AI Engineer – Agentic AI & Cloud Infrastructure

We are seeking a hands-on AI Engineer to design, develop, and deploy AI agents and intelligent automation solutions for enterprise applications who can contribute across the full development lifecycle, from AI agent architecture and backend development to infrastructure deployment, authentication, testing, and production maintenance.

The role involves working with large language models (LLMs), including both commercial and open-source models, with opportunities to fine-tune, customize, evaluate, and optimize models for specific business use cases.

The ideal candidate must have strong software engineering skills, experience working with Microsoft Azure infrastructure, and the ability to develop AI agents that integrate securely with enterprise systems such as Microsoft Teams, SharePoint, Outlook, and other Microsoft 365 services.

Key Responsibilities

• AI Agent Development
• Design, develop, and deploy AI agents capable of performing multi-step tasks and automating business workflows
• Build agents using Microsoft Foundry (Azure AI Foundry), Azure AI Agent Service, and other agent frameworks such as LangGraph, Semantic Kernel, or similar technologies
• Develop agent capabilities including tool calling, function execution, retrieval-augmented generation (RAG), memory management, and workflow orchestration
• Integrate agents with internal applications, external APIs, databases, and enterprise systems. Implement multi-agent workflows, human-in-the-loop approvals, and appropriate security guardrails
• Test, evaluate, and optimize agent accuracy, reliability, and performance
• Microsoft Azure & Enterprise Integrations
• Build and deploy AI agents that operate within Microsoft Teams and other Microsoft 365 applications
• Integrate AI agents with Microsoft Graph API to access authorized resources such as SharePoint, Outlook, OneDrive, Teams, and calendars
• Implement secure authentication and authorization using Microsoft Entra ID (Azure AD), OAuth 2.0, and single sign-on (SSO)
• Work with delegated permissions, application permissions, app registrations, and On-Behalf-Of (OBO) authentication flows
• Develop backend APIs and services to support agent interactions with enterprise applications. Ensure secure data access and appropriate user-level permissions across integrations
• Open-Source LLMs & Model Fine-Tuning
• Work with commercial and open-source/open-weight LLMs, including models such as Llama, Qwen, and Mistral
• Fine-tune and customize LLMs for domain-specific applications, enterprise workflows, and specialized tasks
• Apply parameter-efficient fine-tuning techniques such as LoRA and QLoRA
• Prepare training datasets, perform data preprocessing, and develop model evaluation pipelines
• Experiment with inference optimization, quantization, and model serving frameworks
• Evaluate and compare open-source and commercial LLMs based on accuracy, latency, scalability, and cost
• Work with frameworks and libraries such as Hugging Face Transformers, PEFT, PyTorch, and related tools
• Support the deployment and integration of fine-tuned models into AI applications and agent workflows
• Azure Infrastructure & DevOps
• Provision, configure, and manage cloud infrastructure for AI applications within Microsoft Azure. Work with Azure services including:
• Microsoft Foundry / Azure AI Foundry
• Azure OpenAI Service
• Azure App Service, Azure Functions, and Azure Container Apps
• Azure AI Search
• Azure Storage and database services
• Azure Key Vault
• Azure Monitor and Application Insights
• Microsoft Entra ID
• Build and maintain CI/CD pipelines using Azure DevOps
• Manage source control, deployments, and environment configurations using Git and Azure DevOps
• Support infrastructure as code using Terraform or Bicep
• Implement monitoring, logging, error handling, and troubleshooting for production AI systems
• Optimize cloud infrastructure for performance, reliability, security, and cost

Requirements

What We’re Looking For

We are looking for a practical, technically capable engineer who enjoys building working AI systems and solving complex engineering problems who is not only knowledgeable in AI but also a hands-on practitioner who can confidently discuss AI solutions with clients, translate technical concepts into business value, and deliver training or knowledge-transfer sessions when needed.

The ideal candidate should be comfortable working across AI development, backend engineering, cloud infrastructure, and enterprise integrations. They should be able to take an AI agent from prototype to production, understand how to connect it securely to enterprise applications, and have the technical curiosity to experiment with open-source models and emerging AI frameworks.

This is a hands-on engineering role, not a prompt engineering or no-code automation position. We are particularly interested in candidates who can demonstrate working AI agents, real Azure deployments, enterprise integrations, or fine-tuned LLM projects they have personally developed.

Required Qualifications

• 2–5 years of experience in software engineering, AI engineering, machine learning engineering, or related technical roles
• Strong programming proficiency in Python, with experience developing backend services and APIs
• Practical experience developing LLM-powered applications, AI agents, or intelligent automation systems
• Experience with agent frameworks such as Microsoft Foundry Agent Service, LangGraph, Semantic Kernel, or equivalent tools
• Working knowledge of RAG, vector databases, embeddings, tool calling, and LLM orchestration
• Hands-on experience with Microsoft Azure, including deploying and managing cloud applications
• Experience using Azure DevOps, Git, and CI/CD pipelines
• Understanding of REST APIs, authentication, authorization, and enterprise system integrations
• Familiarity with Microsoft Entra ID, OAuth 2.0, and Microsoft Graph API
• Experience working with open-source LLMs and familiarity with model fine-tuning techniques
• Understanding of model evaluation, inference, and performance optimization

Preferred Qualifications

• Experience with AWS cloud services and architecture
• Experience building and deploying AI agents within Microsoft Teams
• Experience implementing OAuth On-Behalf-Of flows and delegated Microsoft Graph permissions
• Experience with Microsoft 365 Agents SDK, Teams SDK, or Azure Bot Service
• Hands-on experience fine-tuning open-source LLMs using LoRA, QLoRA, or Hugging Face PEFT
• Experience deploying open-source models using vLLM, Ollama, Hugging Face, or similar serving frameworks
• Familiarity with GPU infrastructure and model inference optimization
• Experience with Docker, containerized applications, Terraform, or Bicep
• Familiarity with Model Context Protocol (MCP), multi-agent systems, and agent orchestration
• Experience integrating AI applications with SharePoint, Dynamics 365, SQL databases, or other enterprise systems
• Familiarity with enterprise AI security, observability, and MLOps practices

Benefits

• Free lunch meal, fruits, snacks, and drinks (if you are in an onsite shift)
• Onsite gym with a free professional instructor
• Weekly fitness activity and an annual fitness challenge where you can win up to Php 70,000
• Weekly engagement activities with prizes that are up to Php 3,000
• Free upskilling academy to improve your performance and skillset
• State-of-the-art facilities from toilets to your workstation
• Amenities such as sleeping quarters, game area, chat room, shower room