Artificial Intelligence Engineer

SYSGEN RPO; Philippines; On-site; PHP 1,200,000 - 1,900,000; Full time

Job summary

SYSGEN RPO is seeking an experienced AI/ML engineer to design, develop, test, and deploy AI-powered applications and services in the Philippines. You will build custom AI solutions addressing business requirements, develop RAG pipelines, and integrate AI capabilities into existing platforms.

You'll work with OpenAI/Anthropic SDKs, LangChain, PyTorch/TensorFlow, and ML infrastructure to deliver scalable, secure, production-ready solutions.

Responsibilities

• Design, develop, test, and deploy AI-powered applications and services.
• Build custom AI solutions that address specific business and operational requirements.
• Develop and optimize RAG pipelines, agentic AI workflows, semantic search systems, predictive models, and fine-tuned AI models.
• Integrate AI capabilities into existing applications, platforms, workflows, and business systems.
• Design AI solutions that are scalable, maintainable, secure, and production-ready.
• Evaluate and select appropriate models, frameworks, APIs, databases, and infrastructure based on project requirements.
• Develop LLM-powered applications using technologies such as OpenAI SDK, Anthropic SDK, LangChain, LangGraph, LlamaIndex, and Model Context Protocol (MCP).
• Design and implement prompting, tool calling, structured outputs, and context-management strategies.
• Develop agentic workflows capable of planning, reasoning, tool use, and task execution.
• Evaluate model performance and optimize solutions for accuracy, latency, reliability, and cost.
• Design and implement enterprise-grade Retrieval-Augmented Generation (RAG) systems.
• Build document ingestion, preprocessing, chunking, embedding, and retrieval pipelines.
• Work with vector databases such as Pinecone, Weaviate, pgvector, ChromaDB, or Qdrant.
• Implement semantic search, hybrid search, re-ranking, metadata filtering, and graph-augmented retrieval.
• Improve retrieval quality through evaluation, experimentation, and optimization.
• Develop and deploy machine learning models for applicable business use cases.
• Work with PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers.
• Perform data preprocessing, feature engineering, model evaluation, and optimization.
• Implement model fine-tuning techniques such as LoRA and QLoRA, where appropriate.
• Evaluate foundation models and determine whether existing models, fine-tuning, or custom model development is the most appropriate approach.
• Explore multimodal AI applications involving text, images, audio, structured data, and other data sources.
• Develop and maintain reliable pipelines for AI model development, testing, deployment, monitoring, and lifecycle management.
• Implement MLOps and CI/CD practices for AI applications.
• Work with Docker, Kubernetes, Git, CI/CD pipelines, model serving platforms, and monitoring/observability tools.
• Deploy and manage AI workloads using AWS, GCP, or Azure.
• Optimize AI infrastructure for scalability, performance, reliability, and cost.
• Implement monitoring for model performance, latency, failures, data quality, and potential data/model drift.
• Write clean, maintainable, well-tested, and production-quality code.
• Develop APIs and backend services supporting AI applications.
• Work with SQL and NoSQL databases and data pipelines.
• Apply software engineering best practices including version control, code reviews, automated testing, documentation, secure coding, CI/CD, and system monitoring.

Skills

AI systems

RAG pipelines

LangChain

OpenAI SDK

LangGraph

LlamaIndex

LLM integration

MLOps

PyTorch

TensorFlow

Hugging Face Transformers

Prompt engineering

Semantic search

Model fine-tuning

LoRA / QLoRA

Multimodal AI

Tools

Docker

Kubernetes

Git

CI/CD

Pinecone

Weaviate

pgvector

ChromaDB

Qdrant

Job description

• Design, develop, test, and deploy AI-powered applications and services.


• Build custom AI solutions that address specific business and operational requirements.


• Develop and optimize RAG pipelines, agentic AI workflows, semantic search systems, predictive models, and fine-tuned AI models.


• Integrate AI capabilities into existing applications, platforms, workflows, and business systems.


• Design AI solutions that are scalable, maintainable, secure, and production-ready.


• Evaluate and select appropriate models, frameworks, APIs, databases, and infrastructure based on project requirements.


• Develop LLM-powered applications using technologies such as OpenAI SDK, Anthropic SDK, LangChain, LangGraph, LlamaIndex, and Model Context Protocol (MCP).


• Design and implement prompting, tool calling, structured outputs, and context-management strategies.


• Develop agentic workflows capable of planning, reasoning, tool use, and task execution.


• Evaluate model performance and optimize solutions for accuracy, latency, reliability, and cost.

3. RAG, Search & Knowledge Systems

• Design and implement enterprise-grade Retrieval-Augmented Generation (RAG) systems.


• Build document ingestion, preprocessing, chunking, embedding, and retrieval pipelines.


• Work with vector databases such as Pinecone, Weaviate, pgvector, ChromaDB, or Qdrant.


• Implement semantic search, hybrid search, re-ranking, metadata filtering, and graph-augmented retrieval.


• Improve retrieval quality through evaluation, experimentation, and optimization.


• Develop and deploy machine learning models for applicable business use cases.


• Work with PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers.


• Perform data preprocessing, feature engineering, model evaluation, and optimization.


• Implement model fine-tuning techniques such as LoRA and QLoRA, where appropriate.


• Evaluate foundation models and determine whether existing models, fine-tuning, or custom model development is the most appropriate approach.


• Explore multimodal AI applications involving text, images, audio, structured data, and other data sources.

5. AI Infrastructure & MLOps

• Develop and maintain reliable pipelines for AI model development, testing, deployment, monitoring, and lifecycle management.


• Implement MLOps and CI/CD practices for AI applications.


• Work with Docker, Kubernetes, Git, CI/CD pipelines, model serving platforms, and monitoring/observability tools.


• Deploy and manage AI workloads using AWS, GCP, or Azure.


• Optimize AI infrastructure for scalability, performance, reliability, and cost.


• Implement monitoring for model performance, latency, failures, data quality, and potential data/model drift.

6. Software Engineering

• Write clean, maintainable, well-tested, and production-quality code.


• Develop APIs and backend services supporting AI applications.


• Work with SQL and NoSQL databases and data pipelines.


• Apply software engineering best practices including version control, code reviews, automated testing, documentation, secure coding, CI/CD, and system monitoring.