JOB
SUMMARY

We are seeking a highly skilled Quality Engineer with 5–10
years of experience who is passionate about leveraging AI to transform how test
automation is built and maintained. The ideal candidate uses AI-powered agentic
tools — such as Playwright in agentic mode, Claude, Cursor, or GitHub Copilot —
to autonomously generate, execute, and iterate on automation scripts across the
SmartFM platform. Rather than writing every test by hand, this engineer directs
AI agents to produce and refine testing automation code, dramatically
accelerating test coverage across applications, data pipelines, AI/ML systems,
APIs. This role is at the cutting edge of modern QA engineering, combining deep
automation expertise with hands-on experience orchestrating AI agents as a
force multiplier for quality.

ROLES AND
RESPONSIBILITIES

Core QA & Test Strategy

·
Develop
and implement end-to-end quality assurance strategies and test plans for
applications, data pipelines, data transformations, APIs, and machine learning
models within the SmartFM platform.

·
Collaborate
with the Engineering team throughout the product development lifecycle to
ensure alignment with end-user product and quality expectations, as well as
adherence to delivery timelines.

·
Establish
test strategy automated test suites and automate functional testing for
existing and new applications.

·
Create
test Cases, test scripts and test data from user stories and ensure new
functionality meets acceptance Criteria.

·
Document testing procedures, test results, and data
quality metrics, providing clear and actionable insights to cross-functional
teams.

AI Agentic Automation

·
Use
AI agentic tools (e.g., Playwright's AI/MCP mode, Claude, Copilot) to
autonomously generate, execute, refine, and maintain automation scripts —
treating AI as a co-engineer in the test development workflow.

·
Direct and prompt AI agents to produce Playwright test
suites covering end-to-end UI flows, API contracts, and integration scenarios
across the SmartFM platform.

·
Review, validate, and improve AI-generated automation
scripts, applying engineering judgment to ensure correctness, maintainability,
and coverage quality.

Data, ML
and Pipelines

·
Perform rigorous data validation and quality checks on
data stored in NOSQL databases (MongoDB), including schema validation, data
integrity checks, and performance testing of data retrieval.

·
Collaborate closely with Data Engineers to ensure the
robustness and scalability of data pipelines and to identify and resolve data
quality issues at their source.

·
Work with Data Scientists to validate the performance,
accuracy, fairness, and robustness of Machine Learning, Deep Learning, Agentic
Workflows, and LLM-based models. This includes testing model predictions,
evaluating metrics, and identifying potential biases.

·
Implement automated testing frameworks for data
quality, pipeline validation, and model performance monitoring.

·
Stay updated with the latest trends and tools in data
quality assurance, and MLOps, advocating for continuous improvement in our
quality processes.

·
Collaborate with product and
implementation teams in resolving defects and improve the quality of the
product

·
Coordinate with cross-functional
teams and stakeholders to achieve project alignment.

REQUIRED
TECHNICAL SKILLS AND EXPERIENCE

·
5-10
years of professional experience in Quality Assurance, with a significant focus
on automated application testing, data quality,
or ML model testing.

·
Strong
proficiency in Test Automation tools such as Playwright

·
CI/CD integration experience (GitHub Actions,
Azure DevOps, Jenkins)

·
Strong
proficiency in SQL for complex data validation, querying, and analysis across
large datasets.

·
Proven
experience in testing and validating data stored in
NOSQL
databases
(MongoDB) or similar NoSQL databases.

·
Understanding
of Agentic Workflows and LLMs from a testing perspective, including prompt
validation and output quality assessment.

·
Experience with API testing and using Playwright or
similar tools to automate API validation.

·
Solid understanding of data pipeline concepts
(ingestion, transformation, validation) and the ability to direct AI agents to
generate appropriate quality checks across these layers.

·
Proficiency
in
Python
for scripting, test automation,
and data validation.

·
Familiarity
with Machine Learning and Deep Learning concepts, including model evaluation
metrics, bias detection, and performance testing.

·
Knowledge of cloud platforms (Azure, AWS, or GCP) and
their relevance to deploying and running automated test infrastructure.

ADDITIONAL QUALIFICATIONS

·
Communication:
Exceptional communication skills to engage with stakeholders, clients, and
senior leadership effectively.

·
Collaboration:
Strong skills in fostering cross-functional teamwork and aligning goals with
stakeholders.

·
Ability
to comprehend, analyze, and interpret documents effectively

·
Highly
motivated to acquire new skills, explore emerging technologies in data quality
and AI/ML testing, and stay updated on the latest industry best practices.

·
Domain
knowledge in facility management, IoT, or building automation is a plus.

EDUCATION
REQUIREMENTS / EXPERIENCE