FMX
Senior AI Research Engineer
London
Posted about 15 hours ago
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Senior AI Research Engineer— Agentic AI Applications
FMX is seeking an experienced AI Engineer to help design, build, and scale AI-powered applications for our rates and derivatives exchange business. This role is ideal for an engineer with strong software development experience, hands-on expertise with large language models, and practical experience building agentic AI systems that can reason, use tools, retrieve information, orchestrate workflows, and operate reliably in production environments.
The ideal candidate will bring a strong engineering foundation, a deep understanding of LLM behaviour and failure modes, and experience developing AI applications that meet high standards for reliability, security, observability, and responsible use. You will work closely with product, engineering, data, and business stakeholders to deliver AI solutions that support FMX’s exchange technology, market operations, analytics, and business workflows.
Responsibilities
- Design, build, evaluate, and maintain production-grade AI applications for FMX's rates and derivatives exchange business.
- Develop the Skynapse orchestration layer, including task decomposition, agent routing, tool coordination, context management, response synthesis, and execution monitoring.
- Apply model-level LLM knowledge to improve reasoning quality, retrieval quality, agent reliability, latency, cost, safety, and user experience.
- Evaluate when to use commercial LLM APIs, open-weight models, fine-tuned models, embedding models, rerankers, smaller specialized models, or deterministic software.
- Build agentic workflows using retrieval-augmented generation, semantic search, structured outputs, function/tool calling, planning, workflow orchestration, and human review.
- Integrate agents with FMX enterprise data sources, internal APIs, market data systems, reference data, documents, search indexes, and code repositories.
- Design guardrails for permissions, audit logging, approval workflows, escalation paths, fallback behaviour, tool-use limits, source attribution, and production kill switches.
- Create benchmark datasets, regression tests, red-team scenarios, human review workflows, and production monitoring for LLM and agentic systems.
- Mentor FMX engineers in LLM architecture, model behaviour, agent design, evaluation, retrieval, tool integration, and production AI engineering.
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Qualifications
- Bachelor's degree in computer science, machine learning, AI, mathematics, engineering, statistics, computational linguistics, or a related technical field.
- 5+ years of professional software engineering experience, including production system design, deployment, and support.
- 3+ years of hands-on experience with LLMs, deep learning, NLP, or advanced AI systems.
- Strong academic or research background in machine learning, deep learning, NLP, transformers, LLMs, generative AI, model evaluation, or related areas.
- Demonstrated understanding of transformers, attention mechanisms, tokenization, embeddings, pretraining, instruction tuning, fine-tuning, alignment, inference, context windows, decoding strategies, and evaluation.
- Experience building LLM applications beyond basic prompting, including RAG, structured outputs, function/tool calling, agent orchestration, evaluation, and production monitoring.
- Strong Python skills and experience writing clean, tested, maintainable, production-quality code.
- Experience evaluating, adapting, fine-tuning, or deploying open-source or open-weight language models.
- Practical understanding of LLM and agent failure modes, including hallucination, prompt injection, retrieval errors, tool misuse, reasoning errors, data leakage, unsafe automation, and non-deterministic behaviour.
- Strong understanding of enterprise security, privacy, access control, entitlementing, auditability, and responsible AI considerations.
- Ability to communicate complex AI concepts clearly and drive projects from concept through production deployment.
Preferred Qualifications
- Master's degree or PhD in computer science, machine learning, AI, NLP, statistics, mathematics, engineering, or a related field.
- Research experience or publications in LLMs, transformers, NLP, deep learning, retrieval, alignment, inference optimization, model evaluation, or agentic AI systems.
- Hands-on experience with supervised fine-tuning, instruction tuning, LoRA, QLoRA, parameter-efficient fine-tuning, preference optimization, distillation, quantization, or domain adaptation.
- Experience with model serving and inference optimization tools such as vLLM, TensorRT-LLM, Hugging Face TGI, ONNX, batching, caching, GPU utilization, and latency/cost optimization.
- Experience selecting and evaluating open-weight models for enterprise use, including trade-offs across model size, latency, quality, context length, licensing, hosting, security, cost, and governance.
- Experience applying AI in financial markets, exchanges, trading platforms, rates, derivatives, market data, risk, surveillance, clearing, or client onboarding.
- Hands-on experience with LangGraph, LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or similar orchestration tools.
- Experience with vector databases, hybrid search, semantic retrieval, reranking, OpenSearch, Elasticsearch, pgvector, FAISS, Pinecone, Weaviate, or Milvus.
- Experience with kdb+/q, time-series databases, order book data, trade data, mark-outs, liquidity analytics, or trading behavior analysis.
- Experience with AI observability, LLMOps, model monitoring, prompt/version management, evaluation dashboards, governance, and enterprise controls.
- Experience with C++ or other systems programming languages is a plus, especially in exchange, trading, market data, or high-performance systems environments.
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Key Attributes
- Deep curiosity about how LLMs work, why they fail, and how behaviour can be measured and improved.
- Strong ownership mindset and ability to deliver production-ready AI systems in a mission-critical exchange environment.
- Ability to think at both the model level and system level: neural networks, embeddings, inference, agents, tools, workflows, controls, observability, and governance.
- Research discipline: careful experimentation, benchmark design, ablation analysis, failure analysis, and reproducible evaluation.
- Pragmatic judgment about when AI should act autonomously, when human review is required, when model adaptation is useful, and when deterministic software is better.
- Strong communication skills across engineering, product, operations, sales, client onboarding, and business stakeholders.
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Skills
Large Language Models
Agentic AI
Python
Retrieval-Augmented Generation
Software Engineering
Fine-tuning
Model Evaluation
Vector Databases
Prompt Engineering
NLP
Deep Learning
System Design
LLMOps
C++
Financial Markets
Orchestration Frameworks
Location