I architect software systems end-to-end: from service boundaries, APIs, and data flows to cloud infrastructure, delivery pipelines, and day‑2 operations. AWS is the platform I use most, but my core value is software architecture under real constraints: reliability, cost, security, latency, maintainability, and teams that need to ship. Lately that is mostly applied to Generative AI applications.

I’m a Sr Innovation Architect (Office of the CTO) at Caylent. I also write Simple AWS, a weekly newsletter read by 45k+ engineers, architects and team leads who want clear explanations and practical patterns, not copy‑pasted architectures.

Advisory & consulting (where I’m most useful)
- Architecture reviews and “what should we build next?” roadmaps (software + cloud, GenAI)
- High-stakes tradeoffs: build vs buy, serverless vs containers, async vs sync, data modeling and boundaries
- GenAI solution architecture: production patterns, evaluations, model selection, production deployments and best practices, cost/latency tradeoffs, and integration into existing products
- Cloud-native architectures on AWS (serverless, event-driven, containers where it fits, networking and security)
- Delivery practices for code (Python, JavaScript/TypeScript) and Infrastructure as Code (CDK, Terraform, CloudFormation)
- Enablement: workshops, presales support, and technical talks for teams and leadership

Credibility (high level)
- 11+ years building software professionally; 25+ projects across startups, enterprises and SMBs
- AWS Solutions Architect Professional; AWS Community Builder and Builder of the Year award winner; former AWS Authorized Instructor
- International speaker (Argentina, US, Mexico, Colombia, and looking to expand this list)

I’m not actively job-hunting, but I’m open to selective advisory/consulting engagements (reviews, workshops, short-term roadmap work, or fractional architecture and technology support). If you think I can help, DM me.

Start reading here: simpleaws.dev
Sponsorships for Simple AWS: https://www.passionfroot.me/guilleojeda

Guille Ojeda

Helping you become a better software engineer through coding challenges that build real applications.

I’ve built and scaled high-performing teams of software engineers, shaped engineering culture, and driven technical strategy in VC-backed startups and multi-billion-dollar enterprises. I’m known for taking on underperforming teams and turning them into confident, capable, and effective engineering organisations.

I combine deep technical expertise with a pragmatic, commercially driven approach to leadership. My MBA, along with experience as a founder and startup executive, enables me to align technology strategy with business objectives and deliver measurable commercial outcomes. A servant leader at heart, I focus on mentoring, coaching, and enabling teams to deliver software that drives growth and business success.

I bring:
- 25+ years of software engineering experience across a range of technology stacks. 
- 20+ years of software engineering leadership experience from Engineering Manager to VP Engineering/CTO.
- 10+ years of shipping AI based products (across Evolutionary Algorithms, Constraint Propagation, Search and Optimisation, Neural Networks and LLMs).
- 5+ years of experience representing software engineering at board level.
- 3+ years of leveraging LLMs for software engineering.

John Crickett

“AI will replace software engineers because it can write all the code.”

That argument sounds convincing until you’ve actually built something valuable.
In a past role I built a server that unlocked over $10M a year in additional profit. Writing the code took just under two weeks. With today’s AI tools, I could probably get that done in under a week.

But that misses the point entirely.

The coding was the easy part. The real work took months and had very little to do with writing code. It involved understanding a messy domain, pressure-testing assumptions with domain experts, working through edge cases with other engineers, and making hard tradeoffs about what not to build.

Code is an output. Engineering is the legwork and judgment enables that output.

AI makes generating the output faster. The engineer still needs to do the legwork and provide the judgement.

CTO / VP Engineering / Head of Software Engineering

What happens when the big AI companies need to charge $2000 per month for their model?

What happens when the open source model I can run locally on $4000 hardware is 50% as good as the big AI companies' models?

What happens when both these things are already true and become apparent this year?

If you want to be a great software engineer, soft skills matter more than technical skills.

You need to collaborate effectively with your team, stakeholders, and increasingly, with AI coding assistants. Yes, prompting AI is also communication.

Not sure how? Here are three simple tips to become a better collaborator:

1. Communicate clearly and often: Regular updates and transparent communication help keep everyone aligned. This applies whether you're explaining requirements to a teammate or crafting prompts for an AI tool.

2. Practice active listening: Listen to understand other people's perspectives. When working with AI, review its output critically and iterate based on what it actually generated, not what you assumed it would.

3. Prioritise team success: Focus on shared wins over personal wins to foster trust and camaraderie.

As a reminder:

"The vast majority of the work at Google (and at most companies!) doesn't require genius-level intellect, but 100% of the work requires a minimal level of social skills. What will make or break your career, especially at a company like Google, is how well you collaborate with others."

Source: Software Engineering at Google, Chapter 2.

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