New opportunity

Sr. Architect, AI Data Integration

Vantive · Bengaluru, India

About this role

Vantive is seeking a visionary and hands-on Enterprise Data Integration Architect to join the Integration Architecture team. This role serves as the dedicated integration representative partnering with the Data & AI organization to shape and execute enterprise-wide integration strategies for data and AI initiatives. The position focuses on designing scalable, secure, and high-performance integration architectures using cloud-native platforms on AWS and Azure to enable advanced analytics, machine learning, and AI capabilities. Responsibilities include defining integration strategies, evaluating modern technologies like event-driven and API-led patterns, and ensuring seamless data flow across systems. The architect will lead proof-of-concept initiatives, mentor developers, and act as a primary liaison between integration and AI teams. Required qualifications include a Bachelor’s or Master’s degree in Computer Science or a related field and 10 or more years of experience in enterprise integration architecture, with at least 3 years specifically in data-centric integrations. Candidates must have hands-on experience building or enabling AI agents, working with agent frameworks, integrating LLMs via APIs, and familiarity with RAG architectures, vector databases, and prompt management. Proficiency with AWS services such as Glue, Kinesis, Lambda, and Redshift is essential. Preferred qualifications include experience in regulated industries like healthcare or finance, knowledge of AI/ML pipelines, and experience with DevOps and CI/CD practices.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Enterprise Integration ArchitectureAWSAzureAWS GlueAWS LambdaAmazon KinesisAmazon S3Amazon RedshiftAI AgentsAgent FrameworksMCPLLMsRAGVector DatabasesPrompt ManagementTool CallingData LakehouseData MeshReal-time StreamingMuleSoftApache KafkaInformaticaData GovernanceMetadata ManagementObservabilityCI/CD

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

AI-Integrated Data Architecture
The primary focus is designing and executing integration strategies that bridge traditional enterprise data systems with modern AI/ML capabilities. You must demonstrate how to architect scalable, secure data flows that support RAG, vector databases, and LLM API integrations within an AWS/Azure cloud-native environment.
Cross-Functional Liaison and Strategy
You will act as the bridge between the Integration and Data/AI teams. Success requires aligning technical integration patterns—such as event-driven or API-led designs—with broader business goals, while ensuring that data governance, lineage, and observability are maintained across all AI initiatives.
Hands-on Technical Leadership
Beyond high-level strategy, the role requires hands-on involvement in proof-of-concept initiatives for AI agents and orchestration frameworks. You are expected to mentor developers on best practices and contribute to the creation of reusable integration assets and frameworks.

Likely challenges to think through

Balancing Innovation with Regulated Standards
Vantive operates in the healthcare space, which necessitates strict data governance and compliance. The challenge lies in implementing rapid, cutting-edge AI agent frameworks without compromising the security, lineage, or regulatory requirements of sensitive medical data.
Managing Hybrid Integration Complexity
You will likely face the difficulty of integrating legacy systems with modern cloud-native data architectures like data mesh or lakehouses. Prepare to discuss how you maintain performance and consistency when moving data across disparate environments using tools like Kafka, MuleSoft, or AWS services.

Practice your answers

Technical practice

Designing RAG Architectures for Enterprise

What to coverExplain how you would design a Retrieval-Augmented Generation (RAG) pipeline to ensure data privacy and accuracy. Discuss the trade-offs between different vector database choices and how you manage embeddings and context retrieval in a high-performance environment.

Integrating AI Agents with Legacy Systems

What to coverDescribe your approach to enabling AI agents to interact with existing enterprise systems via APIs. Address how you handle tool calling, authentication, and error handling when integrating LLMs into a production-grade workflow.

Scalable Data Integration on AWS

What to coverDiscuss how you leverage AWS services like Glue, Kinesis, and Lambda to build a resilient data integration architecture. Focus on how you ensure data consistency and low-latency processing for real-time streaming versus batch workloads.

Implementing Data Governance in AI Pipelines

What to coverHow do you embed data lineage, governance, and observability into an integration architecture that supports AI/ML initiatives? Explain your strategy for maintaining metadata management across complex, multi-domain data environments.

Event-Driven vs. API-Led Integration Patterns

What to coverCompare event-driven and API-led integration patterns in the context of a modern data mesh architecture. Explain the criteria you use to decide which pattern is appropriate for specific enterprise data flow requirements.

