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About the Role
As an AI Engineer focused on optimization, you will build and run AI tools and platforms used across the front office, ensuring the firm maximizes value from its AI investments. You will design and deliver central AI services, assistants, and agents for investment professionals, while improving the efficiency of AI usage across providers and teams. A key objective is migrating suitable workloads to open-weight models served on internal GPUs, fine-tuned as needed, at a significantly lower cost than external APIs.
Responsibilities
- Build central platform capabilities including model access, an AI gateway, MCP services, and knowledge retrieval with built-in entitlements, audit, and monitoring.
- Design, build, and run AI assistants, agents, and services for portfolio managers, traders, quants, and researchers.
- Set up and manage production inference for open-weight models on internal GPUs using engines like vLLM or SGLang behind OpenAI-compatible APIs.
- Fine-tune models for specific front office tasks using LoRA and QLoRA, and efficiently serve multiple adapters on shared base models.
- Apply quantization techniques (e.g., FP8, AWQ, GPTQ) and serving optimizations such as prefix caching, continuous batching, speculative decoding, and tensor parallelism to maximize GPU performance.
- Benchmark latency and throughput (time to first token, inter-token latency, tokens per second) using tools like GuideLLM, and plan GPU capacity based on service levels.
- Manage inference on Kubernetes with GPU scheduling, autoscaling, and monitoring of GPU utilization and health.
- Route traffic across hosted and self-hosted models via a central gateway like LiteLLM, directing each workload to the most cost-effective model that meets quality standards.
- Implement cost-saving measures including model right-sizing, prompt caching, batching, and context efficiency.
- Compare the cost and quality of self-hosted and API models for each task, and support provider and hardware decisions with data.
- Identify AI applications within the business with significant usage or value, and migrate them to central platforms.
- Transform recurring requirements into shared services, MCP servers, skills, and agent templates to reduce duplication.
- Address other team priorities as they arise, including new front office tools, knowledge services, MCP integrations, and data access.
- Contribute to shared engineering standards, code reviews, and production support.
- Provide usage, performance, and cost metrics from models and services to the AI spend reporting lead to inform decisions on scaling, consolidating, or retiring AI tools.
Requirements
- Experience building and running AI tools and platforms.
- Experience designing and delivering central AI services, assistants, and agents.
- Experience with migrating workloads to open-weight models served on internal GPUs.
- Experience fine-tuning models.
- Experience with inference engines such as vLLM or SGLang.
- Experience with OpenAI-compatible APIs.
- Experience with LoRA and QLoRA.
- Experience with quantization techniques (e.g., FP8, AWQ, GPTQ).
- Experience with serving optimizations (e.g., prefix caching, continuous batching, speculative decoding, tensor parallelism).
- Experience benchmarking latency and throughput.
- Experience with tools like GuideLLM.
- Experience running inference on Kubernetes with GPU scheduling, autoscaling, and monitoring.
- Experience routing traffic across models.
- Experience with cost optimization techniques for AI models.
- Experience comparing cost and quality of different AI models.
- Experience identifying and migrating AI applications to central platforms.
- Experience turning requirements into shared services.
- Experience contributing to engineering standards, code review, and production support.
- Experience providing usage, performance, and cost metrics.
Skills
- AI
- Optimization
- Model Access
- AI Gateway
- MCP Services
- Knowledge Retrieval
- Entitlements
- Audit
- Monitoring
- AI Assistants
- AI Agents
- Inference
- Open-weight Models
- GPUs
- vLLM
- SGLang
- OpenAI-compatible APIs
- LoRA
- QLoRA
- Quantization
- FP8
- AWQ
- GPTQ
- Prefix Caching
- Continuous Batching
- Speculative Decoding
- Tensor Parallelism
- Latency Benchmarking
- Throughput Benchmarking
- GuideLLM
- Kubernetes
- GPU Scheduling
- Autoscaling
- GPU Utilization Monitoring
- GPU Health Monitoring
- LiteLLM
- Cost Optimization
- Model Right-sizing
- Prompt Caching
- Batching
- Context Efficiency
- Shared Services
- Engineering Standards
- Code Review
- Production Support
- Data Analysis
About the Company
- The AI Team, part of Front Office Technology, builds AI tools and platforms used across the front office, including assistants and agents for investment teams, secure access to firm data and knowledge, and controls for safe AI usage in a regulated trading environment.
- We build central capabilities for the entire firm and support business teams in developing their own AI solutions, eventually taking ownership of successful initiatives as they grow in importance.
- BlueCrest is committed to providing an inclusive environment for its workforce.
Equal Opportunity
- 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.