Senior AI Engineer – Agentic Systems

Xurpas, Inc.; Taguig; On-site; PHP 1,800,000 - 3,000,000; Full time

Job summary

Xurpas, Inc. seeks a Senior AI Engineer with 5–7 years of experience to design, develop, and deploy autonomous AI agents and multi-agent systems. You will build intelligent workflows that reason, plan, and execute complex tasks, interacting with enterprise applications.

You will leverage LLMs, agent orchestration frameworks, and cloud AI platforms to automate business processes, improve efficiency, and communicate complex AI concepts across teams.

Qualifications

• Expert in agentic AI frameworks with Python.
• Experience with LLMs for reasoning and tool usage.
• Strong API integration and backend engineering.

Responsibilities

• Design and build production-grade Agentic AI solutions.
• Develop autonomous and multi-agent systems.
• Integrate AI agents with enterprise systems and APIs.
• Manage context, memory, and RAG.
• Ensure governance, security, and compliance.
• Monitor and optimize AI agents in production.
• Collaborate with stakeholders to identify use cases.
• Write technical docs and establish engineering standards.

Skills

Agentic AI

Python

LLM frameworks

API integration

Backend engineering

Communication

Tools

LangChain

CrewAI

AutoGen

LangGraph

Pinecone

Chroma

Job description

We are seeking a highly skilled Senior AI Engineer with 5 to 7 years of professional experience to design, develop, and deploy autonomous AI agents and multi-agent systems. In this role, you will build intelligent workflows that reason, plan, execute complex tasks, and seamlessly interact with enterprise applications. You will leverage Large Language Models (LLMs), cutting-edge agent orchestration frameworks, and cloud AI platforms to automate business processes and drive operational efficiency. Strong technical execution and excellent communication skills are required.

Responsibilities:

• Agentic System Architecture: Design and develop production-grade Agentic AI solutions utilizing advanced LLMs and agent frameworks.
• Autonomous & Multi-Agent Design: Build autonomous agents capable of reasoning, planning, tool execution, and multi-step workflows, as well as complex multi-agent orchestrated systems.
• Integration & Workflows: Integrate AI agents with enterprise systems, APIs, internal databases, and modern cloud services to power business automation.
• Context & Memory Management: Implement robust memory systems, dynamic context management, RAG (Retrieval-Augmented Generation), and knowledge-grounding capabilities.
• Governance & Security: Apply Responsible AI principles, rigorous governance, data security, and compliance controls throughout the entire solution lifecycle.
• Production Operations: Conduct ongoing testing, evaluation, monitoring, and performance optimization of AI agents in live production environments.
• Stakeholder Collaboration: Partner directly with business stakeholders to identify high-impact Agentic AI use cases and translate them into functional requirements.
• Documentation & Standards: Author detailed technical documentation, build reusable frameworks, and establish engineering best practices for the AI development team.

Qualifications:

• Core Technology: Strong technical expertise in Agentic AI frameworks (e.g., LangChain, CrewAI, AutoGen, or LangGraph) paired with advanced Python development skills.
• Communication: Exceptional verbal and written communication skills to articulate complex AI concepts to both technical teammates and business stakeholders.
• LLM Engineering: Proven experience working with foundational models (OpenAI, Anthropic, open-source models) for reasoning, function calling, and tool usage.
• Software Engineering: Solid foundation in backend engineering, API integration, and handling structured/unstructured data.

Good-to-Have Skills:

• Experience with vector databases (e.g., Pinecone, Chroma, Milvus) and optimization of RAG pipelines.
• Familiarity with enterprise cloud platforms (AWS, Azure, or GCP) and AI orchestration deployment.
• Knowledge of MLOps / LLMOps practices for monitoring drift, latency, and costs of agentic workflows.