FDE Senior Engineer, AI & Capital Markets

Urban Ridge Supplies

Mississauga

Hybrid

CAD 150,000 - 210,000

Full time

14 days+

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Benefits offered by this job

Competitive Salary

Tailored training and skills dev

Private healthcare + EAP

Enhanced maternity/paternity package

Wellness Days

Job summary

KX’s Forward Deployed Engineering team seeks a senior, hands-on engineer to design and build systems that run LLMs on time-series data for leading banks and markets. You will deliver from design to production alongside quants and researchers in client environments.

Hybrid work from our Toronto office with frequent client interaction, owning retrieval, vector search and orchestration layers that scale to institutional volumes.

Qualifications

Extensive capital-markets experience building front-office systems in production.
Hands-on experience with LLM-backed retrieval and tool use in active deployments.
6+ years in or around capital markets technology.

Responsibilities

Identify viable use cases with quants, traders and researchers.
Map and prepare data estates, joining unstructured and structured data.
Build retrieval and inference layers, including embeddings and indexing.
Develop agent tooling with guardrails and entitlements.
Design an evolution framework with evaluation harness and latency benchmarks.
Lead production deployment in secure, regulated environments.
Create reusable accelerators and reference implementations.

Skills

Vector search

Time-series data

Python and SQL

Model orchestration

Data platform design

Capital-markets knowledge

q & kdb+ familiarity

Vector stores (FAISS, Milvus, Qdrant)

Tools

KDB.AI

FAISS

pgvector

Milvus

Qdrant

Job description

About KX

KX software powers the time-aware data-driven decisions that enable fast-moving companies to outpace competitors, realizing the full potential of their AI investments. The KX platform delivers transformational value by addressing data challenges related to completeness, timeliness and efficiency, ensuring companies understand change over time and can achieve faster, more accurate insights at any scale, cost-effectively.

KX is essential to the operations of the world's top investment banks, aerospace and defence, high-tech manufacturing, healthcare and life sciences, automotive and fleet telematics organizations. The company has established offices and a robust customer base across North America, Europe, and Asia Pacific.

Overview Of The Role

This is a senior, hands-on delivery role with the Forward Deployed Engineering team at KX: part solution engineer, part AI engineer. You work alongside quants, traders, e-trading desks and research teams inside their own environment, designing and building systems that put language models, agents and vector search on top of the deepest time-series data in the industry.

Working with the most innovative investment banks, hedge funds, market makers and exchanges globally, you own delivery from first design through to production, on timescales measured in weeks rather than quarters. The role needs enough domain knowledge to be trusted by the desk and enough engineering depth to make the system work at institutional volume.

About Forward Deployed Engineering

Forward Deployed Engineering is how KX works with its leading customers on their hardest problems. Rather than handing a customer software, we put senior engineers inside their environment, alongside their quants, traders, risk teams and platform engineers, to design, build and prove the thing that solves the problem, and we stay until it runs in production.

The team is deliberately small, senior, high impact and it is being built now, which means the standards, the architecture patterns and the way we work are still open questions. If you would rather set them than inherit them, this is the moment to join.

Key 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 evolution 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.

Skills

Working knowledge of embeddings and vector search: how to choose and tune an index, how to measure recall honestly, and why the obvious chunking strategy fails on financial documents.
Real data-modelling instinct: schema, partitioning and storage design for time-series at volume, and the judgement to know which decisions are expensive to reverse.
Comfort being the most technical person at the table and the most commercially aware person in engineering.
Python and SQL are essential. Experience with q and kdb+ is highly desirable but not required. The appetite to become genuinely good at it matters more.
The AI stack in practice: at least one major model API or an open-weights deployment, an orchestration or agent framework, and a vector store such as KDB.AI, FAISS, pgvector, Milvus or Qdrant. Considered opinions about what does and does not work matter more than brand familiarity.
Equivalent evidence counts. Depth in another tick store or time-series engine (OneTick, ClickHouse, InfluxDB, Arctic or an in-house build), or in array and functional languages (APL, J, OCaml, Haskell, Scala) where the thinking transfers directly to q.

Essential Experience

Strong capital-markets experience building front-office systems (trading, pricing, market data, risk or research platforms) in production and under real market conditions.
Hands-on experience building and shipping LLM-backed systems: retrieval architecture, evaluation, tool use and the operational reality of running them once real users arrive.
Six or more years in or around capital markets technology: sell-side, buy-side, a fund, an exchange, or a vendor who served them well.
Genuine depth in at least two of: equities, FX or futures microstructure; derivatives and pricing; market or credit risk; trade lifecyle analytics; quantitative research workflows.
Real-time and time-series data engineering: tick stores, streaming, historical replay and intraday reconciliation at institutional volumes.
Consultative range: you can run a discovery workshop, handle a sceptical head of trading, and write a solution document that survives procurement.
Practical experience with the plumbing: FIX engines, market data handlers, exchange protocols, and the reference and corporate-actions data that decides whether anything reconciles.

Preferred Qualifications