# Kishore Rao

AI/ML Validator | 4 years experience | Bangalore, Karnataka

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## Professional Summary

- Experienced AI/ML Validator with 4 years of expertise in validating model outputs and ensuring production reliability
- Proficient in implementing A/B testing and canary deployment procedures for ML models
- Skilled in monitoring model performance in production and setting up automated alerts for drift detection
- Knowledgeable in defining success metrics, thresholds, and KPIs for ML model evaluation
- Experienced in gradual rollout strategies and rollback mechanisms for safe model deployment
- Familiar with data science tools, statistical analysis, and model interpretability techniques
- Collaborative professional working with ML engineers and data scientists on model validation
- Strong analytical skills for identifying model biases and performance degradation

## Technical Skills

Python, TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy, A/B Testing Tools, Canary Deployment, Monitoring Tools (Prometheus, Grafana), Statistical Analysis, Model Interpretability, Jupyter Notebook, Git, CI/CD, Cloud Platforms (AWS, GCP), DeepEval, RAGAS, PromptBench, LangSmith, LangChain Testing, Hallucination Detection, RAG Pipeline Testing, Agentic Workflow Testing, Prompt Injection Testing, LLM Safety Testing, AI Alignment Testing

## Work Experience

### AI/ML Validator

[Company name] | [Start date - End date]

4 years of experience as an AI/ML Validator at a leading AI company in Bangalore. Specialized in model validation, A/B testing, and production monitoring for machine learning applications.

### Responsibilities

- Implement A/B testing and canary deployment procedures for ML models
- Monitor model performance in production and set up alerts for drift
- Define success metrics, thresholds, and KPIs for model evaluation
- Collaborate with ML teams on model validation and deployment strategies
- Analyze model biases and ensure fairness in production
- Set up monitoring dashboards and automated reporting
- Provide recommendations for model improvements and optimizations
- Ensure compliance with ethical AI guidelines and standards
- Validate LLM-powered AI systems: hallucination detection, RAG pipeline accuracy (RAGAS), agentic workflow reliability, prompt injection security testing, and AI safety/alignment assessment
- Monitor production LLM systems with LangSmith tracing and implement continuous evaluation pipelines for deployed AI assistants

## Project Experience

### ML Ranking Model Canary Testing

Implemented canary testing for an ML ranking model with gradual rollout and monitoring, reducing production risks by 50% and ensuring stable performance. Technologies: Python, TensorFlow, Monitoring Tools, A/B Testing Tools.

### Recommendation Engine Validation

Validated model outputs for a recommendation engine, setting up performance monitoring and alerts, achieving 95% accuracy in drift detection. Technologies: PyTorch, Pandas, Grafana, Statistical Analysis.

### Image Classification Model A/B Testing

Designed A/B testing procedures for image classification models, analyzing results and providing recommendations for model improvements. Technologies: Scikit-learn, NumPy, Jupyter Notebook, Cloud Platforms.

### RAG Pipeline and LLM Hallucination Validation

Designed and implemented validation framework for an enterprise RAG-based knowledge assistant, using RAGAS to measure faithfulness, answer relevancy, and context recall. Implemented DeepEval tests for hallucination detection and LangSmith-based tracing for production monitoring. Added adversarial prompt injection tests with PromptBench. Reduced hallucination rate by 45% pre-deployment. Technologies: RAGAS, DeepEval, LangSmith, PromptBench, RAG Pipeline Testing, Hallucination Detection, Prompt Injection Testing, Python, AWS.

## Education

MSc in Computer Science from IIT Bangalore, Bangalore (2018-2020, 8.5 CGPA)

## Certifications

Certified Machine Learning Engineer, AI Ethics Certification, AWS Certified Machine Learning - Specialty

## Achievements

Awarded "AI Validation Excellence" for successful canary deployments; Reduced model drift incidents by 60%; Published paper on A/B testing for ML models; Led team in implementing ethical AI monitoring