Google Vertex AI
• Experience building AI/ML solutions using Google Vertex AI on Google Cloud Platform (GCP)
• Hands-on experience with Google ADK, LangGraph, and Agentic AI frameworks
• Strong expertise in LLM & Agent Evaluation Frameworks, including trajectory/trace-based evaluation, tool-calling & planning metrics, Pass@k multi-run evaluation, and LLM-as-a-Judge / Agent-as-a-Judge methodologies
• Experience with Prompt Engineering, Retrieval-Augmented Generation (RAG), Knowledge Graphs, Semantic Modeling, and Vector Databases
• Experience implementing AI Guardrails, AI Governance, Model Monitoring, Evaluation Gating, and Responsible AI practices
• Strong Python development skills with experience in FastAPI, Pandas, NumPy, PyTest, and REST API development
• Experience with OCR and Intelligent Document Processing (IDP) using Google Document AI, Tesseract OCR, Azure Document Intelligence, or Amazon Textract
• Hands-on experience with Named Entity Recognition (NER), document classification, information extraction, text preprocessing, tokenization, embeddings, semantic search, and NLP pipelines
• Experience with fuzzy string matching and record linkage algorithms including Jaro-Winkler, Levenshtein Distance, Damerau-Levenshtein, Jaccard Similarity, Cosine Similarity, TF-IDF, FuzzyWuzzy/RapidFuzz, and entity resolution techniques
• Strong understanding of Machine Learning, Data Science, Statistical Analysis, Feature Engineering, Classification, Regression, Clustering, Recommendation Systems, and model evaluation metrics
• Experience with AI evaluation and observability tools such as Braintrust, Arize Phoenix, OpenTelemetry, Promptfoo, Galileo, DeepEval, Confident AI, Ragas, Langfuse, and LangSmith
• Hands-on experience with Google Kubernetes Engine (GKE), Cloud Run, Docker, Kubernetes, and cloud-native application deployment on GCP
• Experience implementing security controls including RBAC, Data Redaction, PII Detection & Masking, Secrets Management, and AI security governance
• Strong understanding of cloud-native architectures, CI/CD pipelines, containerized deployments, MLOps, LLMOps, software engineering best practices, and production AI deployment
• Excellent analytical, problem-solving, stakeholder communication, technical documentation, and solution architecture skills
