Jennifer V.

Key Skills

Software

Scale AI

Top Subject Matter

Enterprise intelligence

LLM-powered workflows

retrieval-augmented generation

Top Data Types

Document

Text

Top Task Types

Function Calling

Freelancer Overview

Machine Learning Engineer at Palantir Technologies (agentic AI, RAG, and AI evaluation/testing). Brings 13+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Scale AI. Education includes Bachelor of Science, Florida State University (2012) and Master of Science, Florida State University (2014). AI-training focus includes data types such as Document, Computer Code, and Programming and labeling workflows including Evaluation, Rating, and Function Calling.

Labeling Experience

Senior AI Full Stack Engineer at Robinhood (LLM agents, RAG, and AI output validation)

DocumentFunction Calling

Led enterprise-scale AI agent platforms and retrieval systems that automate complex financial operations using LLM orchestration and knowledge retrieval. Designed production-grade RAG platforms for compliance and operational knowledge retrieval using embeddings and vector search. Implemented validation testing frameworks for AI-generated outputs with automated evaluation pipelines, confidence scoring, human-in-the-loop review workflows, and monitoring. • Developed agentic workflows combining OpenAI models, LangGraph orchestration, and tool usage/function calling. • Built RAG knowledge retrieval from compliance documents and transaction/operational repositories. • Implemented automated evaluation, confidence scoring, and human-in-the-loop review for AI outputs. • Established observability/monitoring standards using AI-specific evaluation metrics to sustain reliability.

2024 - Present

Machine Learning Engineer - Palantir Technologies

Scale AI

Designed and deployed large-scale AI and machine learning platforms to support enterprise intelligence initiatives. Led agentic AI development by orchestrating specialized agents for retrieval, reasoning, planning, summarization, and workflow execution. Built full-stack applications including React-based interfaces and Python-based REST APIs with deployment on cloud-native infrastructure. • Built RAG systems integrating structured databases, operational repositories, and unstructured knowledge sources • Developed Python microservices and REST APIs for high-availability, low-latency AI-powered applications • Implemented prompt engineering strategies, retrieval optimization, and evaluation frameworks to improve reliability • Provided technical leadership through architecture reviews, code reviews, mentoring, and testing frameworks

2022 - 2024

Machine Learning Engineer at Palantir Technologies (agentic AI, RAG, and AI evaluation/testing)

Document

Built AI and ML systems that rely on retrieving and generating contextual outputs from enterprise knowledge sources, including validation and evaluation of AI-generated responses. Implemented prompt engineering, retrieval optimization, and evaluation frameworks to improve reasoning quality, response relevance, and reliability. Designed testing coverage for AI validation, frontend, API integration, and regression to ensure production readiness of AI workflows. • Created evaluation frameworks for AI reasoning and response quality. • Implemented testing and regression strategies for AI validation. • Worked on retrieval systems that provide labeled/grounded context from structured and unstructured sources. • Delivered enterprise RAG and agentic workflows requiring continuous quality checks.

2022 - 2024

Education

Work History