Quality Engineer (Tester)

Accenture; Gurugram District; On-site; INR 800,000 - 1,200,000; Full time

Job summary

Accenture is seeking a Quality Engineer (Tester) in Gurugram District, India, to enable full stack solutions through multi-disciplinary team planning. The role involves creating automation strategies, optimizing delivery, and driving quality across the application lifecycle. Candidates should have a minimum of 3 years of experience and be proficient with SAP BTP Datasphere. Strong fundamentals in data warehousing and software engineering are required, along with skills in LLMs and data modeling.

Qualifications

• Minimum 3 years of experience is required.
• Hands-on building with LLMs + RAG (retrieval, grounding, prompt/tool design, evaluation).

Responsibilities

• Design and implement governed data products using Datasphere concepts.
• Create grounded AI experiences by connecting LLM applications to Datasphere curated models.
• Enable transition paths from legacy warehouse investments by leveraging BW bridge.
• Implement layered design patterns to cleanse/validate and publish analytics ready models.
• Build data agents that can plan, call tools, and generate answers.
• Establish offline/online evaluation loops for data agents.
• Partner closely with business and platform teams to align data products.

Skills

SAP BTP Datasphere

Data modeling

Strong data warehousing fundamentals

Building with LLMs + RAG

Solid software engineering fundamentals

Education

15 years full time education

Job description

Project Role

Quality Engineer (Tester)

Project Role Description

Enables full stack solutions through multi-disciplinary team planning and ecosystem integration to accelerate delivery and drive quality across the application lifecycle. Performs continuous testing for security, API, and regression suite. Creates automation strategy, automated scripts and supports data and environment configuration. Participates in code reviews, monitors, and reports defects to support continuous improvement activities for the end-to-end testing process.

Must Have Skills

SAP BTP Datasphere

Good to Have Skills

None

Experience

Minimum 3 year(s) of experience is required.

Education

15 years full time education

Summary

Build AI native, data centric products on SAP BTP Datasphere by combining strong enterprise data warehousing and semantic modeling expertise with agentic AI architectures (LLMs + tools + retrieval + evaluation). The focus is to move beyond dashboards into intelligent data experiences—data agents, conversational analytics, and grounded insights—built on governed Datasphere models and integrated enterprise sources.

Core Responsibilities

1. AI Native Data Product Engineering (on Datasphere). Design and implement governed data products using Datasphere concepts such as Spaces and shareable models/views, enabling teams to explore, transform, and share curated datasets across domains. Build semantic models that are fit for both analytics and AI consumption (clear entity definitions, measures, hierarchies, lineage-friendly design).
2. Retrieval + Grounding (RAG) over Enterprise Data. Create grounded AI experiences by connecting LLM applications to Datasphere curated models and enterprise sources (SAP and non SAP), ensuring responses are traceable to governed data. Engineer retrieval strategies that respect domain boundaries (spaces), freshness needs, and access controls, so AI outputs remain reliable and compliant.
3. Hybrid Modernization & Migration (BW bridge patterns). Enable transition paths from legacy warehouse investments by leveraging approaches such as reusing SAP BW models and skills with Datasphere / BW bridge, supporting phased cloud modernization.
4. Lakehouse style Layering & Data Quality by Design. Implement layered design patterns (e.g., Bronze/Silver/Gold) to land raw data, cleanse/validate, and publish analytics ready models—while maintaining clear rules for what is exposed for consumption. Embed quality controls, validation checks, and reproducible transformations as part of the delivery lifecycle.
5. Agentic Orchestration & Tooling. Build data agents that can plan, call tools (query/metadata/lineage), retrieve context, and generate answers with citations—backed by deterministic checks and fallback behaviors. Implement prompt templates, tool schemas, and safe action boundaries for enterprise-grade usage.
6. Evaluation, Observability & Responsible AI. Establish offline/online evaluation loops (golden questions, regression suites, behavior tests) for conversational analytics and data agents. Add telemetry for AI interactions (latency, grounding rate, failure modes) to improve reliability and cost efficiency.
7. Integration & Collaboration. Partner closely with business, data governance, and platform teams to align data products with real decisions and operational workflows. Drive reusable patterns and accelerators for repeatable delivery across domains.

Primary Skills (AI Native Must Have)

• SAP BTP Datasphere: data modeling, spaces, sharing patterns, enterprise semantic design.
• Strong data warehousing fundamentals and ability to translate business domains into governed analytical models.
• Hands‑on building with LLMs + RAG (retrieval, grounding, prompt/tool design, evaluation).
• Solid software engineering fundamentals: testability, CI/CD mindset, reliable integrations.

Secondary / Strongly Beneficial Skills

• Migration/modernization experience leveraging BW bridge style transition patterns.
• Layered architecture implementation (Bronze/Silver/Gold) for scalable analytics delivery.
• Familiarity with vector search / embedding pipelines (when integrating external AI retrieval components).

What This Role Does Not Center On

Training foundation models from scratch (the emphasis is on building agentic apps and governed retrieval on enterprise data). AI assisted only delivery this role owns the AI behavior (grounding, evaluation, safety) end to end.

Value Delivered

Faster path from data to decision through conversational + agentic analytics grounded in governed Datasphere models. Scalable modernization of hybrid data estates via patterns like BW bridge. Higher trust AI outputs by implementing layered quality + evaluation loops.

Equal Employment Opportunity Statement

All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law.