TL;DR

Lead AI Infrastructure Engineer (AI Engineering): Design and maintain high-performance, scalable, and resilient GPU-based infrastructure for advanced AI inference systems including LLMs, VLMs, and SLMs across cloud and on-premises environments with an accent on throughput, latency, availability, and compliance. Focus on architecting scalable inference platforms, integrating orchestration and observability frameworks, automating deployments, and leading client engagements to align AI infrastructure with business objectives.

Location: Singapore only (Singapore Citizens and Permanent Residents)

Company

%hirify_global% is a leading technology consultancy with 30+ years of experience delivering impactful solutions by solving complex business problems through technology.

What you will do

Design and operate GPU-based infrastructure (e.g., NVIDIA GB200, H100) across cloud and self-hosted environments.
Architect scalable inference platforms supporting real-time and batch serving with high availability and fault tolerance.
Integrate inference workloads with orchestration frameworks such as Kubernetes, Slurm, and Ray, and observability stacks like Prometheus, Grafana, and OpenTelemetry.
Automate infrastructure provisioning and deployment using Terraform, Helm, and CI/CD pipelines.
Collaborate with ML engineers to co-design systems optimized for low-latency serving and advanced inference optimization techniques.
Lead client engagements by shaping technical roadmaps aligning AI infrastructure with business goals, ensuring compliance and scalability.
Champion DevOps and agile practices to accelerate delivery while maintaining reliability and resilience.
Mentor teams in best practices for AI infrastructure engineering, fostering technical excellence and innovation.

Requirements

Must have current right to work in Singapore (Singapore Citizens or Permanent Residents only)
Expertise in GPU-based AI infrastructure (H100, GB200, or similar) and scaling across clusters.
Strong knowledge of orchestration frameworks: Kubernetes, Ray, Slurm.
Experience with inference-serving frameworks such as vLLM, NVIDIA Triton, DeepSpeed.
Proficiency in infrastructure automation tools: Terraform, Helm, CI/CD pipelines.
Experience building resilient, high-throughput, low-latency AI inference systems with observability tools (Prometheus, Grafana, OpenTelemetry).
Familiarity with security, compliance, and governance in AI infrastructure (data sovereignty, air-gapped deployments, audit logging).
Solid understanding of DevOps, cloud-native architectures, Infrastructure as Code, and multi-cloud/hybrid deployments (AWS, GCP, Azure, sovereign/private cloud).
Background in MLOps or collaboration with ML teams on large-scale AI production systems.

Nice to have

Proven ability to partner with senior client stakeholders (CTO, CIO, COO) and translate technical strategy into business outcomes.
Skilled at leading multi-disciplinary teams and building trust across diverse technical and business functions.
Strong communication skills to explain complex AI infrastructure concepts to technical and non-technical audiences.
Comfortable navigating uncertainty, making pragmatic decisions, and adapting quickly to evolving technologies.
Passionate about creating scalable, sustainable, and high-impact AI solutions transforming industries.

Culture & Benefits

Supportive and inclusive community focused on continuous learning and career development.
Interactive tools and numerous development programs to empower career growth.
Culture of technical excellence, innovation, and collaboration.