Anderson Nascimento Silva
Senior Principal Senior Lead Ai Engineer @ZS
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WORK HISTORY
Senior Principal Senior Lead Ai Engineer @ZS
Vancouver, WA, US
As a core contributor to the BI Assistant, I helped design and implement a multi-agent system that enables users to query structured and unstructured data seamlessly. The assistant generates SQL queries, surfaces relevant insights, and creates visualizations. All while correlating information across different data modalities. Our goal was to deliver high-quality answers with fast turnaround (under 20s avg.), and we hit that through a thoughtful combination of multi-agent architecture, RAG pipelines, and aggressive caching strategies.The system is built using Python, Langchain, LangGraph, FastAPI, and deployed with Docker + Kubernetes, integrating pgvector for semantic search and Langfuse for observability. We used LiteLLM to optimize model routing and control cost/performance trade-offs.Beyond hands-on development, I also played a key role in maturing our engineering culture. I focused on raising the bar for code quality, testing, and performance. This meant introducing better dev workflows, enforcing solid pull request practices, promoting unit testing, and leading efforts around observability and load testing.
EDUCATION
Universidade do Sul de Santa Catarina
Third level degree, JAVA application development
Universidade do Sul de Santa Catarina
Bachelor, information Systems
Pontifícia Universidade Católica do Rio Grande do Sul
Data Science Specialization
SATC - Associação Beneficente da Indústria Carbonífera de Santa Catarina
Techinician, Industrial Informatics
ABOUT ANDERSON NASCIMENTO SILVA
I’m a Software Engineer specialized in Machine Learning and Generative AI, with 10+ years of experience delivering intelligent systems and scalable infrastructure across startups and enterprise tech environments.I enjoy being hands-on, data-driven, and outcome-focused, and what drives me is turning complex problems into impactful, real-world AI solutions. Whether it\'s building LLM-based applications, designing Feature Stores, deploying real-time inference platforms, or optimizing model performance at scale.At companies like Dell and ZS Associates, I’ve led key ML initiatives: launching a Lakehouse that unlocked $1M in strategic funding, reducing model runtime by 30% through smarter architecture, and developing frameworks for evaluating and optimizing LLMs. I thrive in diverse and cross-functional teams, collaborating with data scientists, product managers, and engineers to build future-proof AI solutions.My stack includes Python, PyTorch, Spark, MLFlow, Airflow, FastAPI, Kubernetes, and all things MLOps. I’m also passionate about prompt engineering and using AI responsibly to create products that are not just smart but truly useful.
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