Hire AI developers who think before they prompt
Hire dedicated AI developers from a staffing partner built around the Product Driven framework and the modern AI engineering stack. Our senior AI engineers in the Philippines ship LLM apps, RAG systems, AI agents, and ML pipelines for SaaS and enterprise teams. Every engineer on the bench is pre-vetted, full-time, and ready to start in 7 days.
from anthropic import Anthropic
from pinecone import Pinecone
def answer_with_rag(question: str):
hits = index.query(question, top_k=5)
context = rerank(hits, question)
return claude.messages.create(
model="claude-sonnet-4-6",
system=GROUNDED_PROMPT,
messages=[{"role": "user",
"content": f"{context}\n\n{question}"}],
)
AI teams trusted by SaaS scale-ups, enterprises, and Fortune 500s
Previously founded VinSolutions ($150M+ exit) and Stackify
I started building with LLMs in 2025 and watched them get good fast
When I first started using LLMs in 2025, it was clear that we'd be able to build amazing functionality into our software. The first thing we tried was qualifying leads, which required real analysis and numeric comparisons across noisy inputs. The models that year honestly didn't do that great of a job consistently. Fast forward a year, and the new models do that work perfectly. I describe myself as a product person first and an engineer second, somebody who is a builder. From that seat, it has never been a better time to be alive and use AI to build absolutely amazing things.
We are building three different startups inside Full Scale Ventures right now, all of them AI related, and we do a wide range of AI development for our clients on top of that. If you need developers who are knowledgeable not only on using AI to write code but on using LLMs to build features inside your software, Full Scale can help. We have hired hundreds of engineers in the Philippines over the years, every one on the bench is working with Claude, GitHub Copilot, and Cursor every day, and we staff specialists in LLM application development, RAG, agents, machine learning, and MLOps.
AI engineers, trained on Product Driven principles
Most teams adopting AI right now are shipping more code without shipping better software. The slop volume climbs, hallucinations leak into production, evals get skipped, and AI features that looked great in a demo quietly bleed budget after launch.
Full Scale AI developers are trained on something different: the Product Driven approach from Matt's book, combined with the full modern AI toolkit (Claude, GitHub Copilot, Cursor, and the OpenAI, Anthropic, and Google AI APIs). They think first, type second, and use AI for the parts where judgment doesn't add value. That combination is rare, and it is what serious AI teams should actually be hiring for in 2026.
Product Driven engineering
Our engineers are trained on the five pillars from Matt's book: Vision, Focus, Clarity, Ownership, and Courage. The result is AI developers who push back on bad product decisions, ask whether a feature should ship before they wrap an LLM around it, and own the outcome of what gets deployed. They are not order takers, and they are not prompt jockeys.
Read Product Driven, the bookAI as a thinking partner
Every AI engineer on our bench works with Claude, GitHub Copilot, and Cursor every day, and most have shipped production features built on the OpenAI, Anthropic, and Google AI APIs. They use AI to explore options, scaffold the boring parts, generate evals, and review their own pull requests before a human ever sees them. Judgment stays with the engineer, the grunt work moves to the machine.
I describe myself as a product person first and an engineer second, and from that seat, it has never been a better time to be alive and use AI to build things. But AI without product thinking is just a slop machine, and the engineers I want on my team know the difference. They reason about the product before they reach for a prompt, and they use AI for the parts where judgment doesn't matter. That's who we hire and train at Full Scale.
The AI team behind [Featured Client]
[One- to two-sentence quote about how Full Scale's AI engineers shipped the feature, model, or system that the client now relies on. Concrete over generic.]
Dedicated AI developers, starting at $35 an hour
That rate is fully loaded. Senior AI engineer in the Philippines, working full-time on your project, with payroll, benefits, HR, and equipment all handled by Full Scale. The same role hired locally in the US runs $200K to $300K a year for a senior LLM or ML engineer. The math is what drives most of our clients to call.
- Full-time, dedicated AI engineer
- Pre-vetted by senior AI reviewers
- Works your hours, your tools, your codebase
- Payroll, HR, equipment, benefits handled by us
- US-based account manager you can escalate to
- 30-day replacement guarantee if it isn't a fit
Full Scale has made the Inc. 5000 four years in a row and is Great Place to Work certified. We have been doing this since 2018, and pricing isn't the only reason clients stay with our AI development company, it's the easiest reason to call.
The reason offshore AI works here
You can also hire dedicated developers in the Philippines across every other stack we staff, with the same vetting bar, retention numbers, and engagement model that AI clients get.
