AI Security Automation Engineer, AVP
HybridSingapore, Singapore
Job Summary
Build production-grade AI systems including agents, skills, memory patterns, and guardrails under the guidance of a Lead or Senior Engineer. Engineer retrieval and context-engineering approaches using embeddings, semantic search, and grounding, while establishing well-governed APIs that connect AI capabilities to security platforms. Develop cloud-native AI services in AWS, Azure, and GCP using containers, serverless patterns, and event-driven messaging. Establish evaluation, research, regression testing, and observability frameworks to continuously improve quality and agent behavior. Work with geographically distributed team members to deliver next-generation AI platforms and autonomous agents that strengthen cyber resilience.
Required Qualifications
- Master's or bachelor's degree in computer science, Software Engineering, Artificial Intelligence, Data Science, Cybersecurity, or a related discipline
- 5+ years of professional software engineering experience
- 5+ years of experience developing AI, Machine Learning, or Generative AI solutions
- Work shift is 8 AM – 5 PM local time with occasional Level 2-3 escalation resolution to support operational incidents
- This hybrid role includes an in-office presence requirement of 2–4 days per week, consistent with the organization's hybrid work policy
- Experience developing, and deploying production-grade Generative AI and Large Language Model (LLM) based solutions, including agentic workflows, intelligent agents, and enterprise tool integration frameworks
- Strong software engineering fundamentals with expertise in designing and delivering cloud-native applications and services leveraging containers, serverless architectures, and modern public cloud platforms
- Hands-on expertise developing Retrieval-Augmented Generation (RAG) solutions, including embeddings, semantic search, knowledge grounding, context engineering, prompt optimization, and prompt lifecycle management
- Hands-on experience utilizing enterprise-approved AI-assisted software development tools to accelerate application delivery, improve code quality, streamline testing, and enhance documentation, while ensuring outputs are validated through secure coding practices, peer review, and automated testing
- Ability to collaborate and work with geographically distributed team members through virtual collaboration, fostering strong partnerships, driving technical outcomes, and building effective relationships across engineering organizations
- Strong software development and automation skills with experience in Python, JavaScript/TypeScript, Rust, Go (Golang), Bash, and PowerShell
- LangGraph, Semantic Kernel, CrewAI, AutoGen, LangChain, or similar frameworks
- Spark, Kafka, Delta Lake, Iceberg, and Airflow
- Experience with vector databases, semantic search, and enterprise RAG platforms
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