Deep Learning Architect at AWS

Dimas Jackson, PhD

Building scalable AI for Financial Service Institutions at the AWS Gen AI Innovation Center. I design agentic systems, LLM pipelines, and secure cloud architectures that turn R&D into reliable enterprise outcomes.

São Paulo, Brasil AWS Gen AI Innovation Center Financial Service Institutions

Deep Learning Agentic AI Amazon Bedrock RAG Prompt Engineering

Current role

Deep Learning Architect @Amazon

Driving generative AI and agentic AI architecture for Financial Services clients.

Recent impact

$2M ARR

Delivered AI-powered conversational experiences that accelerated sales performance.

Engineering outcome

90% efficiency gain

Architected code remediation agents to accelerate compliant adoption of Brazil’s alphanumeric CNPJ standard.

Summary¶

I'm an architect with deep expertise in AI, cloud engineering, and financial services. I build end-to-end systems that combine LLMs, autonomous agents, and secure AWS deployments to deliver measurable business value.

What I do

Design scalable AI solutions on AWS for FSIs and enterprise teams
Build agentic systems that automate compliance, code remediation, and workflows
Lead pilots with Amazon Q, Bedrock, MCP, Databricks, and multi-cloud integrations

Why it matters

Turns AI prototypes into production-ready systems with governance
Reduces developer risk while improving operational efficiency
Aligns AI architecture with business outcomes and enterprise strategy

Experience¶

November 2025 – Present

Deep Learning Architect, AWS

AWS Gen AI Innovation Center, São Paulo

Led Agentic AI initiatives for Financial Services clients, delivering AI-powered conversational experiences and driving $2M ARR.

July 2024 – November 2025

Senior AI Architect, Santander

São Paulo, Brasil

Architected an AI agent to detect and remediate legacy CNPJ code with an estimated 90% reduction in time and cost.
Designed a global-scale speech recognition architecture and generative AI solution.
Validated enterprise pilots using Amazon Q, A2A, MCP, Databricks, and Power BI.

May 2023 – July 2024

Data and AI Consultant, Mackenzie

São Paulo, Brasil

Built knowledge graph and natural language search solutions over institutional datasets.
Improved SQL procedures performance by up to 400%.

March 2021 – May 2023

Research Fellow, CAPES

São Paulo, Brasil

Led advanced machine learning and Bayesian inference research for General Relativity applications.
Published peer-reviewed articles in top-tier journals and presented findings at international conferences.
Developed computational models and validated results with rigorous theoretical analysis.