Specialised AI Engineer (AI)

TL;DR

Specialised AI Engineer (AI): Designing and optimizing distributed systems for a vertically integrated GenAI cloud platform with an accent on inference optimization, large-scale training, and post-training alignment. Focus on solving hard systems challenges, ensuring performance and scalability, and building developer-facing APIs and tooling for high-throughput AI services.

Location: Must be based in London, UK

Company

hirify.global is building a vertically integrated GenAI cloud platform, owning the data centers, software, and applications that power modern AI stacks.

What you will do

• Design and optimize scalable AI platform systems for training, fine-tuning, and inference.
• Drive inference performance and efficiency using techniques like continuous batching, quantization, and model compression.
• Develop post-training services including fine-tuning, RLHF, and agentic RL workflows.
• Create evaluation and benchmarking systems to measure model quality and system performance.
• Develop distributed systems for GPU-accelerated workloads to ensure reliability and efficiency.
• Build developer-facing APIs, SDKs, and tooling to enable effective platform consumption.

Requirements

• 5+ years of experience building production systems in machine learning or high-performance infrastructure.
• 4+ years of hands-on experience in at least one core area: inference optimization, large-scale training, or post-training.
• Proven ability to design and operate systems at scale with an understanding of performance trade-offs.
• Deep understanding of transformer architectures and LLMs in production environments.
• Strong proficiency in Python and PyTorch.
• Experience with distributed compute paradigms and hardware/software boundary optimization like CUDA or memory management.

Nice to have

• Experience working in containerized, distributed environments like Kubernetes.
• Contributions to widely used open-source AI frameworks.
• Hands-on experience with advanced inference optimization techniques such as MoE or speculative decoding.
• Experience developing APIs using OpenAPI 3.0+ specifications.

Culture & Benefits

• Focus on relentless innovation, ownership, and accountability.
• Culture of transparency and open collaboration across research and engineering teams.
• Work on critical infrastructure problems at the boundary of hardware and AI models.
• Opportunity to influence the development of industry-standard AI platform services.