The AI Solution Architect will own the end-to-end architecture for Artificial Intelligence, Machine Learning, Generative AI, Agentic AI, and Intelligent Automation solutions. The role will bridge business requirements with enterprise technology architecture, ensuring AI solutions are scalable, secure, reusable, and aligned with business objectives.

# Key Responsibilities

## Enterprise AI Architecture

- Define the enterprise AI architecture roadmap aligned with the organization's Digital & Data strategy.
- Develop AI reference architectures, reusable patterns, and architecture principles.
- Standardize AI design patterns for enterprise-wide adoption.
- Build scalable, modular, API-first AI architectures.
- Ensure interoperability between AI platforms and enterprise applications.
- Establish architecture governance and review mechanisms.

## AI Solution Design

Lead architecture and design for enterprise AI initiatives including:

- AI-powered Customer Service
- AI Copilots
- AI Sales Assistants
- Loan Underwriting Assistants
- Intelligent Document Processing
- Customer 360 Intelligence
- Collections Intelligence
- Fraud Detection
- Risk Analytics
- Enterprise Search
- Knowledge Management
- Decision Intelligence
- Voice AI
- Conversational AI
- Hyper-personalization Engines

Prepare High-Level Designs (HLD), Low-Level Designs (LLD), architecture diagrams, API specifications, and deployment blueprints.

## Generative AI & Agentic AI Architecture

Architect enterprise solutions using

- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- AI Agents
- Multi-Agent Systems
- Prompt Engineering Frameworks
- Semantic Search
- Vector Databases
- Knowledge Graphs
- AI Orchestration Frameworks
- AI Workflow Engines




- Model Context Protocol (MCP)

Evaluate and select appropriate models and frameworks based on business use cases.

## Enterprise Integration Architecture

Design AI integrations with

- CRM
- Loan Origination System (LOS)
- Loan Management System (LMS)
- Mobile Applications
- Customer Portals
- Enterprise Data Lake
- Data Warehouse
- Contact Centre Platforms
- Marketing Automation Platforms
- API Gateway
- Enterprise Service Bus (ESB)
- Payment Platforms
- Third-party FinTech APIs

Ensure event-driven, API-first, and microservices-based integration.

## AI Platform Architecture

Collaborate with AI Platform and MLOps teams to design:

- AI Platform Architecture
- Model Serving Infrastructure
- Feature Stores
- Model Registry
- Vector Database Architecture
- AI API Gateway
- AI Security Layer
- AI Monitoring Framework
- Experiment Tracking
- Model Lifecycle Management

Ensure platform scalability, resilience, and high availability.

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## Cloud & Infrastructure Architecture

Design AI solutions leveraging

- Microsoft Azure (Preferred)
- Azure OpenAI
- Azure AI Foundry / AI Studio
- Azure Machine Learning
- Azure Kubernetes Service (AKS)
- Azure Data Lake
- Databricks
- Azure API Management

Knowledge of AWS Bedrock, SageMaker, and Google Vertex AI is desirable.

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## Security & Responsible AI





Work with Information Security and AI Governance teams to ensure:

- Secure AI architecture.
- Identity & Access Management (IAM).
- Data encryption and key management.
- AI model security.
- Prompt security.
- Responsible AI implementation.
- Compliance with RBI guidelines, DPDP Act, and enterprise security standards.
- Explainability and auditability of AI solutions.

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## Technology Evaluation & Innovation

- Evaluate emerging AI technologies and platforms.
- Define technology standards and selection criteria.
- Conduct Proof of Concepts (PoCs) and architecture assessments.
- Recommend open-source and commercial AI technologies.
- Drive innovation through reusable frameworks and accelerators.

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## Architecture Governance

- Lead architecture review boards for AI initiatives.
- Review solution designs and ensure compliance with enterprise standards.
- Define AI coding, deployment, and integration standards.
- Maintain AI architecture documentation and repositories.
- Mentor engineering teams on architectural best practices.

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## Stakeholder Management

Act as the primary technical advisor for enterprise AI initiatives.

# Educational Qualifications

- Bachelor's degree in Computer Science, Information Technology, Engineering, Artificial Intelligence, or a related field.

# Experience

- 10–15 years of experience in enterprise solution architecture or application architecture.
- Minimum 5 years in AI/ML or advanced analytics solution design.
- Hands-on experience architecting enterprise Generative AI solutions.
- Experience in Banking, NBFC, Financial Services, Insurance, or FinTech is highly preferred.

📌 Assistant Vice President - Application (Mumbai)
🏢 HDB Financial Services
📍 Mumbai