Middleware Architect

Design and evolve enterprise application architecture for CRM, wealth management,

dashboards, analytics, and digital platform use cases.

Define scalable and reusable architecture patterns for backend services, APIs, integrations,

and platform components.

Improve system reliability, maintainability, performance, and production readiness across

business-critical applications.

Align pod-level application development with central architecture standards, engineering

governance, and integration principles.

Enable AI-first application design by ensuring that platforms can securely consume data

services, analytics services, and AI capabilities.

Provide technical leadership across design reviews, solutioning, delivery planning, and

engineering standards.

Design end-to-end solution architecture for new initiatives and modernization programs.

Define application architecture patterns for microservices, APIs, event-driven systems,

integration layers, and modular monoliths where appropriate.

Review business and functional requirements and convert them into scalable technical

architectures.

Job Description: Application Architect

Role purpose

Role summary

Key objectives

Key responsibilities

Work with database and data platform teams to define clean data access patterns, service

boundaries, and integration contracts.

Establish standards for performance, caching, resiliency, observability, logging, security,

and deployment readiness.

Lead architecture and code reviews across engineering squads and pods.

Guide teams on non-functional requirements such as scalability, availability, disaster

recovery, monitoring, and cost efficiency.

Support modernization of legacy systems into cloud-native or service-oriented

architectures.

Collaborate with DevOps teams on CI/CD, cloud deployment, environment strategy, and

release governance.

Work with AI and analytics teams to integrate recommendation engines, text-to-SQL

services, document intelligence, and other AI capabilities into applications in a secure and

controlled way.

Mentor senior engineers and technical leads, and help improve engineering maturity

across teams.

10 to 15+ years of experience in software engineering and application architecture.

Strong hands-on experience in backend application design using one or more major stacks

such as Java/Spring Boot, .NET/.NET Core, Node.js, or Python for enterprise platforms.

Strong experience in designing REST APIs, microservices, integration services, and

service-to-service communication patterns.

Good understanding of event-driven architecture, asynchronous processing, caching, and

distributed systems.

Experience with relational and non-relational databases such as PostgreSQL, SQL Server,

MongoDB, Redis, ClickHouse, Elasticsearch, or similar technologies.

Experience in cloud-native and containerized environments such as AWS, Azure, Docker,

Kubernetes, and managed cloud services.

Strong understanding of observability, logging, tracing, API monitoring, and production

support practices.

Experience in authentication, authorization, RBAC, secure design, and compliance-

sensitive application development.

Proven ability to lead solution design discussions and guide engineering teams through

implementation.

Required skills and experience

Experience in BFSI, wealth management, broking, lending, insurance, or related regulated

industries.

Experience in CRM, business workflow systems, dashboards, reporting platforms, or

customer-facing transaction platforms.

Familiarity with Kafka, Redis, Elasticsearch/OpenSearch, and API gateways.

Familiarity with architecture for analytics-enabled and AI-enabled applications.

Exposure to data platform interaction patterns such as curated data services, semantic

layers, reporting APIs, and governed data access.

Experience in leading cross-functional teams and working with distributed engineering

organizations.

Improved application scalability, modularity, and ease of change across platforms.

Better engineering consistency through reusable architecture patterns and standards.

Faster delivery of new platform capabilities with fewer production issues.

Improved integration quality between application platforms, data platforms, and AI

services.

Stronger observability, resiliency, and production readiness across pods.