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 ExperienceFinancial 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 SkillsetAI 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 ResponsibilitiesDevelop 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 & ExperienceMinimum 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.