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Job Description & How to Apply Below
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:
● 10–12+ 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 executing effectively/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, Lang Graph, Appium, Robot, and related Libraries.
● Strong knowledge of CI/CD pipeline (Airflow/Azure Dev Ops/Jenkins/Git Hub 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 Num Py 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
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