Principal Software Engineer

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 We’re doing work that matters. Help us solve what others can’t.