Senior AI Automation Engineer

Bold Business; Manila; On-site; PHP 6,071,645 - 9,107,467; Full time

Job summary

Bold Business is hiring a Senior AI Automation Engineer to lead the design and implementation of cloud-native AI platforms. You'll transform complex workflows into measurable outcomes by architecting AI solutions. Responsibilities include designing complex workflows, overseeing RAG pipelines, and developing orchestration services. The ideal candidate has over 10 years of engineering experience, strong expertise in Python, deep learning frameworks, and SaaS platforms. This remote position offers a dynamic environment for innovation in AI.

Qualifications

• 10+ years of software engineering experience with a focus on applied machine learning and LLM-based systems.
• Experience scaling SaaS platforms and improving operational efficiency through automation and intelligent content creation.

Responsibilities

• Lead the design of complex agentic workflows and state machines using LangChain and LangGraph.
• Architect and oversee production-ready RAG pipelines and data ingestion.
• Develop Python-based orchestration services and Node.js APIs for AI capabilities.
• Establish evaluation and monitoring pipelines for LLM quality and performance.
• Own CI/CD and observability for AI services across AWS and GCP.
• Work with leadership and design teams on technical AI roadmaps.

Skills

Python

Deep learning frameworks (PyTorch, TensorFlow)

LangChain

LangGraph

PostgreSQL

NoSQL (MongoDB, Elasticsearch)

AWS services (Lambda, EKS, EventBridge)

GCP services (Vertex AI, Cloud Run)

Docker

Kubernetes

Terraform

TypeScript/Node.js

React/Next.js

Education

Bachelor of Science in Computer Science

Job description

Senior AI Automation Engineer - Remote, Global

Bold Business is seeking a visionary Senior AI Automation Engineer to lead the design and implementation of production‑grade, cloud‑native AI platforms. You will transform complex business workflows into measurable outcomes by architecting end‑to‑end AI solutions that leverage the latest in LLM orchestration, vector search, and agentic reasoning.

Key Responsibilities

• AI Orchestration & Agentic Design: lead the design of complex agentic workflows and state machines using frameworks like LangChain and LangGraph to automate multi‑step business tasks.
• Production RAG Architectures: architect and oversee the deployment of production‑ready Retrieval‑Augmented Generation (RAG) pipelines, including unstructured data ingestion and high‑performance vector store management (Pinecone, Weaviate, pgvector).
• Full‑Stack AI Integration: bridge the gap between AI research and product by developing Python‑based orchestration services and Node.js APIs that expose AI capabilities through secure, rate‑limited endpoints.
• Performance & Evaluation: establish rigorous evaluation and monitoring pipelines to track LLM quality, including latency, token usage, and hallucination rates, while iterating on retrieval strategies and guardrails.
• Infrastructure & MLOps: own the CI/CD and observability for AI services across AWS and GCP, ensuring secure SDLC practices and auditable deployment strategies.
• Strategic Collaboration: work directly with leadership, product, and design teams to translate high‑level business requirements into technical AI roadmaps and success metrics.

Technical Requirements

• Deep expertise in Python and deep learning frameworks (PyTorch, TensorFlow) with specialized experience in fine‑tuning and prompt engineering for Gemini, OpenAI, and Anthropic.
• Hands‑on mastery of LangChain, LangGraph, and LlamaIndex, specifically for tool‑augmented reasoning and function calling.
• Expert knowledge of PostgreSQL (multi‑tenant schemas), NoSQL (MongoDB, Elasticsearch), and specialized vector databases.
• Extensive experience with AWS (Lambda, EKS, EventBridge) and GCP (Vertex AI, Cloud Run), paired with Docker, Kubernetes, and Terraform.
• A strong foundation in TypeScript/Node.js and modern front‑end frameworks (React, Next.js) to build AI‑powered UX flows and assistants.

Education & Experience

• 10+ years of software engineering experience, with a significant recent focus on applied machine learning and LLM‑based systems.
• Bachelor of Science in Computer Science from a top‑tier institution.
• Experience scaling SaaS platforms and improving operational efficiency through automation and intelligent content creation.