English-fluent by default
The Philippines is the third-largest English-speaking country in the world. Standups, code reviews, prompt design sessions, and customer calls work the way they do with any US team member.
Real time-zone overlap
Most of our AI engineers work US business hours with 4-8 hours of real-time overlap with East and West Coast teams, so prompt iteration, eval reviews, and design decisions happen live during shared hours rather than crawling through 24-hour async handoffs.
Deep engineering talent pool
Cebu and Manila produce tens of thousands of CS, IT, and data-science graduates a year. The Philippines has been an offshore engineering home for two decades, and the AI talent pipeline has scaled with it.
Cultural alignment with US teams
Filipino engineers grow up on US business norms, US TV, and US tech culture, so agile rituals, direct feedback, and collaborative workflows feel familiar from day one. These teams integrate fast rather than needing constant management.
Writing a prompt is not the same as building an AI system
Anyone who watched a YouTube tutorial can call the OpenAI API. Building an AI feature that holds up in production requires a different bench entirely. When you outsource AI development or hire offshore AI developers, this is the gap that decides whether the project ships. Here is what we test for, and what most offshore AI staffing companies skip.
System design, not prompt tricks
A clever prompt is not an AI system. Senior AI engineers reason about retrieval strategy, evals, fallbacks, cost ceilings, and where the LLM should and shouldn't be in the loop. Most candidates can show a Streamlit demo, very few can ship a feature that holds up under real users.
Retrieval that actually works
We test for the parts of RAG that go wrong in production: chunking strategy, embedding choice, hybrid retrieval, reranking, and when to ditch a vector database for keyword search. Bad retrieval is the single most common reason RAG projects fail to ship.
Production LLM ops beyond the notebook
Real LLM engineering covers streaming responses, function calling, structured outputs, token budgets, rate-limit handling, caching, and observability with tools like Langfuse and LangSmith. Notebook prototypes rarely survive contact with a production load.
Evals before vibes
Senior AI engineers write evals before they tune prompts. They know how to build golden datasets, run regression tests on LLM outputs, and decide when a prompt change is actually an improvement versus a coin flip. Most offshore AI candidates have never written an eval.
Security, privacy, and prompt injection
AI security in 2026 is OWASP for LLMs, prompt injection defenses, data exfiltration controls, PII redaction, and tenant isolation in RAG. The engineers we hire treat user input as untrusted and structure the system so a hostile prompt can't drain your database.
Cost and latency engineering
An AI feature that costs $40 per active user a month is a P&L problem before it is a product. Our AI engineers benchmark token usage, choose the right model tier per call, cache aggressively, and route between providers. Most offshore shops bill you for tokens they never measured.
Hire dedicated AI developers for the work that actually matters
Most AI hiring conversations skip past the actual project. What kind of AI work do you need done? A production LLM app, a RAG system over your docs, an agent that runs background work, an ML model trained on your data, an AI feature wired into an existing SaaS product? As an AI development company that bills for engineering hours rather than fixed-bid projects, our developers ship across all of it. Here are the AI development services we get hired for most often.
Generative AI and LLM application development
Production LLM apps on Claude, GPT, and open-weight models. Custom AI development means real engineering around the model: structured outputs, function calling, streaming UIs, multi-turn memory, evals, and cost controls baked in from day one. We build AI features that survive contact with real users instead of falling apart the week after the demo.
Retrieval-augmented generation (RAG)
End-to-end RAG systems over your private data: ingestion, chunking, embeddings, hybrid retrieval, reranking, and grounded generation. We build the boring parts that decide whether RAG actually works, like document parsing, metadata filtering, and citation handling, on vector stores like Pinecone, Weaviate, Qdrant, and pgvector.
AI agent engineering
Autonomous and human-in-the-loop agents built with the OpenAI Agents SDK, the Anthropic Agent SDK, LangGraph, and CrewAI. We staff engineers who know how to design tool interfaces, scope agent autonomy, handle long-running tasks, and keep the agent from drifting off the rails when production data hits it.
Machine learning engineering
Custom ML models trained on your data: classification, regression, recommendation, ranking, forecasting, anomaly detection. Our ML engineers work fluently in PyTorch, TensorFlow, scikit-learn, XGBoost, and HuggingFace Transformers, and they know when a smaller model beats a fine-tuned LLM on cost and latency.
AI integration and product engineering
Embedding AI features into existing SaaS products. API integration with OpenAI, Anthropic, Google AI, and Cohere, plus streaming UIs in React and Next.js, eval pipelines, observability, and per-tenant cost controls. This is the work most engineering teams need most: making AI feel like a native part of their product rather than a bolted-on chatbot.
