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Principal Software Engineer

Cadence
Bengaluru, IND
Full-time
Senior · 7+ years experience
Onsite
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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
  • 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
  • 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)
  • 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

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)

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

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

Shift-left testing practices

Production observability and monitoring

Chaos or resilience testing

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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