At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.
Senior AI Test / Automation Engineer
Overview
Role: AI Test / Automation Engineer
Location: Bangalore India
Department: AI Engineering / Quality Assurance
Experience Level: Mid to Senior
We are looking for a highly motivated Senior AI Test / Automation Engineer to design and scale automated validation frameworks for AI/ML models, LLM-based applications, and agentic systems. This role is critical to ensure that AI solutions meet enterprise standards for quality, reliability, safety, and compliance before and after production deployment.
Key Responsibilities
- Build and maintain AI test automation frameworks for pre-qualification and continuous validation of models and agent workflows
- Develop comprehensive test suites, including:
- Unit, integration, and end-to-end (E2E)
- Functional, regression, performance, and safety testing
- Validate AI system behavior, including:
- Non-deterministic LLM outputs
- Hallucinations and edge cases
- Multi-step agent decision-making
- Design and manage evaluation systems:
- Golden datasets
- Benchmarking pipelines (accuracy, latency, reliability)
- Automate testing within CI/CD pipelines for model updates, prompt changes, and tool integrations
- Implement observability and telemetry to enable traceability, monitoring, and audit readiness
- Collaborate cross-functionally with ML, MLOps, Product, and Security teams to define quality gates and release criteria
- Track and report quality KPIs, including test coverage, defect leakage, and system reliability
- Drive root-cause analysis and continuous improvement across the AI testing lifecycle
Required Skills
Core Engineering
- Strong programming skills in Python; familiarity with Bash, TypeScript, or Go
- Experience with test automation frameworks such as PyTest, Playwright, Selenium, or Cypress
- Proficiency in CI/CD tools (GitHub Actions, Jenkins, GitLab CI)
- Experience with cloud platforms (AWS, Azure, GCP) and containers (Docker, Kubernetes)
AI / ML & Agentic Systems
- Hands-on experience with LLM ecosystems (OpenAI, Anthropic, Bedrock)
- Familiarity with:
- RAG architectures and vector databases (Pinecone, Weaviate)
- Agent frameworks (LangChain, LlamaIndex, AutoGen)
AI Testing Techniques
- Experience with non-deterministic testing approaches (statistical assertions, tolerance thresholds)
- Knowledge of evaluation methods:
- LLM-as-a-judge
- BLEU, ROUGE, semantic similarity scoring
- Experience with prompt and agent regression testing
- Understanding of AI safety testing, including adversarial testing, bias/fairness validation, and jailbreak detection
Tooling (Preferred)
- AI testing & observability tools: LangSmith, TruLens, Arize, Weights & Biases
- Evaluation tools: DeepEval, Ragas, PromptFoo, Giskard
- Monitoring: Prometheus, Grafana, OpenTelemetry
Soft Skills
- Strong analytical and problem-solving skills
- Excellent communication and cross-functional collaboration
- Data-driven mindset with focus on quality KPIs
- Detail-oriented with a strong bias toward automation and scalability
Experience Requirements
- 7+ years in QA, SDET, or test automation engineering
- Proven experience building and scaling automation frameworks
- Hands-on experience with AI/ML systems or LLM-based applications
- Experience testing RAG pipelines or agentic workflows
- Owned end-to-end AI test strategy and architecture
- Defined quality metrics and release gates
- Delivered scalable validation pipelines for production AI systems
- Supported audit and compliance readiness
Preferred
- Experience in enterprise or regulated environments (SOC2, ISO 27001, etc.)
- Exposure to:
- Shift-left testing practices
- Production observability and monitoring
- Chaos or resilience testing
Senior-Level Differentiators
Education
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related field
Nice-to-have:
- ISTQB certification
- Cloud/ML certifications (AWS, Azure, GCP)
- AI testing certifications
What Success Looks Like
- AI systems that are accurate, reliable, and safe
- Fully automated test pipelines integrated into CI/CD
- Measurable improvements in defect leakage and model quality
- Strong observability and auditability across AI systems
- Scalable validation frameworks supporting rapid AI innovation
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