About the Role
Our client is an emerging trading firm with an exciting new opportunity for an AI Automation Engineer. At its core, this role will play a vital part in shaping the future development of comprehensive ML models and AI-integrated software to enhance efficiency across their entire trading operations. The ideal candidate will possess strong communication skills, enabling effective collaboration with industry experts, software engineers, and researchers to guide the firm's integration of advanced AI technologies—redefining the workday for traders and engineers alike.
Responsibilities
* Design, prototype, and optimise low‑latency batch and streaming inference pipelines that deliver continuously updated predictions derived from upstream training pipelines.
* Integrate and fine‑tune LLM‑based conversational systems (e.g., GPT) to support front‑office functions such as research query handling, data interrogation, and operational assistance.
* Develop AI‑driven tools to automate issue detection, classification, and triage, improving system reliability and reducing engineering overhead.
* Collaborate closely with quant researchers, software engineers, traders, and platform teams to deliver robust and scalable AI solutions aligned with trading and business requirements.
* Leverage accelerated computing (GPU/TPU) and optimised inference frameworks (e.g., TensorRT, ONNX Runtime, CuDNN) to process high‑frequency trading data streams and support predictive modelling at scale.
* Continuously monitor, test, and retrain machine learning models to maintain performance, mitigate drift, and ensure optimal behaviour in live trading environments.
* Develop and maintain high‑performance model deployment infrastructure, including feature stores, monitoring systems, and model versioning tools.
* Implement distributed training strategies to accelerate research workflows and support experimentation with larger or more complex model architectures.
* Document architectures, best practices, and operational playbooks, ensuring clarity and maintainability for long‑term production systems.
Core Technical Experience
* 5–10+ years of experience in software engineering or machine learning, with practical exposure to both training and deploying AI/ML systems.
* Strong programming capability in Python, with additional proficiency in C++, CUDA, C#, or Java highly valued.
* Hands‑on experience building and optimising real‑time, low‑latency ML or inference pipelines.
* Expertise with modern ML frameworks such as PyTorch, TensorFlow, or JAX.
* Deep understanding of LLMs, NLP, and conversational AI, including use of OpenAI, Azure OpenAI, or similar APIs.
* Strong competency in GPU programming and optimisation, including tools such as CuDNN, TensorRT, or similar acceleration libraries.
* Experience with distributed training and scaling ML workloads using frameworks like Horovod, NCCL, or DeepSpeed.
* Familiarity with vector databases, RAG pipelines, and modern retrieval frameworks.
* Solid experience with cloud platforms (AWS, Azure, or GCP) and containerisation/orchestration, e.g., Docker and Kubernetes.
* Demonstrated ability to lead or contribute to complex, cross‑functional technical projects.
* Experience contributing to open‑source projects in ML, data science, or distributed systems is a plus.
* Passion for AI innovation, continuous learning, and practical experimentation with emerging technologies.
* Strong communication skills with a proactive, solution‑oriented mindset.
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