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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)
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Establish QA best practices for Traditional ML and Generative AI workflows, including:
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Functional and regression testing of ML pipelines using pytest and Airflow/Dagster test utilities and API testing tools (e.g., Postman, pytest-httpx).
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Validate data contracts, schemas, and API compatibility across services using Pandera, and custom validation rules.
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Model behavior validation (input/output ranges, invariants, edge cases) using NumPy, SciPy, and statistical assertions
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Runtime and performance testing for inference latency, throughput, and resource usage using Locust, k6, or custom load tests.
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Integrate ML-specific tests into CI/CD pipelines using GitHub Actions, GitLab CI, or Jenkins, alongside containerized workflows (Docker, Kubernetes).
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Implement LLM-specific testing, including:
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Prompt and response validation, determinism checks, and regression testing using LangSmith.
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Evaluation of hallucinations, toxicity, and policy adherence using LLM-as-a-judge and/or rule-based checks.
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Cost, token usage, and timeout monitoring for GenAI workflows
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Verify logging, monitoring, and alerting for ML services using Prometheus, Grafana, and cloud-native observability tools.
Requirements:
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BS or MS in Computer Science or a related field.
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2-5 years of experience in Data or Machine Learning projects.
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Familiarity and experience of GenAI applications and tools -PyTorch, LangChain, vLLM etc.
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Demonstrates a commitment to continuous learning in this rapidly evolving field.
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Tools listed in the responsibilities section.
