AI Software Development Engineer in Test (AI-SDET)
Tasks
- Automate testing for microservices and streaming
- Build automated LLM validation pipelines
- Champion AI testing tools and practices
- Create synthetic and benchmark datasets
- Define AI quality standards for SDLC
- Design AI evaluation frameworks
- Develop API automation frameworks
- Develop UI automation frameworks
- Identify QA process gaps and introduce AI solutions
- Integrate automation into CI/CD pipelines
- Measure AI latency token usage cost and throughput
- Monitor AI observability logging tracing
- Perform cloud testing and deployments
- Run workshops to upskill QA engineers
- Validate AI agents and orchestration frameworks
- Validate LLM outputs
- Validate RAG pipeline architectures
- Validate distributed system reliability
- Validate multi agent workflows
- Validate vector database and retrieval accuracy
Perks/Benefits
- Annual bonus
- Extra days off
- Health care benefits
- In-company English lessons
- Internet reimbursement
- LinkedIn Learning
- Referral program bonus
- Tuition reimbursement
- Work from home or hybrid options
- Work-life balance programs
Skills/Tech-stack
AI evals | API Testing | AWS | Agile Scrum | Autogen | Azure | Azure DevOps | CI/CD | ChromaDB | CrewAI | Cypress | Docker | Embeddings | Event Driven | Event-driven architecture | Faiss | Function Calling | GCP | GitHub Actions | GitLab CI | GitLab CI/CD | GraphQL | Jenkins | Kubernetes | Langchain | Langgraph | Language Models | Large Language Models | Logging | Micro Frontends | Microservices | Observability | OpenAI | Pinecone | Playwright | Prompt engineering | Python | Qdrant | RAG | REST APIs | Robot Framework | SQL | Selenium | Semantic Kernel | Semantic Scoring | Streaming | Structured outputs | Synthetic data | Tool-Calling | Tracing | UI Automation | Vector Databases | Weaviate | Webhooks
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