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.