IT Consultant Software Engineer Philippines
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HIRE PYTHON DEVELOPERS — AI/ML

Hire Filipino Python Developers for AI/ML Systems

Senior Python engineers with shipped AI/ML in production. LLM orchestration with OpenAI / Anthropic / Gemini APIs, RAG pipelines with vector DBs, agent frameworks, model fine-tuning, evaluation harnesses — the actual production AI/ML work, not just demos.

AI/ML hiring exploded in 2023-2025 and the bench is finally deep enough in the Philippines to source quality. The Filipino Python AI/ML engineers I source have shipped production LLM-backed products: RAG-based chatbots, semantic search, agent workflows, fine-tuned domain models, evaluation pipelines. Real production systems, not Streamlit demos.

$40-65

USD/hr direct (60% below US AI/ML)

4+

LLM providers with production experience

5+

Vector DBs in production use across the bench

Why hire AI/ML-experienced Python devs from the Philippines

1

Production LLM orchestration is real-world experience

Not "I built a chatbot demo." Vetted bench has shipped LLM-backed products with real users: prompt engineering with versioning, retry logic across providers, cost monitoring, content moderation, evaluation harnesses for non-deterministic outputs.

2

RAG pipeline depth (chunking, embedding, retrieval, reranking)

Semantic search and RAG are stack-heavy: text chunking strategies, embedding model choice, vector DB selection (Pinecone vs Weaviate vs pgvector vs Qdrant), retrieval scoring, reranking with cross-encoders. Vetted bench has shipped multiple RAG implementations.

3

Vector DB and embedding strategy fluency

Familiar with the major vector DBs and the tradeoffs (Pinecone for managed simplicity, Weaviate for open-source flexibility, pgvector for "just use postgres", Qdrant for performance). Embedding model choice (OpenAI vs Cohere vs sentence-transformers).

4

$40-65/hr — AI/ML premium reflects thin bench + high demand

AI/ML-experienced Python devs command roughly 25-40% over generalist Python rates. Still 60% below equivalent US AI/ML rates ($150-280/hr).

What this engagement covers

  • Senior Python engineers with shipped production LLM-backed products
  • LLM orchestration with OpenAI, Anthropic, Google, Cohere APIs (multi-provider with fallback)
  • RAG pipeline build (text chunking, embedding, vector DB, retrieval, reranking, generation)
  • Vector DB integration: Pinecone, Weaviate, pgvector, Qdrant, Chroma
  • Agent framework experience: LangChain, LlamaIndex, custom agent runtimes
  • Model fine-tuning (HuggingFace ecosystem, OpenAI fine-tuning API, LoRA adapters)
  • Evaluation harnesses for non-deterministic LLM outputs (LangSmith, Phoenix, custom)
  • Production observability for AI systems: cost tracking, latency monitoring, output drift detection

Frequently asked questions

How do I tell a "production AI/ML dev" from someone who built a Streamlit demo?

Ask: "Walk me through your evaluation harness for the last LLM-backed product you shipped." A demo builder cannot answer this; a production AI/ML dev describes the eval framework, the test set, how they detect drift, what they do when a new model version performs worse on key tests, and how they handle regression in non-deterministic outputs.

Which LLM providers does the bench have production experience with?

OpenAI (most common, GPT-4 family), Anthropic (Claude family — growing share), Google (Gemini — growing), Cohere (specialty for embeddings + RAG). Multi-provider with fallback is increasingly the norm for production reliability.

Vector DB experience — which ones?

Production experience across Pinecone (most common, managed), Weaviate (open-source, flexible), pgvector (when "just use postgres" wins), Qdrant (for performance), Chroma (early-stage prototyping). We match the shortlist to your stack.

Can they do model fine-tuning, or just API integration?

Both, but split. Most senior AI/ML bench is strong at API-level orchestration (RAG, agents, prompt engineering). Fine-tuning experience is thinner but available — request specifically. Common fine-tuning work: HuggingFace transformers + LoRA, OpenAI fine-tuning API, domain-specific models for specialized use cases.

How fast from "we need an AI/ML dev" to onboarded?

Typical: shortlist in 7-14 business days (AI/ML bench is thinner so longer search), interviews over 1-2 weeks, paid trial week immediately. End-to-end usually 4-6 weeks to fully onboarded.

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