AI Infrastructure Engineer

360-365 Marketing OPC; Taguig; On-site; PHP 1,674,000 - 2,790,000; Full time

Job summary

360-365 Marketing OPC is seeking a highly technical AI Engineer / MLOps Specialist to lead a core infrastructure migration toward a fully self-hosted, open-source AI architecture on AWS. You will own end-to-end pipelines, including data ingestion, vector database management, and secure LLM serving 24/7.

You should excel at allocating GPU VRAM, optimizing inference speeds, and managing bare-metal Linux servers, with a focus on cost efficiency and data privacy.

Qualifications

• AI / MLOps: Proven experience downloading raw model weights (Hugging Face) and serving locally on GPUs.
• Cloud Infrastructure: Hands-on AWS experience, spinning up, configuring, and securing persistent Linux EC2 VMs.
• Data Engineering / Python: Strong Python skills; proficient with modern web scraping libraries and handling messy HTML data.
• RAG Architecture: Deep understanding of embedding models, semantic chunking, and vector databases.
• DevOps: Highly proficient in Docker, NVIDIA Container Toolkit for GPU-accelerated apps. Bonus points for bringing SLM migration, advanced RAG, and reverse-proxy tooling.

Responsibilities

• Infrastructure Migration: Move from managed APIs to self-hosted open-source models (e.g., Llama 3, Qwen) on AWS EC2 GPUs.
• Data Ingestion & Scraping: Build Python pipelines/scrapers to daily fetch unstructured data from external sources.
• Vector Database Management: Embed text into a vector DB with upsert logic to avoid duplicates.
• Local LLM Serving: Deploy models with optimized engines; apply quantization (GGUF/AWQ) to prevent OOM.
• Backend Integration: Expose RAG via a secure, high-concurrency REST API (FastAPI).
• Cloud Security: Implement VPC routing, IAM roles, Security Groups for AWS EC2.

Skills

AI / MLOps

Cloud infrastructure

Python

RAG architecture

DevOps

Docker

NVIDIA Container Toolkit

Tools

Playwright

BeautifulSoup

Scrapy

Job description

We are looking for a highly technical AI Engineer / MLOps Specialist to lead a core

infrastructure migration. We are currently transitioning from managed AI services to a fully

self-hosted, open-source AI architecture on AWS to optimize operating expenses and increase

data privacy and control.

You will be responsible for the end-to-end pipeline: building automated data ingestion systems,

managing a vector database, provisioning AWS GPU servers, and ensuring an open-source

Large Language Model (LLM) runs securely 24/7.

Note: If your AI experience is limited to calling managed APIs (like OpenAI or Anthropic), this

role is not for you. We need someone who knows how to allocate GPU VRAM, optimize

inference speeds, and manage bare-metal Linux servers.

What You Will Build (Core Responsibilities):

• Infrastructure Migration: Transition existing generative AI workflows off managed cloud APIs and onto self-hosted open-source models (e.g., Llama 3, Qwen, Gemma) hosted on AWS EC2 GPU instances.
• Data Ingestion & Scraping: Build and maintain robust Python automated pipelines/scrapers that run daily to extract unstructured data from external web sources.
• Vector Database Management: Clean, chunk, and embed the extracted text into a Vector Database (preferably PostgreSQL + pgvector). Implement strict "upsert" logic to ensure daily updates do not create duplicate vectors.
• Local LLM Serving: Deploy the open-source model using optimized inference engines (e.g., vLLM, Ollama, llama.cpp). Apply quantization techniques (GGUF, AWQ) where necessary to maximize hardware efficiency and prevent Out of Memory (OOM) crashes.
• Backend Integration: Wrap the RAG (Retrieval-Augmented Generation) pipeline in a secure, high-concurrency REST API (FastAPI) to serve the frontend application.
• Cloud Security: Secure the AWS EC2 environment using proper VPC routing, IAM roles, and Security Groups.

What You Must Have (Requirements):

• AI / MLOps: Proven experience downloading raw model weights (Hugging Face) and serving them locally on GPUs. Deep understanding of LLM memory requirements (KV cache, VRAM allocation).
• Cloud Infrastructure: Hands-on experience with AWS, specifically spinning up, configuring, and securing persistent Linux EC2 VMs.
• Data Engineering / Python: Strong Python skills. Experience with modern web scraping libraries (Playwright, BeautifulSoup, Scrapy) and handling messy HTML data.
• RAG Architecture: Strong understanding of embedding models, semantic chunking, and vector databases.
• DevOps: Highly proficient in Docker, specifically containerizing GPU-accelerated applications (NVIDIA Container Toolkit). Bonus Points If You Have:
• Experience actively migrating off managed APIs to self-hosted Small Language Models (SLMs).
• Advanced RAG experience (Cross-encoder re-ranking, Hybrid Search).
• Experience with Caddy, Nginx, or similar tools for reverse-proxying secure API endpoints and managing SSL certificates.