Remote GenAI Architect: LLMs, RAG & MLOps Lead

RemoteFetch; Dallas (TX); Remote; USD 160,000 - 200,000; Full time

Benefits offered by this job

Medical/Dental/Vision/Life Insurance

Paid holidays plus Paid Time Off

401(k) plan and contributions

Long-term/Short-term Disability

Paid Parental Leave

Employee Stock Purchase Plan

Job summary

Cognizant is seeking an AI Architect to design and implement scalable GenAI architectures for production use, leveraging LLMs, RAG, embeddings, and multi-agent frameworks. You will translate product needs into AI-enabled capabilities and drive integration across customer-facing and internal products.

Responsibilities include building data pipelines, knowledge graphs, and modular, API-first systems, plus overseeing MLOps, containerization with Kubernetes, and cost-aware inference.

Qualifications

• Deep knowledge of Generative AI platforms and architectures.
• Experience building scalable GenAI systems for production.
• Strong background in data pipelines, security and governance.

Responsibilities

• Design scalable GenAI architectures for production use.
• Translate product requirements into AI-enabled capabilities and designs.
• Lead integration of GenAI into customer-facing and internal products.

Skills

GenAI concepts

Prompt engineering

LLMs & multimodal models

Foundation models adaptation

RAG architectures

Vector databases & embeddings

Multi-agent systems

Machine learning fundamentals

Data pipelines & governance

Security & responsible AI

System design & scalability

API-first architectures

MLOps practices

Cost optimization & inference

Tools

Kubernetes

CI/CD pipelines

Containerization

GCP / cloud-native

Monitoring & logging

Job description

Cognizant is seeking an AI Architect to design and implement scalable GenAI architectures for production use, leveraging LLMs, RAG, embeddings, and multi-agent frameworks. You will translate product needs into AI-enabled capabilities and drive integration across customer-facing and internal products.

Responsibilities include building data pipelines, knowledge graphs, and modular, API-first systems, plus overseeing MLOps, containerization with Kubernetes, and cost-aware inference.