AI Engineer

Career Connect; Manila; On-site; PHP 1,000,000 - 1,800,000; Full time

Job summary

Career Connect seeks an AI Engineer to design and deploy Large Language Model workflows for incident analysis, RCA, and automation in enterprise environments. You will configure vector databases, implement retrieval strategies, and build ML models to detect anomalies in logs and events.

You will collaborate with cross-functional teams, apply responsible AI practices, and integrate AI components into production platforms, leveraging HuggingFace, LangChain, and OpenAI libraries.

Qualifications

• Experience designing transformer-based workflows and dynamic prompts.
• Familiarity with tokenization, embeddings, and fine-tuning using popular libraries.

Responsibilities

• Design end-to-end AI workflows for RCA, incident summaries, and remediation.
• Develop and deploy LLM-driven automation for operational use cases.
• Build tokenization pipelines, embeddings, prompt engineering, and fine-tuning.
• Integrate AI components with enterprise systems (ELK, SOPs, ServiceNow).
• Ensure responsible AI practices, governance, and documentation standards.
• Collaborate with cross-functional teams to refine requirements and testing.

Skills

Python

LLM workflows

Prompt engineering

Responsible AI

Vector databases

Operational automation

Incident analysis

Tools

HuggingFace

LangChain

OpenAI APIs

Milvus

Pinecone

Azure AI Search

Job description

Job Description:

The AI Engineer requires hands-on expertise in developing and deploying Large Language Models,

Retrieval Augmented Generation pipelines, and Machine Learning models that support automation,

incident analysis, and operational efficiency. The AI Engineer must demonstrate the ability to design

end-to-end AI workflows, configure vector databases, implement chunking strategies for retrieval, and

build ML models that detect anomalies in logs and system events. The capability includes strong

proficiency in Python, model lifecycle management, prompt engineering, responsible AI practices, and

integrating AI components into production environments.

Requirements:

Build LLM modules for RCA, Incident Summaries, correlation logic, and remediation workflows

using operational data sources such as SOPs, ELK logs, and ServiceNow tickets.

Develop machine learning models to detect anomalies in system and infrastructure logs.

Integrate AI solutions with existing platforms to enhance monitoring, reporting, and

Ensure that AI models follow responsible AI practices, governance requirements, and

documentation standards.

Collaborate with cross functional teams to refine AI requirements, support testing, and improve

Relevant Work Experience:

Hands-on experience designing transformer based workflows, dynamic prompts, and

LLM driven automation for operational use cases such as incident analysis and RCA.

Experience building tokenization pipelines, embeddings, prompt engineering, and fine

tuning using frameworks such as Hugging Face, LangChain, and OpenAI libraries.

Experience deploying and configuring vector databases for semantic search, including

HNSW indexing, metadata filtering, and sharding using platforms such as Milvus,

Pinecone, or Azure AI Search.

Experience implementing Retrieval Augmented Generation pipelines with chunking

strategies such as semantic chunking, sliding window, or hierarchical chunking.

Experience integrating LLM workflows with enterprise systems such as ELK Stack, SOP

repositories, and ServiceNow tickets.

Experience with ML model lifecycle management including training, evaluation,