SteelEye Tech Stack

Integrated trade and communications surveillance platform for financial compliance

Software Development London, England 51–200 employees Founded 2017 Privately Held

SteelEye builds a unified surveillance platform for trade monitoring and communications capture in regulated financial markets. The tech stack—Python, PyTorch, TensorFlow, scikit-learn, Elasticsearch, and orchestration via Airflow and Prefect—reveals a machine-learning-heavy architecture focused on signal detection and false-positive reduction. Active hiring is concentrated in data roles across three countries, and the project list shows the company is mid-transition from proof-of-concept execution to live surveillance deployments, with ongoing work on NLP models for communications analysis and ETL pipeline hardening.

Tech Stack 21 technologies

Core StackPython Pandas NumPy scikit-learn PyTorch TensorFlow AWS Docker Kubernetes Apache Airflow Prefect Elasticsearch Jira Confluence Monday.com SQL Pydantic Conductor spaCy Azure

What SteelEye Is Building

◆Challenges

Reducing false positives
Improving detection quality
Trading data integration challenges
Onboarding new clients
Ensuring health & safety compliance
Managing day-to-day office operations

▲Active Projects

Nlp/comms surveillance models
Trade surveillance analytics
Client onboarding and data integration
Proof of concept execution
Transition to live trade and communication surveillance
Model deployment pipelines
Etl pipelines for communications and trades datasets
Implement and evolve pydantic schemas and data mappings
Client onboarding projects

Hiring Activity

Accelerating6 roles · 5 in 30d

Department

Data

Ops

Product

Seniority

Mid

Junior

Manager

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About SteelEye

SteelEye delivers a single-platform solution for trade and communications surveillance, addressing the fragmented tooling that has historically forced compliance teams to stitch together multiple point products. The company operates primarily in regulated financial services, serving institutions subject to MiFID II and equivalent trade-reporting and record-keeping obligations. Clients span multiple geographies: SteelEye is actively hiring in Singapore, Portugal, and the UK, indicating regional expansion and localized deployment complexity. The platform ingests trade and communications data at client onboarding, applies machine learning to detect anomalies and suspicious patterns, and surfaces alerts through analytics dashboards—all from a single console rather than separate surveillance, archival, and reporting systems.

HeadquartersLondon, England

Company Size51–200 employees

Founded2017

Hiring MarketsSingapore, Portugal, United Kingdom

Frequently Asked Questions

What technology does SteelEye use to build its surveillance platform?

Core stack includes Python, PyTorch, TensorFlow, scikit-learn for ML, Elasticsearch for search and analytics, Apache Airflow and Prefect for orchestration, Kubernetes and Docker for deployment, and AWS/Azure for cloud infrastructure. Data processing relies on Pandas, NumPy, and Pydantic schemas.

What is SteelEye working on in 2024?

Key projects include NLP/communications surveillance models, trade surveillance analytics, client onboarding and data integration, model deployment pipelines, ETL for trades and communications datasets, and transitioning proof-of-concept systems to live surveillance environments.