Gen AI & Agentic AI Architect

Cognizant; Gurugram District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Cognizant is seeking an experienced GenAI & Agentic AI Architect to lead the design and deployment of advanced AI solutions across Pan India. The role requires hands-on expertise in Azure and/or AWS, containerization, observability, and modern AI frameworks.

The candidate will architect multi-agent systems, implement RAG architectures, and drive secure, scalable AI deployments with robust MLOps practices. Immediate to 90 days notice period applies, client-facing delivery focus.

Qualifications

• 8+ years in a technical architecture role.
• Proven expertise in Azure and/or AWS cloud architectures.
• Hands-on experience with Docker and Kubernetes.
• Proficiency in Python or R with ML frameworks.

Responsibilities

• Architect scalable Generative AI solutions on Azure/AWS.
• Build containerized AI apps and manage Kubernetes deployments.
• Establish observability for LLMs including performance and health.
• Develop ML models with TensorFlow, PyTorch, or scikit-learn.
• Leverage cloud-native AI services like SageMaker, Azure AI, or Vertex AI.
• Design multi-agent systems and RAG architectures.
• Lead MLOps pipelines from training to deployment and monitoring.
• Collaborate with teams to embed AI in enterprise products.
• Drive performance optimization and troubleshooting of AI apps.

Skills

GenAI architecture

Azure cloud

AWS cloud

Docker

Kubernetes

Python

R

MLOps

LLM observability

Tools

TensorFlow

PyTorch

scikit-learn

SageMaker

Azure AI

Vertex AI

Job description

About The Role

We are seeking a highly experienced

Role: Gen AI & Agentic AI Architect

Location : Pan India

Experience : 6 to 15 Years

Notice Period : Immediate to 90 days

Mode of Interview : In Person

Gen AI & Agentic AI Architect

About The Role

We are seeking a highly experienced GenAI & Agentic AI Architect with strong hands‑on expertise to lead the design, development, and implementation of advanced AI solutions for our clients. This role requires a seasoned technical leader with over 8 years of architecture experience and deep knowledge of Azure and/or AWS cloud ecosystems, combined with strong capabilities in containerization, orchestration, observability, and modern AI frameworks.

You will play a critical role in shaping next‑generation AI systems, leveraging Generative AI and Agentic AI paradigms to build scalable, intelligent, and action‑oriented solutions.

Key Responsibilities

• Architect and develop scalable, high-performance Generative AI solutions on Azure and/or AWS platforms
• Design, build, and manage containerized AI applications using Docker and orchestrate deployments via Kubernetes
• Establish and maintain observability frameworks for LLMs, including performance monitoring, system health, and usage analytics
• Develop and implement machine learning models using Python or R, leveraging frameworks such as TensorFlow, PyTorch, or scikit-learn
• Build and deploy AI/ML solutions using cloud-native services such as AWS SageMaker, Azure AI, or Google Vertex AI
• Design and implement multi-agent systems and Retrieval-Augmented Generation (RAG) architectures
• Define and enforce standards for prompt engineering, vector search, and model orchestration
• Build and manage enterprise‑grade MLOps pipelines covering model training, fine‑tuning, evaluation, deployment, and monitoring
• Collaborate with cross‑functional teams to integrate AI capabilities into enterprise products and platforms
• Lead system optimization, performance tuning, and troubleshooting of AI‑driven applications
• Establish best practices for secure, scalable, and maintainable AI architectures
• Mentor and guide AI engineering teams on emerging technologies and implementation best practices
• Stay current with advancements in Generative AI, Agentic AI, and cloud technologies

Required Skills & Qualifications

• Minimum 8+ years of experience in a technical architecture role
• Proven expertise in Azure and/or AWS cloud architectures
• Strong hands‑on experience with Docker and Kubernetes
• Proficiency in Python or R with experience in TensorFlow, PyTorch, or scikit-learn
• Deep knowledge of MLOps practices and model deployment on AWS SageMaker, Azure AI, or Google Vertex AI
• Experience with multi-agent orchestration and RAG architectures
• Solid understanding of prompt engineering, vector search, and LLM observability frameworks
• Experience with Spring Boot and microservices architecture
• Strong understanding of enterprise AI security, scalability, and governance principles