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.