Artificial Intelligence/LLM Engineer

The Development & Engineering section is responsible for the development and management of in-house applications and data platforms, designing and implementing new solutions, and supporting business technologists.

Required Technical Experience

• Financial Domain AI: Track record in building generative AI applications for financial services, including trading, compliance, investment operations, research, or internal productivity use cases.
• LLM Fine-Tuning and Adaptation: Hands-on experience adapting foundation models using LoRA, QLoRA, PEFT, supervised fine-tuning, instruction tuning, preference tuning, and knowledge distillation.
• Custom Model Deployment: Experience deploying fine-tuned or custom models into production or controlled enterprise environments using containerized inference, GPU-enabled infrastructure, model registries, and serving frameworks.
• Model Evaluation and Benchmarking: Ability to compare base, fine-tuned, distilled, and RAG-based approaches using task-specific evaluations, quality metrics, latency, throughput, and cost benchmarks.
• Open-Source Model Ecosystem: Practical experience with open-weight models such as Llama, Mistral/Mixtral, Qwen, Gemma, Phi, or similar models.
• Collaborative Multi-Agent Systems: Knowledge of building collaborative, autonomous agent systems that communicate effectively and coordinate complex tasks, ideally leveraging Semantic Kernel or LangGraph.
• Continuous Innovation: Interest and engagement in recent AI research and developments across OpenAI, Microsoft, Anthropic, open-source AI, and model optimization techniques.

Technical Skillset

• AI and LLM Integration
  • Python expertise, leveraging large language models (OpenAI, Anthropic, Mistral, GCP) within AI pipelines. Experience with LangChain, LangGraph, and Semantic Kernel for orchestrating generative workflows and agentic behaviors.
• Cloud Platforms
  • Strong knowledge of Microsoft Azure, including Azure OpenAI, Azure Machine Learning, and Azure Cognitive Search. Familiarity with AWS is beneficial but optional.
• Generative AI Pipelines
  • Ability to create multi-step, retrieval-augmented generation (RAG) pipelines using Azure Cognitive Search to integrate structured and unstructured company data effectively.
• Agentic System Design
  • Experience developing autonomous AI agents that handle context-aware interactions, leveraging recent advancements from OpenAI’s Agents SDK or Microsoft’s Semantic Kernel framework.
• Memory and Context Management
  • Skilled in designing conversational memory and long-term context solutions using Azure Cognitive Search, semantic retrieval, and extended-context techniques, such as Claude's large-context models.
• DevOps and Deployment
  • Familiarity with CI/CD practices, containerization (Docker), orchestration (Kubernetes), and observability tools for deployment in regulated environments.

Key Responsibilities

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

Education & Experience

• Minimum 5 years’ experience in enterprise-grade developer roles.
• Minimum 2 years’ relevant experience in AI-specific applications in an enterprise context.
• Bachelor’s or master’s degree in computer science or a related discipline, or equivalent combined education and experience.