Description:
As an innovative AI/ML Engineer, I design and deploy production-ready AI pipelines that convert your company's unstructured data—such as documents, transcripts, and customer interactions—into a powerful, queryable knowledge base driving strategic decisions.
While others may offer basic chatbots, I excel in crafting scalable Retrieval-Augmented Generation (RAG) systems. My focus is on creating comprehensive, automated workflows that ingest raw data, enrich it with AI insights, and enable intelligent querying for real business impact, particularly in fintech and healthcare sectors thriving in tech hubs like San Francisco.
My services provide a full-spectrum, "zero-to-one" solution for unlocking data potential:
- Strategic Architecture & Planning:
I begin with a thorough assessment of your challenges, crafting a tailored, efficient architecture that balances cost, scalability, and performance.
Proficient in the latest AI ecosystem, including cloud tools (Pinecone, Google AI Studio, Cohere) and privacy-focused options (local embeddings with Sentence-BERT), I ensure solutions align with your security needs.
- Comprehensive Pipeline Build:
Ingestion: Develop robust pipelines to source data from APIs, databases, or webhooks.
Enrichment & Processing: Leverage advanced models (DistilBERT, XGBoost, TensorFlow, PyTorch) for feature extraction, summarization, and structured output (e.g., JSON schemas).
Semantic & Structured Storage: Expert in embedding strategies and vector databases like Pinecone, paired with relational stores (SQL, Postgres) for hybrid search and analytics.
- Intelligent Retrieval & Response Generation:
I implement agentic endpoints (using Flask or FastAPI) that handle natural language queries—like "Identify at-risk customers"—by retrieving from semantic layers, synthesizing insights, and delivering actionable responses with tools like Streamlit for visualization.
Proven Execution Highlights:
Top-5 Finish, Google GenAI Hackathon: Led development of SwiftCareAI, a real-time AI triage system for healthcare, using Cohere NLP and Streamlit.
Top-10% Finish, Zindi Challenge: Engineered a predictive ML pipeline for energy forecasting with XGBoost and time-series features.
Fintech Consulting: For a US fintech startup, designed a RAG-based LLM system with DistilBERT and Pinecone for automated transaction categorization and tax optimization.
Key Projects: Architected JobSage, an AI mock interview simulator with adaptive feedback using Google AI Studio and Sentence-BERT; Built an NCAA prediction engine with TensorFlow for advanced feature engineering.
If you're a founder or executive in San Francisco's dynamic tech scene, ready to harness your data as a competitive edge, I'm the expert to build that capability. Let's collaborate to elevate your AI initiatives.