AI Engineer: Build Scalable, Production-Ready AI Systems

SYSGEN RPO

Philippines

On-site

PHP 1,200,000 - 1,900,000

Full time

9 days ago

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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.