Principal Forward Deployed Engineer - AI Platform

About the role

Red Hat is hiring a Principal Forward Deployed Engineer (FDE) for its APAC AI Center of Excellence in Singapore. This is an engineering-led adoption role for a recognized technical expert who works at the frontier of Enterprise and Sovereign AI, writing production-grade code alongside strategic customers, defining technical strategies and architecting reliable, secure systems. About 70% of the work must be upstreamed into global open-source communities.

Key responsibilities

• Work with customers and partners to overcome integration blockers such as SSO, legacy data pipelines and air-gapped networks, and prototype RAG pipelines and AI agent architectures
• Turn POCs and MVPs into secure, scalable production systems and establish testing practices for large-scale AI systems
• Bridge APAC needs with global engineering teams, helping turn field innovations into core products and align on partner blueprints
• Contribute code upstream to open-source projects in cloud-native AI, model serving and MLOps, and standardize regional solutions to avoid technical debt
• Mentor junior and principal engineers, and own technical initiatives that deliver subscription and operational value
• Follow one specialization track: hardware heterogeneity and sovereign AI, or AI platform primitives and distributed systems

Required profile

• Minimum 8+ years in system engineering, distributed computing, platform engineering or AI/ML software development, with a history of technical strategy leadership
• Active upstream contributor or maintainer in open-source communities such as deep learning engines, schedulers or container runtimes
• Experience mentoring senior staff and driving SDLC, CI/CD and testing practices
• Ability to handle ambiguity in a large enterprise and to communicate with enterprise architects and C-suite technologists while respecting data privacy and compliance

Required skills

• C/C++, Go and Python, with production-ready, well-tested code
• Deep learning frameworks, model fine-tuning (LoRA, QLoRA, SFT), model serving and LLM orchestration (LangChain, LlamaIndex)
• Kubernetes and custom operators, CRDs, and container orchestration
• CUDA, ROCm and GPU/NPU driver optimization