Senior AI Engineer for Cybersecurity & Multi-Agent Systems

Hammerjack Pty Ltd; Muntinlupa; On-site; PHP 1,800,000 - 2,400,000; Full time

Job summary

Boehringer Ingelheim is hiring a Senior Data Scientist / AI Engineer to design, build, and optimize multi-agent AI solutions for cybersecurity operations. You will work with security experts and tech teams to enhance triage, decision-making, and workflow automation using LLMs, RAG, and agent-based architectures.

The role emphasizes implementing AI-driven decision support, integrating diverse tools, and maintaining production-grade AI agents with a strong focus on precision and reliability.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, or a related field.
• Equivalent professional experience may be considered in lieu of formal education.
• Minimum of 5 years of experience in Data Science, AI Engineering, Machine Learning, or related technical roles.
• Demonstrated success in developing and refining agent-based systems and machine learning models for decision support in enterprise environments.

Responsibilities

• Design and maintain multi-agent architectures for triage and decision support.
• Develop and refine machine learning models to assess and score incidents.
• Integrate and orchestrate various tools and agents to automate and enrich the triage process.
• Collaborate with project teams to ensure effective implementation and alignment with project goals.
• Document system logic, workflows, and findings for stakeholders.

Skills

Python

Multi-agent systems

ML models

Data science

AI engineering

Education

Bachelor's degree in Computer Science

Master's degree in AI/CS

Tools

Docker

OpenShift

MLflow

Job description

Boehringer Ingelheim is hiring a Senior Data Scientist / AI Engineer to design, build, and optimize multi-agent AI solutions for cybersecurity operations. You will work with security experts and tech teams to enhance triage, decision-making, and workflow automation using LLMs, RAG, and agent-based architectures.

The role emphasizes implementing AI-driven decision support, integrating diverse tools, and maintaining production-grade AI agents with a strong focus on precision and reliability.