• Designed and deployed AI agent testing frameworks integrating GPT-4, BERT, Whisper, and domain-specific LLMs to validate multi-agent workflows and ensure accurate, context-aware responses.
• Engineered RAG pipelines using LangChain, LangGraph, and LlamaIndex with vector databases (Pinecone, Qdrant, Weaviate, Chroma, FAISS) for semantic search, knowledge retrieval, and functional validation of LLM-powered agents.
• Generated and fine-tuned embeddings for banking knowledge bases, improving retrieval accuracy and testing context-aware AI responses by 45%.
• Developed backend microservices in FastAPI and Flask with REST and GraphQL APIs to simulate agent interactions with internal systems and support automated testing scenarios.
• Implemented interactive dashboards using Streamlit and React.js with Tailwind CSS and Material UI to monitor AI agent performance, conversation logs, and test results.
• Applied advanced prompt engineering and LLM fine-tuning using QLoRA, PEFT, DeepSpeed, and Hugging Face Transformers to evaluate and reduce AI hallucinations by 20%, ensuring relevance and reliability.
• Ensured AI safety, compliance, and auditability with Guardrails.ai, differential privacy, and secure handling of sensitive banking data during testing workflows.
• Managed multi-cloud deployment (AWS, Azure OpenAI, GCP Vertex AI) and containerized testing environments with Docker, Kubernetes, and Helm for reproducible agent testing.
• Implemented CI/CD pipelines with GitHub Actions, Jenkins, GitLab CI, and Terraform to automate deployment, rollback, and version control of LLM-based testing frameworks.
• Built analytics and observability pipelines capturing test case execution, conversation metrics, and model performance using Python, Pandas, NumPy, Streamlit, Plotly, ELK Stack, Prometheus, and Grafana.
• Developed escalation and validation workflows to route complex or failed test scenarios to human review, improving QA coverage and reducing error handling overhead by 35%.
• Conducted LLM benchmarking, functional testing, and performance optimization for latency, accuracy, and cloud efficiency, achieving ~30% improvement in processing speed.
• Mentored junior engineers and collaborated with stakeholders via JIRA and Confluence, providing guidance on Python, FastAPI, Streamlit, embeddings, multi-agent testing, and AI validation best practices.

• Designed and deployed AI agent testing frameworks integrating GPT-4, BERT, Whisper, and domain-specific LLMs to validate multi-agent workflows and ensure accurate, context-aware responses.
• Engineered RAG pipelines using LangChain, LangGraph, and LlamaIndex with vector databases (Pinecone, Qdrant, Weaviate, Chroma, FAISS) for semantic search, knowledge retrieval, and functional validation of LLM-powered agents.
• Generated and fine-tuned embeddings for banking knowledge bases, improving retrieval accuracy and testing context-aware AI responses by 45%.
• Developed backend microservices in FastAPI and Flask with REST and GraphQL APIs to simulate agent interactions with internal systems and support automated testing scenarios.
• Implemented interactive dashboards using Streamlit and React.js with Tailwind CSS and Material UI to monitor AI agent performance, conversation logs, and test results.
• Applied advanced prompt engineering and LLM fine-tuning using QLoRA, PEFT, DeepSpeed, and Hugging Face Transformers to evaluate and reduce AI hallucinations by 20%, ensuring relevance and reliability.
• Ensured AI safety, compliance, and auditability with Guardrails.ai, differential privacy, and secure handling of sensitive banking data during testing workflows.
• Managed multi-cloud deployment (AWS, Azure OpenAI, GCP Vertex AI) and containerized testing environments with Docker, Kubernetes, and Helm for reproducible agent testing.
• Implemented CI/CD pipelines with GitHub Actions, Jenkins, GitLab CI, and Terraform to automate deployment, rollback, and version control of LLM-based testing frameworks.
• Built analytics and observability pipelines capturing test case execution, conversation metrics, and model performance using Python, Pandas, NumPy, Streamlit, Plotly, ELK Stack, Prometheus, and Grafana.
• Developed escalation and validation workflows to route complex or failed test scenarios to human review, improving QA coverage and reducing error handling overhead by 35%.
• Conducted LLM benchmarking, functional testing, and performance optimization for latency, accuracy, and cloud efficiency, achieving ~30% improvement in processing speed.
• Mentored junior engineers and collaborated with stakeholders via JIRA and Confluence, providing guidance on Python, FastAPI, Streamlit, embeddings, multi-agent testing, and AI validation best practices.