QA Data Science Engineer

Come work at a place where innovation and teamwork come together to support the most exciting missions in the world! Job Description   We are seeking a Data Science focused QA engineer to develop next-generation Security Analytics products. You will work closely with Data scientists, engineers and product managers to design and optimize AI driven security solutions.   As QA engineer, the ideal candidate has a strong background in Backend engineering, system integrations, ML,AI and data pipelines.  Responsibilities (QA Engineer – Data Science / ML)  * Establish QA best practices for Traditional ML and Generative AI workflows, including:  * Functional and regression testing of ML pipelines using pytest and Airflow/Dagster test utilities and API testing tools (e.g., Postman, pytest-httpx).  * Validate data contracts, schemas, and API compatibility across services using Pandera, and custom validation rules.  * Model behavior validation (input/output ranges, invariants, edge cases) using NumPy, SciPy, and statistical assertions  * Runtime and performance testing for inference latency, throughput, and resource usage using Locust, k6, or custom load tests.  * Integrate ML-specific tests into CI/CD pipelines using GitHub Actions, GitLab CI, or Jenkins, alongside containerized workflows (Docker, Kubernetes).    * Implement LLM-specific testing, including:  * Prompt and response validation, determinism checks, and regression testing using LangSmith.  * Evaluation of hallucinations, toxicity, and policy adherence using LLM-as-a-judge and/or rule-based checks.  * Cost, token usage, and timeout monitoring for GenAI workflows    * Verify logging, monitoring, and alerting for ML services using Prometheus, Grafana, and cloud-native observability tools.  **Requirements:**  * **BS or MS** in **Computer Science or a related field**.  * **2-5 years** of experience in Data or Machine **Learning** projects.  * **Familiarity and experience of GenAI applications and tools -PyTorch, LangChain, vLLM etc.**  * Demonstrates a commitment to continuous learning in this rapidly evolving field.  * Tools listed in the responsibilities section.