Job Overview
This is a highly embedded, client-facing engineering role. The individual will play a role of Forward Deployed Engineer and will work directly with Fixed Income business users, technology teams, quants, operations, and control functions to identify high-value AI use cases, translate business workflows into technical designs, and build production-grade AI solutions.
This role is not advisory. The successful candidate will be expected to sit close to the business, understand front-office and operational workflows, write code, test solutions, debug issues, and help move AI use cases from concept to controlled production adoption.
The ideal candidate combines strong software engineering skills, practical generative AI experience, and a consultative approach to working with senior business stakeholders in a regulated financial-services environment.
Roles and Responsibilities
- Embed with banks Fixed Income teams in New York to understand business workflows, pain points, data availability, and AI opportunities.
- Partner with traders, sales, research, operations, risk, compliance, and technology stakeholders to translate business requirements into AI-enabled solutions.
- Design, prototype, and build generative AI applications using Python, OpenAI or Claude Agent SDK and other agentic frameworks (comparable LLM APIs), prompt engineering, MCP integrations, orchestration, retrieval-augmented generation, and agentic workflows.
- Develop AI solutions that support Fixed Income use cases such as knowledge retrieval, workflow automation, document analysis, trade support, client intelligence, operational exception handling, and management reporting.
- Build retrieval pipelines across structured and unstructured data, including research, policies, procedures, trade-related data, market commentary, client documents, and internal knowledge repositories.
- Apply prompt engineering techniques to improve model accuracy, consistency, explainability, and user trust.
- Design evaluation frameworks to test AI outputs for accuracy, completeness, hallucination risk, relevance, and compliance alignment.
- Work with technology and control teams to ensure solutions meet enterprise standards for security, data privacy, auditability, access control, and model governance.
- Communicate technical trade-offs clearly to both engineering teams and business stakeholders.
- Support user adoption by gathering feedback, refining workflows, and ensuring AI tools solve real business problems rather than becoming standalone experiments.
- Identify reusable patterns, accelerators, and components that can be scaled across other financial-services use cases.
Required qualifications
- Bachelor’s degree in computer science, Engineering, Mathematics, Finance, or a related field, or equivalent practical experience.
- Strong hands-on experience with Python and modern software engineering practices.
- Practical experience building applications using Python, OpenAI or Claude Agent SDK and other agentic frameworks (comparable LLM APIs).
- Strong understanding of prompt engineering, including prompt design, prompt testing, grounding, context management, and output validation.
- Experience building AI or data-driven applications that use APIs, databases, cloud services, and enterprise data sources.
- Ability to work directly with business stakeholders, lead discovery discussions, clarify ambiguous requirements, and convert business problems into technical solutions.
- Strong communication skills and the ability to explain AI concepts, risks, and design choices in clear business language.
- Experience working in agile, fast-moving environments where prototypes must quickly evolve into production-ready solutions.
Preferred qualifications
- Financial-services experience, ideally within capital markets, Fixed Income, trading, sales, risk, operations, or banking technology.
- Familiarity with Fixed Income products such as rates, credit, securitised products, municipals, FX, or derivatives.
- Experience with retrieval-augmented generation, vector databases, embeddings, semantic search, and document intelligence.
- Experience designing AI evaluation frameworks, including test datasets, quality metrics, human review loops, and model performance monitoring.
- Familiarity with enterprise AI governance, model risk management, compliance review, data privacy, and regulatory expectations in financial services.
- Experience in a forward-deployed, consulting, client engineering, post-sales engineering, or technical delivery role.
- Exposure to cloud platforms, secure APIs, authentication/authorisation, and enterprise integration patterns.
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