Company Overview

Incedo is a US-based consulting, data science and technology services firm with over 2,800 people helping clients from our six offices across US and India. We help our clients achieve competitive advantage through end-to-end digital transformation. Our uniqueness lies in bringing together strong engineering, data science, and design capabilities coupled with deep domain understanding. We combine services and products to maximize business impact for our clients in telecom, financial services, product engineering and life science & healthcare industries.

Working at Incedo will provide you an opportunity to work with industry leading client organizations, deep technology and domain experts, and global teams.

Incedo

University, our learning platform, provides ample learning opportunities starting with a structured onboarding program and carrying throughout various stages of your career. A variety of fun activities are also an integral part of our friendly work environment. Our versatile career paths allow you to grow into a program manager, a technical architect or a domain expert based on your skills and interests.

Role Description

This is a role for a QA Engineer who can operate autonomously in an AI-augmented, fast-paced environment. You won't be handed detailed test plans — you'll derive them from specs, generate automation with AI assistance, validate data integrity with SQL, and own the quality gate that decides whether a feature ships.

The Control Tower gives you structure: workflows that generate test cases from acceptance criteria, AI that writes first-draft Playwright scripts, and a knowledge graph that tracks coverage gaps. Your job is to apply domain expertise, critical thinking, and testing craft on top of that foundation — ensuring the Fund Expense Management platform is correct, performant, and reliable across every client deployment.

Prompt engineering isn't a nice-to-have here — it's how you multiply your output. The best QA engineers on this team use AI to explore edge cases humans miss, then apply judgement to decide what matters.

HUB Tech Platform · SDLC Control Tower v5.x · Fund Expense Management

2. What You'll Be Testing

The platform covers expense capture, allocation, contract management, approval workflows, GL posting, and reporting. Invoices and expenses flow in from multiple channels (PDF upload with AI extraction, webhook integrations, polling adapters, email inboxes), get normalised, classified, allocated across funds using a configurable rule engine, approved,



and posted outbound to GL systems and fund admin platforms. The entire lifecycle is period-scoped, audited, and collaborative in real-time.

Key Quality Challenges

- External system integrations — Multiple external systems (expense providers, GL platforms, procurement tools, payment systems) with distinct auth patterns, payload formats, sync strategies (webhooks vs polling), and idempotency requirements. Contract testing and end-to-end integration validation are critical.
- AI/LLM pipelines — Invoice OCR extraction and LLM-powered contract parsing produce confidence-scored results. Testing must validate extraction accuracy, confidence thresholds, human-in-the-loop triggers, and structured output correctness.
- Rule-based allocation engine — Multiple allocation methodologies (percentage, NAV-weighted, usage-based, chained rules) with effective-dated versions and penny-exact rounding guarantees. Calculation correctness and edge-case coverage are paramount.
- Real-time multi-user architecture — SSE broadcasting, optimistic locking, version-based conflict detection, and period-scoped state. Testing concurrent user scenarios and data consistency under contention.
- Contract management — Vendor contract lifecycle validation (upload, AI-parse, link to expenses, service line tracking). Verifying that contracts correctly inform allocation rules and expense validation.
- Reporting & GL posting — Configurable report templates, multi-format export correctness (Excel, PDF, CSV), and GL journal entry accuracy with approval gates.
- Period & accounting lifecycle — Open/close/reopen periods, immutable snapshots, adjustment-only corrections post-lock. Boundary testing and state transition validation.
- Workflow & approval engine — Multi-step approval workflows with routing logic, escalation paths, delegation, and audit trails. Testing complex approval scenarios and edge cases.

Technical Skills

3. Technical Stack (QA-Relevant)

Application Under Test

Layer

Technology

Backend

Java 17, Spring Boot 3.3, REST APIs (OpenAPI/Swagger), Spring Security (Azure AD)

Frontend

React 18, Vite, AG Grid Enterprise, TailwindCSS, React Router

Database

Microsoft Fabric SQL (MSSQL), Flyway migrations

Messaging





Azure Service Bus, Apache Kafka + Avro

Real-Time

Server-Sent Events (SSE)

Storage

Microsoft Fabric Lakehouse (OneLake)

AI/OCR

Azure Document Intelligence

QA Tooling & Automation

Layer

Technology

E2E Automation

Playwright (browser automation, visual regression)

API Testing

REST Assured / Postman / contract testing

Performance k6 (load, stress, endurance)

Data Testing

SQL queries against Fabric SQL, Delta table validation

Unit/Integration

Vitest (frontend), Spring Boot Test + H2 (backend)

CI/CD

Azure DevOps Pipelines — automated test gates

Environments

DEV → QA → UAT → PROD (strict gate promotion)

Mandatory

- Good understanding of API testing — REST API validation, status codes, payload schemas, error handling, authentication flows
- Strong contract testing knowledge — OpenAPI contract validation, consumer-driven contracts, backward compatibility checks
- Playwright expertise — E2E browser automation, page object models, visual regression, cross-browser execution
- Solid automation principles & standards — test pyramid, DRY test code, maintainable selectors, CI-integrated suites, reporting
- Good understanding of data testing / SQL — complex queries, data validation, referential integrity, migration verification against MSSQL/Fabric SQL
- Intermediate knowledge of asset management domain — fund structures, expense types, allocation methodologies, accounting periods, NAV concepts

Nice-to-have skills

Desirable

- Proficiency in prompt engineering — crafting structured prompts for AI-driven test generation, edge-case discovery, and defect root-cause analysis
- Experience with QA workflows in the agentic world — working alongside AI coding assistants (Copilot, Windsurf, Cursor) to generate and review test artifacts
- Familiarity with performance testing (k6 or similar) — defining NFR thresholds, scripting load scenarios, interpreting results
- Experience with event-driven testing — validating SSE streams, Kafka message flows, async data consistency
- Knowledge of Microsoft Fabric — Lakehouse queries, Delta table validation, pipeline testing

Qualifications

Qualifications

- 4-6 years of work experience in relevant field
- B.Tech/B.E/M.Tech or MCA degree from a reputed university. Computer science background is preferred

Company Value

We value diversity at Incedo. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

📌 Full Stack Java Developer (Gurugram)
🏢 Incedo
📍 Gurugram