FDE Senior Engineer, AI & Capital Markets

KX provides a time-aware data platform that helps organizations make faster, more accurate data-driven decisions and realize the value of their AI investments. The Senior Engineer will work with capital-markets customers to identify AI use cases, build retrieval, inference, agent, and data-platform systems, and lead solutions from design through production deployment.

Responsibilities:

• Identify the viable use cases: work with quants, traders and researchers to identify where AI genuinely changes the economics, and to rule out the cases where a well-written query would do. Feasibility, data readiness, cost to run, and an agreed definition of good enough before anything gets built
• Map and prepare the data estate: work alongside the customer's quants and data owners to understand the estate and its ontology, then join unstructured sources (research, filings, news, broker commentary, chat) to structured market and trade data. Point-in-time correctness, corporate actions, symbology and survivorship are what decide whether the answer is right
• Build the retrieval and inference layer: embedding pipelines, chunking suited to financial documents, index selection and tuning, and hybrid search across vector similarity plus time, symbol and entitlement filters. Then the serving path: latency budget, cost per query, caching, and behaviour under load
• Build the agent and tooling layer: give models controlled access to the customer's data and analytics through tool and function definitions, context assembly and multi-step orchestration, with guardrails including entitlements, so an agent can never see what the user cannot
• Define the evaluation framework: build the evaluation harness alongside the customer's own experts: golden sets, accuracy and recall measures, regression tests, and latency and cost benchmarks. On a A system that cannot be measured will not be approved for production
• Design the data platform: schema, partitioning, on-disk layout, attribute and index selection, compression and query paths, so the analytics the desk wants next year are straightforward rather than a rebuild
• Lead through to production: deploy into a secure, regulated environment, with monitoring, alerting, index and model refresh, a runbook and a documented handover. You remain engaged until the system is running in production
• Build reusable assets: reusable accelerators, reference implementations, and clear requirements back to KX Engineering, so the next engagement starts further forward than the last