Artificial Intelligence/LLM Engineer

Role context

Required technical experience

• Track record building generative AI applications for financial services, including trading, compliance, investment operations, research, or internal productivity.
• Hands-on foundation-model adaptation using methods such as LoRA, QLoRA, PEFT, supervised fine-tuning, instruction tuning, preference tuning, and knowledge distillation.
• Experience deploying fine-tuned or custom models to production or controlled enterprise environments with containerized inference, GPU infrastructure, model registries, and serving frameworks.
• Compare base, fine-tuned, distilled, and RAG approaches with task-specific quality, latency, throughput, and cost benchmarks.
• Practical experience with open-weight models such as Llama, Mistral/Mixtral, Qwen, Gemma, Phi, or similar.
• Knowledge of collaborative autonomous agents; Semantic Kernel or LangGraph experience is desirable.
• Python expertise integrating LLMs from OpenAI, Anthropic, Mistral, or GCP into AI pipelines; experience with LangChain, LangGraph, and Semantic Kernel for generative workflows and agents.
• Strong Microsoft Azure knowledge, including Azure OpenAI, Azure Machine Learning, and Azure Cognitive Search; AWS familiarity is beneficial but optional.
• Create multi-step RAG pipelines using Azure Cognitive Search to connect structured and unstructured company data.
• Develop autonomous agents for context-aware interactions using tools such as OpenAI Agents SDK or Microsoft Semantic Kernel.
• Design conversational memory and long-term context solutions using Azure Cognitive Search, semantic retrieval, and extended-context techniques.
• Familiarity with CI/CD, Docker, Kubernetes, and observability for deployment in regulated environments.

Responsibilities

• Develop existing and new modules within core applications supporting front-office investment teams.
• Deliver new functionality, data capabilities, and modules, plus enhancements and fixes to existing modules.
• Engage with IT and business stakeholders and take ownership of deliverables.
• Understand business problems and offer technical solutions.
• Improve coding standards, practices, and SDLC maturity.
• Partner with quants and traders to build, support, and maintain a quantitative trading and risk optimization platform.
• Improve productivity through automation where applicable.

Education and experience

• At least 5 years of experience in enterprise-grade developer roles.
• At least 2 years of relevant experience in enterprise AI-specific applications.
• Bachelor's or master's degree in computer science or a related discipline, or equivalent combined education and experience.