PinedaTec

Designing scalable systems where AI operates under control.

Software architect with 25+ years in .NET and distributed systems, currently focused on integrating AI into real architectures with reliability, governance, and operational control.

25+ Years in .NET and backend engineering
25+ Years designing complex integrations
70+ Published articles on AI and architecture
Insights from the field.
CV

Jose Manuel Rodriguez Pineda

Senior software architect focused on .NET, distributed systems, integration, and architecture decisions that hold in production.

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Distributed Systems Architecture
Event-Driven Integration
Technical Leadership & Structural Decisions
AI Integration & Agent Architectures

Capabilities

Capabilities grounded in real systems.

Architecture, integration, and leadership applied to systems that cannot afford uncontrolled decisions.

Distributed Systems Architecture

Designing resilient backend platforms for real workloads, operational constraints, and long-term evolution.

Event-Driven Integration

Connecting APIs, messaging, and storage across systems where consistency, timing, and reliability matter.

Technical Leadership & Structural Decisions

Leading architecture, engineering direction, and execution standards in complex delivery environments.

AI Integration & Agent Architectures

Designing systems where AI agents interact with real services under defined constraints, avoiding uncontrolled behavior and ensuring alignment with business logic.

AI Engineering

AI only matters when the surrounding system can control it.

Current work is focused on integrating language models, local runtimes, and agent-based patterns into architectures with explicit boundaries, observability, privacy, and operational reliability.

Controlled Context

AI components need defined context windows, data boundaries, and clear responsibility limits to remain useful and safe.

Real Integration

Value comes from connecting models to APIs, messaging, storage, and decision flows that already exist in the business.

Observable Behavior

Predictable outputs require instrumentation, review paths, and governance that treat AI as part of the architecture, not as a black box.

Approach

A methodical approach, built for executive confidence.

01

Audit & Alignment

Clarify objectives, risk posture, and investment priorities with leadership and delivery teams.

02

Blueprint & Execution

Translate strategy into working systems with tight feedback cycles and governance embedded.

03

Operational Excellence

Scale responsibly with KPIs, runbooks, and reliability baked into daily operations.

Insights

Insights from the field.

Writing on AI engineering, architecture, governance, and the reality of integrating AI into working systems.

🧠 AI doesn't reduce teams, it redefines them

🧠 AI doesn't reduce teams, it redefines them
Published: April 14, 2026

Capgemini recently announced layoffs in Spain citing AI as justification. The unions blame management; the company blames technology. Both perspectives hold merit. AI doesn't cut headcount — it redefines how teams function.

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🧠 Thinking with AI also requires defense

🧠 Thinking with AI also requires defense
Published: April 13, 2026

Using AI to validate ideas risks reinforcing what we already believe instead of sharpening our thinking. Good prompting doesn't seek confirmation — it attacks the hypothesis before defending it.

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🧠 Transformers without magic

🧠 Transformers without magic
Published: April 08, 2026

A transformer is a way of reading text. But it doesn't do it in order: it evaluates all tokens at once and decides which ones are most relevant to each other. The magic isn't magic — it's attention.

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🧠 The keyboard is no longer the limit

🧠 The keyboard is no longer the limit
Published: April 06, 2026

The keyboard had a property often overlooked: it forced thinking. Voice interfaces eliminate typing friction, but also the discipline that friction imposed.

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Ready to lead the next transformation?

Share your priorities and we will map a focused path from strategy to production.

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