Lead AI Engineer

Project description

The project focuses on developing and productionizing advanced Generative AI solutions for investment and financial-services use cases, with a strong emphasis on LLM fine-tuning, model adaptation, evaluation, and deployment. The role will also contribute to RAG and agentic AI platforms, integrating enterprise data and cloud-based AI services to support front-office investment teams and improve automation, decision-making, and productivity.

Responsibilities

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

SKILLS

Must have

• Minimum of 8 years' experience in enterprise-grade developer roles.
• Minimum of 3 years' relevant experience in AI-specific applications in an enterprise context, including some experience guiding, reviewing or coordinating the work of other engineers.
• Bachelor's or master's degree in computer science or related discipline, or equivalent combined education and experience.
• Technical experience in financial domain AI: Track record in building and leading the delivery of generative AI applications for financial services (trading, compliance, customer support). Experience in fixed income — bonds, FX, credit — would be preferred.
• AI and LLM Integration: Python expertise, leveraging large language models (GPT-4, Claude) within AI pipelines. Experience with LangChain and LangGraph for orchestrating generative workflows and agentic
• Generative AI Pipelines: Ability to design and own multi-step, retrieval-augmented generation (RAG) pipelines using Azure Cognitive Search to integrate structured/unstructured company data effectively, and to set the patterns the wider team re-uses behaviours, and for guiding other engineers' use of these frameworks.
• Agentic System Design: Experience developing autonomous AI agents that handle context-aware interactions, leveraging recent advancements from OpenAI's Agents SDK.
• Fine-Tuning and Customization: Practical experience fine-tuning or adapting LLMs to specific business tasks using modern techniques (LoRA, PEFT).
• Open-Source Model Ecosystem: Practical experience with open-weight models such as Llama, Mistral/Mixtral, Qwen, Gemma, Phi, or similar models.
• DevOps and Deployment: Familiar with CI/CD practices, containerization (Docker), orchestration (Kubernetes), and observability tools for deployment in regulated environments.
• Excellent interpersonal skills, including collaboration, facilitation, and negotiation.
• Excellent planning and organizational skills, including the ability to plan and sequence a workstream's deliverables and coordinate the effort of other engineers contributing to it.

Nice to have

Knowledge of the finance industry