Senior AI Engineer, Capital Markets — Hybrid

Kx; New York (NY); Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

KX seeks a senior Forward Deployed Engineer to deliver AI-enabled trading and research solutions. You will embed models, build retrieval layers over time-series data, and co-design agent tooling inside client environments, working with quants, traders and researchers to run in production.

Responsibilities include data estate mapping, near-term production delivery, and creating reusable accelerators for scalable engagements. Hybrid NY/TO office presence with global client work.

Qualifications

• Strong capital-markets experience building front-office systems in production.
• Hands-on experience with LLM-backed systems: retrieval, evaluation, tooling.
• 6+ years in or around capital markets technology.
• Depth in equities, FX, futures or derivatives and related pricing.
• Real-time and time-series data engineering at institutional scale.
• Consultative discovery and solution documentation for procurement.

Responsibilities

• Identify viable use cases with quants, traders and researchers to ensure economic impact and feasibility.
• Map and prepare data estates, joining unstructured sources to structured market data with correct point-in-time accuracy.
• Build retrieval, inference layers and hybrid search, optimizing latency and cost per query.
• Design and deploy an agent framework with controlled data access and guardrails.
• Define evaluation harness with golden sets, accuracy, recall and regression tests.
• Lead production deployment in a secure environment with runbooks and handover.
• Develop reusable accelerators and reference implementations to speed future engagements.

Skills

Embeddings & vector search

Time-series data

Python & SQL

LLM tooling

Vector stores

Tools

FAISS

pgvector

Milvus

Qdrant

KDB.AI

Job description

KX seeks a senior Forward Deployed Engineer to deliver AI-enabled trading and research solutions. You will embed models, build retrieval layers over time-series data, and co-design agent tooling inside client environments, working with quants, traders and researchers to run in production.

Responsibilities include data estate mapping, near-term production delivery, and creating reusable accelerators for scalable engagements. Hybrid NY/TO office presence with global client work.