Managing Prompt Engineering at Scale

What to coverDescribe your methodology for managing prompts across multiple AI applications. Discuss how you version control prompts and ensure consistency in model outputs when integrating LLMs into automated business processes.

Data Lakehouse and Mesh Architectures

What to coverExplain your experience with Data Lakehouse or Data Mesh architectures. Discuss the challenges of maintaining data quality and accessibility when moving from monolithic integration to a decentralized, domain-oriented data structure.

Ensuring Security in AI-Enabled Integrations

What to coverWhat security considerations are paramount when integrating LLMs and AI agents into a regulated environment? Address topics such as data masking, secure API communication, and preventing prompt injection or unauthorized data access.

Behavioural practice

Bridging Technical and Business Stakeholders

What to coverDescribe a situation where you had to act as a liaison between technical teams (like Data/AI) and business leadership. How did you ensure that the integration strategy aligned with business goals while maintaining technical feasibility?

Mentoring and Technical Leadership

What to coverProvide an example of how you have mentored developers or guided a team on integration best practices. Focus on how you fostered a culture of continuous improvement and technical excellence.

Leading Proof-of-Concept Initiatives

What to coverTell me about a time you led a proof-of-concept (POC) to evaluate a new technology or methodology. How did you define success metrics, and what was the outcome of the initiative?

Navigating Constraints in Regulated Industries

What to coverDescribe a project where you had to design an integration architecture within the constraints of a highly regulated industry (e.g., healthcare or finance). How did you balance innovation with strict compliance and security requirements?

Sharpen your knowledge

Topics to prepare

Enterprise AI Integration Strategy
Focus on the conceptual design of integrating LLMs and AI agents into existing enterprise data ecosystems. Exercise: Draft a high-level architecture diagram showing how an API-led integration layer connects legacy systems to a RAG-based AI agent workflow, ensuring data governance and lineage are maintained. This is relevant to your role as the primary liaison between integration and AI teams.
Modern Data Architecture Patterns
Review the principles of Data Mesh and Data Lakehouse architectures. Exercise: Compare the trade-offs between a centralized data lakehouse and a decentralized data mesh approach for a healthcare-regulated environment. This is critical for the requirement to design scalable, secure, and high-performance data movement across domains.

Skills to revise

AWS Data Integration Services
Hands-on proficiency with AWS Glue, Kinesis, and Lambda for data pipelines. Exercise: Design a serverless pipeline using AWS Glue for ETL and Kinesis for real-time streaming to feed a vector database. This directly addresses the requirement for building resilient integration architectures on AWS.
AI Agent and RAG Implementation
Practical experience with agent frameworks, prompt management, and tool calling. Exercise: Build a small-scale prototype using an agent framework that retrieves context from a vector database via RAG to answer domain-specific queries. This is essential for the requirement to have hands-on experience enabling AI agents in production.
Integration Platform Proficiency
Experience with platforms like MuleSoft, Kafka, or Informatica. Exercise: Map out an API-led connectivity strategy using one of these tools to expose data services for AI consumption. This is relevant to the requirement for evaluating and implementing modern integration technologies.

Your preparation checklist

Map AWS/Azure Integration Patterns to AI Workflows
Prepare a technical summary demonstrating how you have used AWS services like Glue, Lambda, and Kinesis to support AI/ML pipelines. Specifically, explain how you would architect a RAG-based integration that ensures secure, real-time data flow from source systems to vector databases.
Develop an AI Agent Architecture Case Study
Create a brief walkthrough of a production or POC project where you implemented AI agents. Highlight your experience with agent frameworks, tool calling, and prompt management, and explain how you handled API-based LLM integration within a regulated environment.
Prepare for Stakeholder and Governance Discussions
Draft talking points on how you balance innovation with data governance and observability. Be ready to discuss how you maintain data lineage and metadata management when implementing modern architectures like data mesh or data lakehouse.
Formulate Strategic Questions for the Hiring Team
Prepare insightful questions regarding Vantive's current integration challenges, such as: 'How is the team currently balancing the integration of legacy systems with new AI-driven agent frameworks?' or 'What are the primary data governance hurdles you face when scaling RAG architectures in a regulated healthcare context?'

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