AI Solutions Architect

2 days ago

Bengaluru Rural District Karnataka, India Saaki Argus And Averil Consulting Full time ₹ 30,00,000 - ₹ 50,00,000 per year

About the company:

A global technology company specializing in digital transformation services for the telecommunications, media, and technology (TMT) industries. It focuses on AI, automation, cloud, and IoT to drive innovation and operational efficiency.

Role: AI Solutions Architect

Location: Bangalore only

Hiring Mode - C2H only

Interview 2 rounds

Work mode :In office (Bangalore ) 60 Days WFH per year

Shift time General

Exp 8 Years - 10 yrs

Skill LLM (Mandatory), RAQ (M), Python (M) & Gen AI

Overview

We are seeking an experienced AI Solutions Architect to design and implement enterprise-grade AI systems. The ideal candidate will architect end-to-end AI solutions involving LLM integration, RAG pipelines, and multi-agent frameworks while ensuring scalability, security, and performance across cloud platforms. You will work closely with product and engineering teams to convert business needs into technical blueprints and AI-driven solutions.

Key Responsibilities
  • Design and architect end-to-end AI solutions including LLM integration, RAG pipelines, and multi-agent orchestration.
  • Develop scalable frameworks for cloud deployment (AWS/Azure/GCP) and enterprise systems.
  • Define technical standards and evaluate emerging AI technologies for adoption.
  • Collaborate with cross-functional teams to convert business requirements into robust system architectures.
  • Build architecture blueprints, documentation, and deployment strategies.
  • Ensure security, compliance, and performance optimization throughout AI workflows.
  • Implement API gateway design and microservices-based system integration patterns.
  • Conduct performance tuning and system evaluation for large-scale AI applications.
Required Skills & Experience
  • Enterprise AI architecture and solution design
  • LLM fine-tuning, deployment, and integration
  • RAG system design & scalable vector databases
  • Cloud platforms: AWS / Azure / GCP
  • Microservices architecture & API gateway implementation
  • Knowledge of security frameworks and compliance standards
  • System integration patterns and performance optimization
  • Technical documentation and stakeholder communication

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