Daniel Martinez

Applied AI Engineer | Backend Engineer

Building applied AI systems, backend infrastructure, and research tooling that hold up in production.

Skills

Technical stack across LLM systems, backend engineering, and deployment

Agentic LLM Systems

  • Multi-agent orchestration
  • ReAct workflows
  • Tool calling
  • RAG
  • Prompt and context design

AI/ML Frameworks

  • LangChain
  • LangGraph
  • OpenAI API
  • Hugging Face
  • PyTorch
  • scikit-learn
  • Ollama

Evaluation & Reliability

  • Human-in-the-loop QA
  • Offline evals
  • Golden sets
  • Observability
  • Logs and metrics

Engineering & Backend

  • Python
  • Linux
  • FastAPI
  • REST APIs
  • Authentication
  • Structured logging

Cloud & DevOps

  • Azure
  • GCP
  • AWS
  • Docker
  • Terraform
  • GitHub Actions
  • Tailscale

Data & Storage

  • PostgreSQL
  • SQLAlchemy
  • Alembic
  • InfluxDB
  • pandas
  • NumPy

Projects

Production AI System

Quant Server MT5

2026

Agentic Systems

LangGraph-driven trading infrastructure spanning WhatsApp, broker execution, telemetry, and multi-cloud deployment.

  • Python
  • LangGraph
  • OpenAI
  • FastAPI

AI Engineering

Quant Agentic RAG

2026

Agentic Systems

Hybrid retrieval and specialist-agent orchestration for grounded quantitative research workflows.

  • Python
  • LangGraph
  • RAG
  • OpenAI

Deep RL Research

CarlaBEV

2025

Autonomous Systems

A Gymnasium-compatible BEV simulator for autonomous-driving RL, scenario design, and safety-oriented evaluation.

  • Python
  • Gymnasium
  • PyGame
  • PyTorch

Experience

AI Solutions Developer

May 2021 – Present

Consulting / Freelance

Built agentic RAG workflows, cloud-native backend services, and evaluation-aware AI systems for private clients.

AI Research Scientist

Jul 2025 – Present

Technische Universität Dresden

Developing reinforcement-learning workflows for behavior prediction in complex urban environments.

R&D Engineer

Jan 2022 – Dec 2025

CIO, A.C.

Built simulation frameworks and end-to-end perception and control pipelines for autonomous-navigation research.