Software Engineer - AI Focused

RCBC; Taguig; On-site; PHP 1,200,000 - 2,000,000; Full time

Job summary

RCBC is seeking an experienced engineer to design, develop, and maintain LLM-powered applications that connect to banking core systems. You will work across requirements gathering, model development, integration, testing, and deployment to deliver robust solutions.

You will lead front-end interfaces, ensure reliability and low latency, and implement LLM evaluation frameworks with safeguards against hallucinations.

Qualifications

• Degree in Mathematics, Statistics, Computer Science, MIS, or related field.
• 4-5 years of total Software Engineering experience, with at least 2 years in LLM-based apps.
• Strong Python programming and SQL data analytics.
• Familiarity with vector databases Milvus and Pinecone and advanced RAG strategies.
• Experience setting up LLM Ops/ML Ops pipelines (CI/CD, evaluation, monitoring).
• Cloud technologies deployment patterns (AWS and/or Google) preferred.
• Certificates or training in the field (e.g., Google AI, Kaggle) desirable.

Responsibilities

• Lead architecture and development of LLM-integrated apps with banking core systems.
• Develop front-end interfaces exposing Agentic applications to workforce.
• Ensure reliability, usability and low latency of deployed apps.
• Engineer LLM evaluation frameworks and safeguards against hallucinations.
• Define engineering best practices and cascade patterns to data engineers.
• Develop and optimize full-stack interfaces (Front-end and API) for workforce.
• Research emerging Agentic tech and prototype within bank legacy infra.

Skills

LLM development

Python

SQL

RAG retrieval

CI/CD

ML Ops

Cloud deployment

Education

Bachelor's degree in CS/Math/Stats/MIS

Google AI certificate

Kaggle certificate

Tools

Milvus

Pinecone

AWS

Google Cloud

CI/CD tooling

Job description

Job Summary

• Responsible for designing, developing, and maintaining LLM-powered applications including application integration to key banking systems.
• The engineer is expected to be knowledgeable of current software packages related to the development of LLM-powered applications and is up-to-date with up and coming advances in the field. The engineer is expected to be well-verse with software development life cycle including requirements gathering, model development and integration, testing, and deployment.

How will you contribute?

• Lead the solution architecture and development of simple to complex applications that integrate Large Language Models (LLM) with banking core systems via advanced patterns (e.g., Agentic Orchestration, Multi-Agent Systems, RAG, and MCP integration, Prompt Engineering, etc. )
• Develop front-end web-based user interfaces that will expose Agentic applications to the workforce.
• Ensure reliability, usability, and responsiveness of the deployed application in the productionenvironment.
• Engineer robust LLM Evaluation frameworks and maintain accuracy standards and implement controls that safeguards the application from LLM-based hallucinations.
• Define engineering best practices (CI/CD, Unit Testing for AI, Code Reviews) and cascade design patterns and techniques to data engineers to foster an environment of knowledge
• sharing and co-development
• Develop and optimize full-stack interfaces (Front-end and API layers) that expose Agentic applications to the workforce, ensuring high availability and low latency.
• Conduct deep-dive research into emerging Agentic technologies and prototype their application within the bank's legacy infrastructure

What will make you successful?

• Degree in Mathematics, Statistics, Computer Science, Management Information Systems, or related field
• Experience building LLM-powered applications (production or strong POC).
• 4-5 years of total Software Engineering experience, with at least 2 years experience in developing, implementing, and deploying LLM-based applications
• Strong programming skills in Python. Proficiency in SQL for data transformation and analytics.
• Deep understanding of Vector Database architecture (e.g., Milvus, Pinecone) and advanced RAG retrieval strategies.
• Experience setting up LLM Ops / ML Ops pipelines (CI/CD, Evaluation, Monitoring)
• Familiarity with cloud technologies and deployment patterns (AWS and/or Google is preferred).
• Certificates and training in the field is highly desirable (e.g. Google AI certificate, Kaggle, OpenAI etc.)