AI Engineer & Solutions Architect
Struggling to get your AI proof-of-concept past the demo stage? I help companies architect, build, and deploy production-grade AI systems — from RAG pipelines to agentic workflows — on cloud infrastructure that won't break at scale.
If any of these keep you up at night, we should talk.
You've built a promising LLM or RAG prototype, but it falls apart under real traffic, throws hallucinations, or can't handle edge cases at enterprise scale.
I've taken RAG pipelines and agentic AI from prototype to production serving 90 million users. I handle the hard parts — retrieval tuning, latency optimization, vector DB scaling, and LLM governance — so your AI actually works when it matters.
Your legacy system has become a bottleneck. Deployments take hours, a single change risks breaking everything, and your team spends more time firefighting than building.
I've led monolith-to-microservices migrations that cut deployment time by 40% and reduced infrastructure costs by 15%. I design the architecture, execute the migration strategy, and make sure nothing breaks along the way.
Your data scientists build models that can't scale. Your backend engineers don't understand LLMs. You need someone who bridges both worlds and delivers end-to-end.
With 5+ years in enterprise Java/Spring Boot and deep expertise in LLMs, LangChain, and vector databases, I'm the engineer who can design the AI pipeline and build the production infrastructure it runs on.
Your industry is regulated. You can't just plug in an API and hope for the best. You need AI solutions that comply with data privacy rules and pass architecture reviews.
At GCash, I defined AI/ML governance standards for <100 architects under BSP regulations. I design AI solutions that satisfy Architecture Review Boards and meet enterprise data privacy requirements from day one.
I'm Miguel Recido — an AI Engineering Leader & Solutions Architect who bridges the gap between data science prototypes and production engineering, ensuring AI solutions are robust, compliant, and built to last.
Most AI projects fail not because the model is bad, but because nobody planned how to run it at scale. I specialize in the unglamorous but critical work: designing retrieval pipelines that don't hallucinate, building microservices that handle millions of requests, and deploying on cloud infrastructure that stays up.
My track record speaks in production metrics: 90M+ users served, 15% infrastructure cost reduction, 40% faster deployments, 30% data processing efficiency gains — all achieved on real enterprise systems, not toy projects.
RAG, LLMs, agentic AI — from prototype to production-grade, not just a demo
AWS, GCP, Docker, Kubernetes (CKAD certified) — infrastructure that handles real load
Java, Spring Boot, Angular, Python — I own the full stack, not just a slice
Compliance-first architecture for regulated industries like fintech and insurance
Each engagement is tailored to your specific challenge. Here's where I deliver the most impact.
Your system is tangled, deployments are risky, and scaling feels impossible.
I design clean, scalable architectures using C4 diagrams, SAGA patterns, and proven cloud-native practices. Whether you need a monolith broken apart or a greenfield system designed right from the start — I architect systems that your team can actually maintain and scale.
Your AI prototype is impressive but nowhere near production-ready.
I build production-grade AI systems — RAG pipelines with PGVector, agentic workflows with LangChain, and LLM integrations (Gemini, Llama) that are optimized for latency, accuracy, and cost. End-to-end, from model selection through deployment and ongoing governance.
You need robust APIs and a solid backend, not just a pretty frontend.
I build high-performance, testable web applications using Java Spring Boot and Angular. With 90%+ test coverage as standard, SOLID principles, and clean architecture — I deliver code that your next engineer will actually understand and be able to extend.
Your deployments are manual, fragile, and take your team's entire afternoon.
I set up CI/CD pipelines, containerize applications, and orchestrate deployments with Kubernetes (CKAD certified). The result: automated, repeatable releases that your team can trust — whether you're deploying a Spring Boot service or an AI inference endpoint.
Focus: AI/Data Governance
Staff Augmentation
Staff Augmentation — Core Banking
Mobile & Cloud Banking
A mission-critical disaster resilience tool utilizing an agentic AI ecosystem for autonomous status monitoring and predictive analysis of weather and flood risks.
Led architectural standards and AI/ML governance for the Philippines' leading e-wallet serving 90M+ users. Drove microservices ecosystem optimization reducing infrastructure costs by 15%.
Developed enterprise insurance features with robust APIs feeding downstream analytics and AI systems. Achieved 90%+ test coverage with clean, modular architecture.
Engineered batch processing and led monolith-to-microservices migration for core banking systems. Achieved 30% data processing efficiency gains and 40% faster deployments.
Pioneer cloud migration of the Mobeix retail banking platform, reducing IT costs by 20% and boosting developer productivity by 15%. Built high-impact features driving 10% user engagement growth.
Linux Foundation
Dec 2025 – Dec 2027
Proficiency in designing, building, and deploying cloud-native applications on Kubernetes for scalable LLM and microservice deployment.
Udemy
Self-Paced Course Completed
Spring Boot, Spring Cloud, Docker, Kubernetes, and REST API
Self-Paced Course
Completed
Comprehensive containerization and orchestration training
Government Certification
March 11, 2020
Philippine Civil Service Commission professional examination
Reach out through any of the channels below, or book a discovery call directly.
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