Principal Software Engineer

Cadence Design Systems; Bengaluru; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Cadence Design Systems in Bengaluru is seeking a Senior AI Test / Automation Engineer to design and scale automated validation frameworks for AI/ML models, LLM-based applications, and agentic systems. You will ensure quality, reliability, safety, and compliance across production deployments.

Key responsibilities include building AI test frameworks, developing comprehensive test suites, validating non-deterministic outputs, and automating testing within CI/CD pipelines.

Qualifications

• 7+ years in QA, SDET, or test automation engineering
• 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

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 testing
• Validate AI system behavior including non-deterministic outputs and edge cases
• Design and manage evaluation systems with golden datasets and benchmarking pipelines
• Automate testing within CI/CD pipelines for model updates and tool integrations
• Implement observability and telemetry for traceability and audit readiness
• Track and report quality KPIs like test coverage and defect leakage
• Drive root-cause analysis and continuous improvement across the AI testing lifecycle

Skills

Python

Bash

TypeScript

Go

Education

Bachelor’s or Master’s in CS/SE

Tools

PyTest

Playwright

Selenium

Cypress

GitHub Actions

Jenkins

GitLab CI

Docker

Kubernetes

AWS

Azure

GCP

Job description

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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