AI Engineer

BlueCrest Capital Management; Greater London; On-site; GBP 110,000 - 150,000; Full time

Job summary

BlueCrest Capital Management in London is seeking an AI Engineer focused on optimisation to build and run AI tools and platforms used across the front office, including assistants and agents for PMs, traders and researchers. You will help move workloads onto open-weight models served on our own GPUs, fine-tuned where needed, at a fraction of external API costs.

You will design and deliver central AI services, ensure governance and entitlements, and continuously improve efficiency across

Qualifications

• Experience building AI tools for front-office workflows.
• Proficiency with self-hosted models and GPU inference.
• Familiarity with model tuning (LoRA/QLoRA).

Responsibilities

• Build front office AI tools and central platform capabilities for model access, an AI gateway, MCP services and knowledge retrieval with entitlements, audit and monitoring.
• Design, build and run AI assistants, agents and services used by PMs, traders and researchers.
• Stand up and run production inference for open-weight models on our own GPUs using engines like vLLM behind OpenAI-compatible APIs.
• Fine-tune models for front office tasks (LoRA/QLoRA) and serve multiple adapters on shared base models.
• Apply quantisation and serving optimisations (FP8, AWQ, GPTQ) to achieve low latency and high throughput.
• Run inference on Kubernetes with GPU scheduling, autoscaling and health monitoring.

Skills

Python

GPU inference

Systems design

Cost optimization

Kubernetes

Tools

vLLM

LoRA

QLoRA

FP8

GuideLLM

LiteLLM

Kubernetes

Job description

The Team

The AI Team, part of Front Office Technology, builds the AI tools and platforms used across the front office: assistants and agents for investment teams, secure access to firm data and knowledge, and the controls that let AI be used safely in a regulated trading environment.

Role Purpose

The AI Team, part of Front Office Technology, builds the AI tools and platforms used across the front office: assistants and agents for investment teams, secure access to firm data and knowledge, and the controls that let AI be used safely in a regulated trading environment.

We build central capabilities for the whole firm and support teams across the business as they build their own, bringing the most successful into Technology ownership as they grow in importance.

The Role

As an AI Engineer with a focused on optimisation, you will build and run the AI tools and platforms used across the front office, and make sure the firm gets the most value from its AI spend.

You will design and deliver central AI services, assistants and agents for PMs, traders and researchers, while making AI usage more efficient across providers and teams. A key area is moving suitable workloads onto open-weight models served on our own GPUs, fine-tuned where needed, at a fraction of the cost of external APIs.

The team is small and priorities shift as the firm’s use of AI grows. You will be expected to pick up other priorities across the team as they arise, so this role will not be dedicated solely to cost optimisation.

Why Join

• Build AI tools used in live investment workflows, and help the firm run its own models on its own GPUs.
• Join an AI function with a real mandate and the backing to shape how AI is built across the firm.
• Work with current frontier AI tooling in a team that values engineering discipline over hype.

Key Responsibilities

Building front office AI tools

• Build central platform capabilities such as model access, an AI gateway, MCP services and knowledge retrieval, with entitlements, audit and monitoring built in.
• Design, build and run AI assistants, agents and services used by PMs, traders, quants and researchers.

Self-hosted models

• Stand up and run production inference for open-weight models on our own GPUs, using engines such as vLLM or SGLang behind OpenAI-compatible APIs.
• Fine-tune models for specific front office tasks with LoRA and QLoRA, and serve multiple adapters efficiently on shared base models.
• Apply quantisation (e.g. FP8, AWQ, GPTQ) and serving optimisations such as prefix caching, continuous batching, speculative decoding and tensor parallelism to get the most from each GPU.
• Benchmark latency and throughput (time to first token, inter-token latency, tokens per second) with tools such as GuideLLM, and plan GPU capacity against service levels.
• Run inference on Kubernetes with GPU scheduling, autoscaling, and monitoring of GPU utilisation and health.

Cost optimisation

• Route traffic across hosted and self-hosted models through a central gateway such as LiteLLM, sending each workload to the cheapest model that meets its quality bar.
• Apply cost levers: model right-sizing, prompt caching, batching and context efficiency.
• Compare the cost and quality of self-hosted and API models per task, and support provider and hardware decisions with evidence.

Centralising and reuse

• Identify AI applications built in the business with real usage or value, and bring them onto central platforms.
• Turn repeated requirements into shared services, MCP servers, skills and agent templates, reducing duplication across teams and providers.

Wider AI engineering

• Pick up other priorities across the team as they arise, from new front office tools to knowledge services, MCP integrations and data access.
• Contribute to shared engineering standards, code review and production support.

Business outcomes

• Provide usage, performance and cost metrics from the models and services you run to the colleague responsible for AI spend reporting, so decisions to scale, consolidate or retire AI tools are based on data.

BlueCrest is committed to providing an inclusive environment for its workforce. As an employer, we provide equal opportunities to all people regardless of their gender, marital or civil partnership status, race, religion or ethnicity, disability, age, sexual orientation or nationality.