Job Description

Roles & Responsibilities

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.

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 (Optional): Practical experience fine-tuning or adapting LLMs to specific business tasks using modern techniques (LoRA, PEFT).

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

Other

Languages

English: C1 Advanced

Seniority

Lead