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CA
Cadence

Principal Software Engineer

LocationBANGALORE
Work modeon-site
Typefull-time
DepartmentAI Engineering / Quality Assurance
Company size10,000+ people
First seen1w ago
Last seen4d ago
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
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Build and maintain AI test automation frameworks for pre-qualification and continuous validation of models and agent workflows
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Develop comprehensive test suites, including:
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Unit, integration, and end-to-end (E2E)
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Functional, regression, performance, and safety testing
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Validate AI system behavior, including:
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Non-deterministic LLM outputs
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Hallucinations and edge cases
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Multi-step agent decision-making
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Design and manage evaluation systems:
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Golden datasets
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Benchmarking pipelines (accuracy, latency, reliability)
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Automate testing within CI/CD pipelines for model updates, prompt changes, and tool integrations
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Implement observability and telemetry to enable traceability, monitoring, and audit readiness
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Collaborate cross-functionally with ML, MLOps, Product, and Security teams to define quality gates and release criteria
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Track and report quality KPIs, including test coverage, defect leakage, and system reliability
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Drive root-cause analysis and continuous improvement across the AI testing lifecycle
Required Skills
Core Engineering
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Strong programming skills in Python; familiarity with Bash, TypeScript, or Go
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Experience with test automation frameworks such as PyTest, Playwright, Selenium, or Cypress
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Proficiency in CI/CD tools (GitHub Actions, Jenkins, GitLab CI)
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Experience with cloud platforms (AWS, Azure, GCP) and containers (Docker, Kubernetes)
AI / ML & Agentic Systems
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Hands-on experience with LLM ecosystems (OpenAI, Anthropic, Bedrock)
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Familiarity with:
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RAG architectures and vector databases (Pinecone, Weaviate)
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Agent frameworks (LangChain, LlamaIndex, AutoGen)
AI Testing Techniques
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Experience with non-deterministic testing approaches (statistical assertions, tolerance thresholds)
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Knowledge of evaluation methods:
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LLM-as-a-judge
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BLEU, ROUGE, semantic similarity scoring
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Experience with prompt and agent regression testing
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Understanding of AI safety testing, including adversarial testing, bias/fairness validation, and jailbreak detection
Tooling (Preferred)
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AI testing & observability tools: LangSmith, TruLens, Arize, Weights & Biases
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Evaluation tools: DeepEval, Ragas, PromptFoo, Giskard
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Monitoring: Prometheus, Grafana, OpenTelemetry
Soft Skills
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Strong analytical and problem-solving skills
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Excellent communication and cross-functional collaboration
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Data-driven mindset with focus on quality KPIs
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Detail-oriented with a strong bias toward automation and scalability
Experience Requirements
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7+ years in QA, SDET, or test automation engineering
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Proven experience building and scaling automation frameworks
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Hands-on experience with AI/ML systems or LLM-based applications
Nice to have
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Experience testing RAG pipelines or agentic workflows
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Owned end-to-end AI test strategy and architecture
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Defined quality metrics and release gates
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Delivered scalable validation pipelines for production AI systems
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Supported audit and compliance readiness
Preferred
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Experience in enterprise or regulated environments (SOC2, ISO 27001, etc.)
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Exposure to:
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Shift-left testing practices
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Production observability and monitoring
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Chaos or resilience testing
Senior-Level Differentiators
Education
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Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related field
Nice-to-have:
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ISTQB certification
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Cloud/ML certifications (AWS, Azure, GCP)
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AI testing certifications
What Success Looks Like
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AI systems that are accurate, reliable, and safe
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Fully automated test pipelines integrated into CI/CD
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Measurable improvements in defect leakage and model quality
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Strong observability and auditability across AI systems
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Scalable validation frameworks supporting rapid AI innovation
We’re doing work that matters. Help us solve what others can’t.
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