MLOps and AI infrastructure
Production deployment, monitoring, versioning, and scaling for ML and LLM systems. Our MLOps engineers ship with MLflow, Weights & Biases, SageMaker, Vertex AI, Azure ML, Kubeflow, and Langfuse, and they know how to keep model serving cost predictable when traffic grows 10x in a quarter.
Eight AI specializations, one staffing partner
Most AI teams need more than one role. Hire dedicated generative AI developers, senior machine learning engineers, RAG and agent specialists, and MLOps from a single vetted bench. Mix and match seniorities as the project requires.
Generative AI / LLM Engineers
Senior engineers who ship LLM-powered features end-to-end. Fluent in the OpenAI, Anthropic, and Google AI APIs, structured outputs, function calling, streaming UIs, and prompt engineering as a discipline rather than a vibe.
RAG Engineers
Specialists in retrieval-augmented generation: document parsing, chunking strategy, embeddings, hybrid retrieval, reranking, and grounded generation. They know which vector database actually fits your data and when to skip the vector database entirely.
AI Agent Engineers
Engineers who design and ship autonomous and human-in-the-loop agents. They work in the OpenAI Agents SDK, Anthropic Agent SDK, LangGraph, and CrewAI, and they understand tool-use design, scope control, and long-running task patterns.
Machine Learning Engineers
ML engineers who train custom models on your data: classification, recommendation, forecasting, ranking, anomaly detection. PyTorch, TensorFlow, scikit-learn, XGBoost, and HuggingFace Transformers, plus the feature engineering that makes the model worth shipping.
MLOps / AI Platform Engineers
Production owners for ML and LLM systems. CI/CD for models, observability with Langfuse and LangSmith, versioning with MLflow and Weights & Biases, and cost controls that survive a 10x traffic spike. They make AI releases boring in the good way.
Computer Vision Engineers
Vision specialists who ship object detection, OCR, image classification, document understanding, and video analysis. Comfortable with PyTorch, OpenCV, YOLO, Vision Transformers, and the modern multimodal models from Anthropic and OpenAI when a vision-language approach fits the problem.
NLP and Data Engineers for AI
Engineers who own the data side of AI: text processing, semantic search, embeddings pipelines, document parsing, evaluation datasets, and the data plumbing that makes the rest of the stack work. Most AI projects bottleneck here.
AI QA and Evals Engineers
QA engineers who write evals as code, build golden datasets, and run regression tests on LLM outputs. They use LangSmith, Langfuse, Promptfoo, and custom eval harnesses, and they know how to decide when a prompt change is actually an improvement.
AI expertise tuned to your industry
As an AI development company built on top of a decade of software staffing, we have placed dedicated AI developers into nearly every industry that runs production software. Domain knowledge cuts onboarding time in half, so we match engineers to projects where they have already shipped real AI features.
SaaS & Scale-ups
AI in SaaS is where most of our engagements land. Customer-facing AI features, in-product copilots, structured-data extraction, and RAG over the customer's own data. Our engineers ship features that integrate with the rest of the product instead of becoming isolated chatbots bolted onto a sidebar.
From a Claude API call to a production RAG pipeline
Whether you want to hire generative AI developers for a greenfield LLM app, hire machine learning engineers for a custom model, or outsource AI development on a RAG system, the bench covers every layer of the modern AI stack. Pick what you need. We will match an engineer fluent in it.
Hire dedicated AI developers, two ways
Most clients start with a single dedicated AI developer and grow into a full team. Either way, you get full-time engineers who sit on your standups, work your hours, and ship code against your roadmap. Both options are staff augmentation at the core: dedicated, long-term engineers embedded in your team rather than freelancers, shared resources, or a project shop on the side. See the full breakdown of how we hire dedicated AI developers across every engagement we staff. When the AI engineer also needs to ship the application around the model, you can hire dedicated full stack developers from the same bench.
Dedicated developer
Full-time, exclusive, sits on your standups.
- Full-time AI engineer assigned only to your project
- Works your hours, your tools, your codebase
- Joins your standups, reports to your tech lead
- We handle payroll, HR, equipment, retention
- Replace within 30 days if it isn't a fit
How to hire a dedicated AI developer from Full Scale
We skip the 3-6 week recruitment cycle and the cold sourcing entirely. Our bench of remote AI developers and ML engineers in the Philippines is already built and vetted, and every step below has a named owner on our side.
