Quality Engineer - Architect

About the role

As a QE Solution Architect, you will be involved in designing and building automation initiatives, including Framework development using various AI services. More specifically, you will work on:

1012+ years of experience in Data Testing / Data Quality Engineering, Application Testing, API / Microservices Testing, Mobile (iOS or Android) Testing, Performance Engineering.

Build AI-driven solutions for quality and audit purposes

Designing Frameworks, developing Proof of concepts, Accelerators, and executingeffectively/independently.

Designing and implementing Python utilities to enhance productivity for Enterprise Products like Data, Apps, BI, API, Mobile, etc., with GenAI / LLM intervention.

Building simplified interfaces as part of framework design to facilitate features such as easy to maintain, easy to enhance, high reusability, and scalability factors in mind.

Taking part in Demos, Planning, Proposals, etc., with the rest of the team.

Collaborating with business consultants, data scientists, engineers, and application stakeholders.

Desired Skills and Experience

Strong background in building robust Python-based systems and SQL knowledge

Architect QE solutions on cloud platforms such as Azure (ADLS, Databricks, ADF, Synapse), AWS (Glue, Lambda, Aurora, Redshift), GCP (Big Query, GKE), Snowflake, or equivalent.

Familiarity with containerization, orchestration, and scalable architecture

Knowledge of natural language processing (NLP) is a must.

Hands-on experience with multiple LLMs like OpenAI, Claude, Gemini, Llama, etc., along with implementation of AI Agents and Model Context Protocols (MCP).

Knowledge of using hyperscaler products like Hadoop, Pyspark, Databricks, Snowflake, and AWS EMR

Strong Experience in building multiple frameworks using Streamlit, Pytest, Playwright, Selenium, Seaborn, Plotly, Langchain, LangGraph, Appium, Robot, and related Libraries.

Strong knowledge of CI/CD pipeline (Airflow/Azure DevOps/Jenkins/GitHub Actions)

Ability to independently design, develop, and architect highly available, highly scalable accelerators and frameworks from scratch

Proficiency in data manipulation libraries like pandas or NumPy for Python and experience with data visualization tools to build analytical models and statistics as required.

Good knowledge of RESTful interfaces / Microservices concepts

Good to have experience with GenAI, LLMs, RAG pipelines, and vector databases (Pinecone, FAISS)

Define and implement data quality engineering frameworks for data lakes, data warehouses, and analytics platforms.

Architect and implement data test automation using Python, SQL, and data testing frameworks.

Define data quality KPIs, dashboards, and metrics.

Identify risks related to data accuracy, latency, and compliance and communicate them proactively.

Partner with data engineers, product owners, and architects to drive quality from design to delivery.

Mentor QE engineers and SMEs.

Review test strategies, frameworks, and implementations.

Support proposal discussions, estimations, and solutioning from the QE perspective

Understanding of the concepts involved in Functional & Non-Functional validations

Knowledge of Docker and Kubernetes or containers is good to have

Ability to quickly learn and apply complex technical information to automation solutions.

Attention to detail and ability to effectively escalate issues and manage escalations.

Experience with Agile methodology

Ability to handle multiple assignments at one time and meet delivery dates.

Project Management Tools like ADOTestPlans/ALM/Rally/JIRA knowledge is a plus.

Additional programming languages like Java, JavaScript, Rust, or R knowledge is a plus.

Excellent written and communication skills