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About the Role

This role is focused on the end-to-end lifecycle of production-grade AI, from training and fine-tuning specialized models to architecting high-performance inference pipelines. The ideal candidate views AI as a rigorous engineering discipline, building reliable, low-latency, and globally scalable solutions.

Responsibilities

• Lead the adaptation of Large Language Models (LLMs) for domain-specific tasks using techniques like LoRA, QLoRA, and PEFT to balance performance with resource efficiency.
• Architect and optimize inference pipelines to minimize TTFT (Time to First Token) and maximize throughput, implementing quantization, caching strategies, and efficient batching.
• Build and maintain real-time AI pipelines using WebSockets and SSE, ensuring seamless low-latency delivery for voice (ASR/TTS) and text applications.
• Deploy and orchestrate models within containerized microservice architectures (Docker/Kubernetes), ensuring robust monitoring, security, and scalability.
• Work closely with Business Analysts and internal stakeholders to bridge the gap between commercial requirements and technical implementation.

Requirements

• Writing high-quality, maintainable Python code.
• Ensuring solutions are built for reliability, low latency, and global scale.

Skills

• LoRA
• QLoRA
• PEFT
• WebSockets
• SSE
• ASR/TTS
• Docker
• Kubernetes

Location

• London

Work Type

• Full-time

Experience Level

• Mid-level

About the Company

• Join our Global Analytics team.