Relevant Experience & Education Highlights

Demonstrates strong alignment with Data Platform needs through hands-on experience building Vision Language Models, RAG systems, and agentic workflows at scale in enterprise environments.

1. Build and improve state-of-the-art multimodal LLMs to maximize document understanding performance

Applied expertise in vision language models and generative AI directly supports enhancing document processing capabilities.

• Developed image search engine leveraging Vision Language Models at a leading research and advisory firm.
• Implemented transcript and article summarization using GenAI, LLMs, and RAG techniques.
• Gained certifications in model parallelism for large neural networks and data parallelism across multiple GPUs.

2. Design and implement comprehensive evaluation pipelines for E2E agentic RAG workflows

Experience with agentic recommendation systems and RAG aligns with creating robust evaluation frameworks for retrieval-augmented generation.

• Engineered agentic recommendation systems incorporating GenAI, LLMs, VLMs, and RL at a major retail corporation.
• Led weekly paper reading and knowledge sharing sessions to evaluate state-of-the-art advancements in AI.
• Applied RAG for summarization tasks, demonstrating end-to-end workflow proficiency.

3. Architect and build streaming infrastructure, data orchestration systems, vector databases

Background in distributed systems and cloud platforms equips for designing scalable data services handling petabyte-scale ingestion.

• Utilized Ray and Hadoop for distributed computing in machine learning pipelines at a financial services company.
• Leveraged Google Cloud Platform for scalable data processing in fraud detection and NLP applications.
• Diagnosed and optimized systems for multimodal data handling, including vector database familiarity for low-latency retrieval.

Requirements & Candidate Alignment