Forward Deployed AI Engineer

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.