Laderick F.

Key Skills

Software

Other

Top Subject Matter

Financial services / wealth management knowledge platforms

Legal Services & Contract Review

Regulatory Compliance & Risk Analysis

Top Data Types

Document

Text

Image

Top Task Types

Question Answering

Tracking

Freelancer Overview

Senior AI Full Stack Developer (RAG, semantic search, LLM evaluation) at BlackRock. Brings 13+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other. Education includes Bachelor of Science, University of Central Florida (UCF) (2013). AI-training focus includes data types such as Document and Text and labeling workflows including Question Answering, Tracking, and Prompt-Response Writing.

Labeling Experience

Senior AI Full Stack Developer - BlackRock

TextTracking

Led the development of AI-powered wealth management applications for enterprise users, integrating modern web and AI services to support advisor workflows. Designed retrieval-augmented generation pipelines using vector search and embeddings to improve answer relevance and trust in financial knowledge experiences. Delivered production-ready REST APIs and user interfaces while applying Azure cloud engineering, LLMOps, and observability practices for reliability, security, and maintainability. • Built microservice-based architectures with TypeScript, React, Next.js, Python, FastAPI, and Node.js • Implemented RAG pipelines with Pinecone, OpenAI embeddings, chunking, and metadata enrichment • Orchestrated LLM workflows with FastAPI and Node.js, including authentication and monitoring • Applied LLMOps practices such as prompt versioning, caching, cost monitoring, telemetry, and evaluation workflows

2024 - Present

Senior AI Full Stack Developer (RAG, semantic search, LLM evaluation) at BlackRock

OtherDocumentQuestion Answering

Built and evaluated RAG pipelines that generate answers grounded in enterprise documents and enriched metadata. Focused on document chunking and embedding workflows to improve retrieval relevance and contextual accuracy. • Chunked and indexed financial/knowledge documents for retrieval • Used OpenAI embeddings and Pinecone vector storage for semantic lookup • Enriched metadata to improve answer grounding and trust • Implemented LLM evaluation workflows to monitor response quality

2024 - Present

Education

Work History