Job Description

• 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

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