AI Platform Engineer

Role Description

We are seeking an AI Platform Engineer to design, build, and operate scalable AI inference platforms for production ML workloads. The ideal candidate will have expertise in distributed systems, LLM serving, GPU optimization, autoscaling, and cloud-native infrastructure, with a strong focus on performance, reliability, and observability.

Key Responsibilities

• Design and maintain scalable AI model serving platforms.
• Optimize inference performance, GPU utilization, and request routing.
• Build autoscaling, deployment, and monitoring solutions.
• Implement caching, security, and high-availability strategies.
• Collaborate with ML teams to deploy and support production AI models.

Qualifications

• 6+ years of experience in distributed systems, infrastructure, or ML platform engineering.
• Strong proficiency in Python and Go, Rust, or C++.
• Experience with LLM inference frameworks (vLLM, TensorRT-LLM), Kubernetes, cloud platforms, and GPU optimization.

Requirements

• Bachelor's or Master's degree in Computer Science, Computer Engineering, Artificial Intelligence, or a related technical discipline.
• 10+ years of professional experience in distributed systems, infrastructure engineering, cloud platforms, or machine learning platform engineering.
• Strong programming skills in Python and at least one systems programming language such as Go, Rust, or C++.
• Extensive experience with Large Language Model (LLM) serving, model inference optimization, and production AI infrastructure.
• Hands-on experience with vLLM, TensorRT-LLM, Triton Inference Server, Ray Serve, or similar AI serving frameworks.
• Strong expertise in Kubernetes, container orchestration, Docker, and cloud-native application architectures.
• Experience optimizing GPU workloads using CUDA, NVIDIA GPU technologies, distributed inference, and high-performance AI infrastructure.
• Experience with cloud platforms including AWS, Microsoft Azure, or Google Cloud Platform (GCP).
• Strong understanding of distributed systems, networking, scalability, observability, and security best practices.
• Excellent analytical, communication, collaboration, and technical leadership skills.

Preferred Qualifications

• Experience designing and operating multi-region AI platforms and globally distributed inference services.
• Knowledge of model optimization techniques such as quantization, pruning, compression, speculative decoding, KV cache optimization, and mixed-precision inference.
• Experience with MLOps, GitOps, Infrastructure as Code (Terraform, Bicep, CloudFormation), and CI/CD automation.
• Familiarity with service mesh technologies such as Istio or Linkerd, API gateways, and event-driven architectures.
• Contributions to open-source AI infrastructure projects, technical publications, patents, or conference presentations.
• Experience implementing FinOps strategies, cloud cost optimization, and enterprise AI governance.
• Experience with multi-region AI deployments and AI infrastructure.
• Familiarity with model optimization techniques such as quantization or compression.
• Open-source contributions or experience supporting large-scale AI APIs.

Benefits

• Competitive salary range: $100,000 – $150,000 per annum.
• 100% Remote work opportunity.
• Full-time, Direct W2 position.