AI Deployment Architecture

Cognizant; Hyderabad, Chennai District, Bengaluru; Hybrid; INR 4,000,000 - 6,500,000; Full time

Job summary

Cognizant in Hyderabad is seeking an experienced Solution Architect to design and implement AI deployment architectures for scalable enterprise solutions. The role focuses on end-to-end platforms that optimize data-driven decisions and align with business goals and regulatory standards, with preferred experience in sales and marketing domains to boost engagement and revenue.

The candidate will collaborate across product, engineering and data teams to translate requirements into architectural

Qualifications

• Extensive experience in AI deployment architecture including scalable inference and model lifecycle.
• Strong data engineering concepts with pipelines, feature stores and metadata management.
• Hybrid and cloud environments with containerization, orchestration and automation.
• Apply sales/marketing domain knowledge to drive commercial AI outcomes.
• Production readiness, monitoring and retraining strategies for AI systems.
• Excellent communication with technical and business stakeholders and clear tradeoffs.

Responsibilities

• Design AI deployment architectures for scalable enterprise solutions.
• Translate requirements into technical blueprints and governance standards.
• Define data ingestion, model training, serving and monitoring processes.
• Evaluate cloud services, containers and orchestration for cost and resiliency.
• Guide teams on security controls and regulatory compliance for AI.
• Create reference architectures and reusable patterns for adoption.
• Collaborate with sales, marketing and operations on AI-driven insights.
• Document decisions, integration patterns and non-functional requirements.
• Review designs, deployment plans and CI/CD pipelines.
• Mentor junior architects and engineers on best practices.

Skills

AI deployment

Cloud platforms

Data pipelines

Security & governance

Stakeholder communication

Education

Bachelor's in CS/CE

Tools

AWS

Azure

GCP

Kubernetes

CI/CD

Job description

Job Summary

This hybrid day shift role seeks an experienced solution architect with deep expertise in AI deployment architecture to design and implement scalable enterprise solutions for a global organization. The architect will shape end to end platforms that optimize data driven decision making and operational efficiency while aligning technology outcomes with strategic business goals and regulatory standards. Experience in sales and marketing domains is preferred to enhance customer engagement and reven

Responsibilities

• Design comprehensive AI deployment architectures that enable robust model lifecycle management scalable inference and secure integration with enterprise platforms ensuring reliable performance for global business operations.
• Collaborate with cross functional product engineering and data teams to translate complex business requirements into clear technical blueprints that streamline delivery and improve time to value for AI powered solutions.
• Define standards for data ingestion model training model serving and monitoring processes that promote reproducibility transparency and long term sustainability across the AI solution landscape.
• Evaluate and select appropriate cloud services container platforms and orchestration mechanisms to optimize cost performance and resiliency for AI workloads in a hybrid work environment.
• Guide teams in implementing secure access controls and governance frameworks that protect data and models while complying with regional regulations and internal policies for responsible AI usage.
• Create reference architectures and reusable patterns that simplify future solution design reduce implementation risk and accelerate adoption of AI capabilities throughout the organization.
• Partner with stakeholders from sales marketing and operations to identify opportunities where AI driven insights can improve customer engagement campaign effectiveness and revenue forecasting.
• Document architecture decisions integration patterns and non functional requirements in a clear manner that can be easily understood by technical teams and business partners across regions.
• Review solution designs implementation plans and deployment pipelines to ensure alignment with architectural principles performance objectives and maintainability criteria.
• Coordinate with infrastructure and DevOps teams to design automated CI CD pipelines for AI services improving deployment frequency rollback safety and observability in production environments.
• Provide guidance on evaluation of vendor platforms open source tools and frameworks to build future ready AI ecosystems that balance innovation with operational stability.
• Support troubleshooting of complex production issues by analyzing logs metrics and dependencies to drive timely resolutions and continuous improvement of AI systems.
• Mentor junior architects and engineers by sharing best practices on AI solution design documentation techniques and stakeholder communication to foster architectural excellence.

Qualifications

• Possess extensive experience in AI deployment architecture including design of scalable inference services model lifecycle management and integration with enterprise applications.
• Demonstrate strong understanding of data engineering concepts such as data pipelines feature stores and metadata management that support reliable and efficient AI model operations.
• Bring practical experience in hybrid and cloud environments with proficiency in containerization orchestration and automation tools that enable resilient AI service deployments.
• Apply knowledge of sales and marketing domain processes such as lead management campaign optimization and customer segmentation to design AI solutions that enhance commercial outcomes.
• Show proficiency with common AI and machine learning frameworks and tools focusing on production readiness monitoring and retraining strategies rather than only experimentation.
• Communicate effectively with technical and business stakeholders tailoring explanations to varied audiences and ensuring shared understanding of architecture choices and tradeoffs. 1. Solution Design & Architecture

Define the overall architecture of the solution including frameworks components and integration points.

Create technical solution blueprints that address high-level business and technical requirements.

Ensure compliance with industry best practices internal standards and security guidelines.

echnology Selection & Integration

2. Technology Selection & Integration

Evaluate and select appropriate technologies frameworks and platforms for scalability cost-efficiency and maintainability.

Design system integration plans for seamless communication between APIs databases and services

3. Skills & Competencies

Strong knowledge of cloud platforms (AWS Azure GCP) and integration patterns.

Expertise in problem-solving frameworks DevOps practices and security compliance.

Excellent communication and leadership skills to guide cross-functional teams.