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Location: Bangalore, India
Job Description
We are looking for an AI Product Architect – Digital Finance to lead the architecture and technical evolution of an AI-enabled Digital Finance product platform.
The role will sit at the intersection of Finance, Product Engineering, Data, Generative AI and Agentic AI. The architect will translate finance business capabilities into scalable product architecture and ensure AI capabilities are secure, governed, explainable, reusable and production-ready.
The Digital Finance architecture includes role-based finance experiences, finance intelligence, AI workers/agents, governed knowledge and controls, and trusted finance data products.
This is a product architecture role, not just an AI/LLM development role. The successful candidate should be capable of taking an AI use case from business problem through architecture, design, engineering patterns, controls and production deployment.
Key Responsibilities
Own end-to-end architecture for the Digital Finance AI product ecosystem.
Translate product vision and finance use cases into scalable, modular architecture.
Define reusable architectural patterns rather than point solutions for individual finance use cases.
Architect capabilities spanning user experience, orchestration, AI processing, data, integrations, security and platform services.
Establish architecture standards, design principles, APIs and reusable product components.
Design enterprise-grade AI agents and multi-agent workflows for finance use cases.
Define patterns for agent orchestration, tool/API invocation, memory/context, RAG, semantic/vector search, structured data retrieval and human-in-the-loop workflows.
Establish an LLM gateway / model abstraction approach so the product can work with approved foundation models without tightly coupling product capabilities to an individual model.
Design guardrails, grounding, validation, observability and fallback mechanisms.
Establish evaluation approaches covering quality, accuracy, latency, cost and user feedback.
These areas directly align with the current Digital Finance technical direction, which identifies LLM gateway, agent orchestration, vector storage, APIs, monitoring and audit storage as core components.
Record-to-Report / Period Close
Accounts Receivable and collections
Accounts Payable
Invoice and contract intelligence
Cash and liquidity
Working capital
Planning and forecasting
Reconciliation and anomaly detection
Tax, audit and controls
CFO insights and management narratives
The existing product architecture specifically identifies capabilities including Close Intelligence, reconciliation, GL anomaly detection, contract-to-invoice validation, DSO/collections intelligence, cash forecasting, working-capital optimization and finance narratives.
Define how AI consumes governed structured and unstructured finance information.
Design reusable finance data products and semantic/knowledge layers.
Architect RAG and retrieval patterns across policies, contracts, invoices, SOPs and other finance knowledge.
Establish grounding and lineage from source information through AI-generated responses.
Work closely with data engineering teams on lakehouse/data-product architecture.
The current technical requirements use Microsoft Dynamics, Azure Databricks, Delta Lake, Power BI, Unity Catalog, APIs and document stores as components of the Digital Finance landscape.
Define architecture for responsible and trustworthy AI.
Implement guardrails against hallucination, inappropriate actions and unsupported recommendations.
Design model/prompt versioning, evaluation and monitoring.
Ensure traceability of AI recommendations to supporting evidence.
Implement identity, RBAC, sensitive-data protection and audit logging.
Design human approval controls for high-risk finance actions.
The internal requirements explicitly call for human approval for high-risk actions, as well as model governance, prompt governance, audit trails and data lineage.
Work closely with Product Managers, Finance SMEs, UX, AI/ML engineers, data engineers, application engineers and security teams.
Review solution designs and provide architecture guidance to engineering squads.
Drive technical PoCs into reusable, production-quality product capabilities.
Make architecture decisions balancing business value, accuracy, scalability, performance, security, cost and maintainability.
Mentor engineering teams on modern AI architecture and product-engineering practices.
Required Experience
10+ years of technology/product engineering experience, with meaningful hands-on experience in modern AI/data architecture
Candidates should demonstrate:
Strong enterprise/application architecture background.
Hands-on understanding of Generative AI, LLM and Agentic AI architectures.
Experience designing production AI applications, not only PoCs.
Strong understanding of:
LLM orchestration
RAG and semantic search
Vector databases
Embeddings
Agent/tool calling
Prompt/context engineering
LLM evaluation
AI observability
Guardrails and responsible AI
API/microservices architecture
Experience with cloud-native architecture.
Strong data architecture understanding, preferably including lakehouse and governed data-product patterns.
Experience integrating AI systems with enterprise platforms and workflows.
Preferred Technology Experience
AI / GenAI
Azure OpenAI / OpenAI APIs or comparable enterprise LLM platforms
Agentic AI frameworks and orchestration patterns
RAG and vector search
Model evaluation and monitoring
Prompt and model lifecycle management
Data & Cloud
Azure
Azure Databricks
Delta Lake
Unity Catalog
SQL / Python
Data APIs and semantic layers
Enterprise & Integration
REST/API architecture
Event-driven and microservices patterns
Microsoft Dynamics 365
Power Platform / Power Automate
Power BI
Enterprise identity and RBAC
Several of these technologies already appear in the Digital Finance technical architecture, particularly Dynamics, Databricks/Delta Lake, Unity Catalog, Power BI, APIs and workflow integration.
Finance Domain Experience
Deep finance expertise should be preferred rather than mandatory if you want to maximize the AI architecture talent pool.
Exposure to one or more of the following would be valuable:
Record-to-Report
Order/Lead-to-Cash
Procure-to-Pay
Accounts Receivable / Accounts Payable
Working Capital
Treasury / Cash Management
Financial Planning & Analysis
Financial Close and Reconciliation
Audit, Controls and Compliance
Bachelor’s or higher degree in Computer Science or a related discipline; or equivalent
Job Info
Job Identification
30051525
Posting Date
11/04/2026
Apply Before
11/09/2026
Job Schedule
Full time
Locations
Bangalore, Karnataka, India
Job data: Headcount Labs (headcountlabs.com), ODbL 1.0. The job description belongs to the employer. Location data © OpenStreetMap contributors and Overture Maps.
Most IT Jobs posts in Bangalore ask for 3–6 years of experience (41% of 3627 that say).
From live job posts on CoreHRx, updated 6 Nov 2026. Skill figures count posts that list their skills; salary figures only posts that show a salary.
The post does not state the pay. As an estimate, comparable posts on CoreHRx advertise ₹15L – ₹25L a year (typical for IT Jobs in Bangalore, 309 posts).
The post asks for 10 Yrs experience.
In Bangalore.
It is a full time job.
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