Discovery call
30 minutes with our team. We learn your stack, your AI roadmap, the seniority level you need, and which part of the AI stack matters most (LLM apps, RAG, agents, ML, MLOps). We don't pitch on the call, we walk through what you actually need from a hire.
Engineer match
We pull 1-3 pre-vetted AI engineers from the bench whose skills, seniority, and prior AI project experience line up with what you described. You see their full profile and their actual project history.
Technical interview
You interview the candidates the way you would interview any senior AI hire: live coding, system design over RAG or agent architectures, prompt critique, eval design, and architectural reviews. Pass anyone you don't believe in.
Contract & onboarding
Sign once. We handle every contract, payroll, equipment, and HR detail in the Philippines so you don't have an offshore entity to manage. You just get a developer.
First commit
Your AI engineer joins your standups, gets repo and model-provider access, and ships code in their first week. Our delivery managers stay involved to make sure ramp-up doesn't stall.
Full Scale vs the other ways to hire an AI developer
Every hiring path has trade-offs. Here is how a dedicated AI engineer from our AI development company compares against the alternatives most teams consider first when they want to hire AI developers.
| Feature | Full Scale | Freelancer / Upwork | Traditional offshore agency | US recruiter / FTE hire |
|---|---|---|---|---|
| Pre-vetted senior AI bench | ||||
| Time to first hire | 7 days | 1-3 days | 3-6 weeks | 6-12 weeks |
| Dedicated full-time, not shared | ||||
| Trained on Product Driven + modern AI toolkit | ||||
| Sits on your standups, your tools | ||||
| Long-term retention | 93%+ | low | varies | varies |
| Replace within 30 days if it's not a fit | ||||
| Handles payroll, HR, equipment | ||||
| US-based account management | n/a | |||
| Typical fully-loaded cost vs US | ~30-40% | varies | ~50-65% | 100% |
Real AI engineers, named and vetted
A sample of the AI engineers we are currently staffing. You'll see real names and real backgrounds during your interview round.
Builds production LLM apps end-to-end. Has shipped RAG copilots over private docs for legal and SaaS clients, with evals and observability baked in from day one.
Trains custom ML models for recommendation, ranking, and forecasting. Comfortable trading a fine-tuned LLM for a smaller model when cost and latency matter.
Designs and ships AI agents for SaaS automation: tool design, scoped autonomy, human-in-the-loop checkpoints, and long-running task patterns under real production load.
Builds CI/CD for ML and LLM systems. Versioning, observability with Langfuse and LangSmith, and cost controls that survive a 10x traffic spike.
Owns the retrieval side of RAG: ingestion, chunking strategy, hybrid retrieval, reranking. Has shipped grounded-generation systems with full citation handling.
Builds eval pipelines and golden datasets for LLM and agent systems. Catches regressions before they ship, and decides which prompt change is actually an improvement.
Engineer names are anonymized on this page. You'll see real candidates during your interview round.
The numbers behind an AI staffing partner that actually works
From the people we actually staff teams for
Full Scale's development team was pivotal in elevating our facility management software. Their expertise turned complex challenges into seamless functionalities, enhancing user experience and operational efficiency.
With Full Scale's developers, we transformed the commercial real estate landscape. Their team's proficiency in agile development and proactive communication accelerated our product release.
The team at Full Scale brought our vision to life with their development skills. They helped us navigate technical requirements with ease, resulting in a robust platform our users trust.
Deeper guides to AI hiring and development
What is an AI developer?
The role, the responsibilities, and what to test for when hiring AI engineers.
AI development outsourcing guide
When offshore AI development is the right move and what to look for in a partner.
How Full Scale hires AI engineers
The vetting process and bar we set for senior AI, LLM, and ML engineers.
Building production RAG systems
What separates a RAG demo from a RAG system that actually works at scale.
Hiring AI engineers vs ML engineers
Which role you actually need depends on the work itself, regardless of the buzzword.
AI agent engineering, explained
Tool use, scoped autonomy, and the patterns that keep agents shippable.
Hire dedicated QA engineers
AI-generated code ships faster than any team can manually review. Hire senior QA engineers who catch the slop the model wrote and the developer rubber-stamped.
Everything you wanted to know about hiring AI developers
Hire a dedicated AI developer who has actually shipped AI systems before
30-minute discovery call with the AI development company that supplies dedicated engineers and custom AI development services from the Philippines. We'll learn what you're building, walk you through which dedicated AI developers, ML engineers, RAG specialists, or agent engineers are on the bench, and you'll meet candidates within a week. You won't get pressure or a sales pitch on the call.