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Posted 09 July, 2026

AI Solutions Architect

Saaki Argus & Averil Consulting
Bengaluru, KA, IN Full Time
Reference: e133bb77144761ad

Job Description

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About the company:

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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.

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Role: AI Solutions Architect

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Location: Bangalore only

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Hiring Mode - C2H only

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Interview 2 rounds

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

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Shift time General

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Exp 8 Years - 10 yrs

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

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Overview \n

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.

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