QA

 

 

 

 

Role Summary We are seeking a highly skilled and analytical Software Development Engineer in Test (SDET) specializing in Data and AI to join our fast-growing engineering team. In this role, you will be the guardian of quality for our data ecosystems and artificial intelligence models. You will design, develop, and implement robust automated testing frameworks for massive data pipelines, ETL processes, machine learning models, and AI-driven applications. Working at the intersection of quality assurance, data engineering, and data science, you will collaborate closely with cross-functional teams to ensure data integrity, model accuracy, and system reliability from ingestion to inference. Key Responsibilities Test Automation & Framework Development: Design, build, and maintain scalable automated testing frameworks from scratch for backend services, APIs, data pipelines, and AI applications. Data Pipeline & ETL Testing: Validate data extraction, transformation, and loading (ETL/ELT) processes. Ensure data quality, completeness, and consistency across data lakes, data warehouses, and relational databases. AI & Machine Learning Testing: Develop testing strategies for ML models and AI features. This includes validating model training data, testing model endpoints for accuracy and latency, and evaluating edge cases, model bias, and data drift. GenAI & LLM Validation: (If applicable) Design evaluation metrics and automated tests for Large Language Models (LLMs), including testing Retrieval-Augmented Generation (RAG) pipelines, prompt effectiveness, and hallucination mitigation. CI/CD Integration: Integrate automated data and AI test suites into continuous integration and continuous deployment (CI/CD) pipelines to ensure seamless and reliable release cycles. Performance & Load Testing: Conduct performance, scalability, and load testing on data processing frameworks and AI model inference endpoints to ensure they meet strict SLAs. Defect Management: Identify, document, and track software, data, and model defects. Work closely with Data Engineers and Data Scientists to perform root cause analysis and resolve complex architectural or algorithmic issues. Quality Advocacy: Champion best practices for software quality, test-driven development (TDD), and data governance across the engineering organization. Required Qualifications and Skills Education: Bachelor’s or master''s degree in computer science, Data Science, Engineering, or a related technical field. Experience: 4+ years of experience as an SDET, Data Quality Engineer, or ML Engineer with a strong focus on test automation. Programming: Advanced proficiency in Python (highly preferred for AI/Data) or Java/Scala. Experience writing clean, modular, and maintainable code. Data Skills: Expert-level SQL skills for complex data querying and validation. Hands-on experience testing ETL/ELT pipelines and working with big data technologies (e.g., Spark, Hadoop, Kafka, Snowflake, Databricks). AI/ML Skills: Solid understanding of machine learning lifecycle and data science concepts. Experience testing RESTful APIs and microservices serving machine learning models. Familiarity with Python data and ML libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch). Test Automation Tools: Proficiency with testing frameworks such as PyTest, JUnit, TestNG, or similar. DevOps & Tools: Experience with CI/CD tools (e.g., Jenkins, GitHub Actions, GitLab CI), containerization (Docker, Kubernetes), and version control (Git). Preferred Qualifications Experience with cloud platforms such as AWS, Google Cloud Platform (Google Cloud Platform), or Microsoft Azure. Hands-on experience with modern data orchestration tools (e.g., Apache Airflow, dbt, Dagster). Previous experience testing Generative AI applications, vector databases (e.g., Pinecone, Milvus), or working with OpenAI/Anthropic APIs. Familiarity with data quality and observability tools (e.g., Great Expectations, Monte Carlo). Experience with performance testing tools (e.g., JMeter, Locust, Gatling).