Senior AI Research Engineer
Cantor Fitzgerald / BGC Partners2 months ago
Responsibilities
- Design, build, evaluate, and maintain production-grade AI applications for rates and derivatives exchange workflows.
- Develop the Skynapse orchestration layer for task decomposition, agent routing, tool coordination, context management, response synthesis, and execution monitoring.
- Improve LLM reasoning, retrieval quality, agent reliability, latency, cost, safety, and user experience using model-level knowledge.
- Build agentic workflows using retrieval-augmented generation, semantic search, structured outputs, function and tool calling, planning, workflow orchestration, and human review.
- Integrate agents with enterprise data sources, internal APIs, market data systems, reference data, documents, search indexes, and code repositories.
- Design permissions, audit logging, approval workflows, escalation paths, fallback behavior, 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 engineers in LLM architecture, model behavior, agent design, evaluation, retrieval, tool integration, and production AI engineering.
Requirements
- Bachelor’s degree in computer science, machine learning, AI, mathematics, engineering, statistics, computational linguistics, or a related technical field.
- At least 5 years of professional software engineering experience, including production system design, deployment, and support.
- At least 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.
- Understanding of transformers, attention mechanisms, tokenization, embeddings, pretraining, instruction tuning, fine-tuning, alignment, inference, context windows, decoding strategies, and evaluation.
- Experience building production LLM applications involving RAG, structured outputs, function and tool calling, agent orchestration, evaluation, and monitoring.
- Strong Python skills and experience writing clean, tested, maintainable production code.
- Experience evaluating, adapting, fine-tuning, or deploying open-source or open-weight language models.
- Understanding of LLM and agent failure modes, enterprise security, privacy, access control, entitlementing, auditability, and responsible AI.
- Ability to communicate complex AI concepts clearly and drive projects from concept through production deployment.
- Preferred: a master’s degree or PhD and research experience or publications in LLMs, transformers, NLP, deep learning, retrieval, alignment, inference optimization, model evaluation, or agentic AI systems.
- Preferred: hands-on experience with supervised fine-tuning, instruction tuning, LoRA, QLoRA, parameter-efficient fine-tuning, preference optimization, distillation, quantization, or domain adaptation.
- Preferred: experience with model serving, inference optimization, open-weight model selection, financial-market applications, orchestration frameworks, vector databases, market-data systems, AI observability, LLMOps, model monitoring, governance, and enterprise controls.
- Experience with C++ or other systems programming languages is a plus, especially in exchange, trading, market-data, or high-performance environments.
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