven capabilities and technical designs.
 * Familiarity with A/B testing , feature flags, and iterative product releases involving AI.

Data & Knowledge Engineering

 * Proficiency in data pipelines , feature engineering , and unstructured data processing .
 * Experience with:
 * Knowledge graphs
 * Metadata-driven architectures
 * Document ingestion and chunking strategies
 * Strong understanding of data quality, provenance, and governance for AI systems.

Cloud, MLOps & Platform Skills

 * Strong experience in cloud-native environments (GCP).
 * Familiarity with MLOps practices , including:
 * Model versioning
 * Deployment pipelines
 * Monitoring, logging, and drift detection
 * Experience with containerization , Kubernetes , and CI/CD pipelines.
 * Knowledge of inference optimization and cost-control strategies.

Security, Privacy & Responsible AI

 * Understanding of AI security risks (prompt injection, data leakage, model abuse).
 * Experience implementing guardrails , content filters , and policy enforcement .
 * Knowledge of responsible AI practices , including explainability, bias mitigation, and compliance.
 * Familiarity with data privacy regulations (e.g., GDPR, enterprise governance standards).