---
title: 'MLOps Engineer at CUBE'
canonical: 'https://feeny.ai/job/mlops-engineer-cube-bengaluru-k5q4znftt65y'
type: 'job'
last_seen: '2026-09-09'
---

# MLOps Engineer at CUBE

- **Company:** CUBE
- **Location:** Bengaluru, India
- **Employment:** full-time
- **Posted:** 2026-09-01
- **Last confirmed live:** 2026-09-09
- **Apply:** https://jobs.ashbyhq.com/cube/86df2d98-f055-4a86-b843-a6a0dcf3788a

## Job description

CUBE are a global RegTech business defining and implementing the gold standard of regulatory intelligence for the financial services industry. We deliver our services through intuitive SaaS solutions, powered by AI, to simplify the complex and everchanging world of compliance for our clients.

Why us? 🌍 CUBE is a globally recognized brand at the forefront of Regulatory Technology. Our industry-leading SaaS solutions are trusted by the world’s top financial institutions globally. 🚀 In 2024, we achieved over 50% growth, both organically and through two strategic acquisitions. We’re a fast-paced, high-performing team that thrives on pushing boundaries—continuously evolving our products, services, and operations. At CUBE, we don’t just keep up we stay ahead. 🌱 We believe our future is built by bold, ambitious individuals who are driven to make a real difference. Our “make it happen” culture empowers you to take ownership of your career and accelerate your personal and professional development from day one. 🌐 With over 700 CUBERs across 19 countries spanning EMEA, the Americas, and APAC, we operate as one team with a shared mission to transform regulatory compliance. Diversity, collaboration, and purpose are the heartbeat of our success. 💡 We were among the first to harness the power of AI in regulatory intelligence, and we continue to lead with our cutting-edge technology. At CUBE, You will work alongside some of the brightest minds in AI research and engineering in developing impactful solutions that are reshaping the world of regulatory compliance.

We're looking for an MLOps Engineer to own the end-to-end lifecycle of machine learning models in production at CUBE. As our AI capabilities grow — spanning both proprietary ML models and large language model integrations — we need someone who can build the infrastructure and operational discipline that keeps those systems reliable, observable, and cost-effective. You'll sit at the intersection of data engineering, software engineering, and machine learning, translating the work of data scientists and AI architects into robust, scalable production systems. In a platform undergoing significant consolidation following CUBE's acquisitions, this role has real scope: you'll be helping to establish MLOps as a discipline from the ground up, not inheriting a fully formed practice. This role reports to the Lead Data Scientist and works closely with the broader Data and AI Engineering team. You'll be expected to bring rigour and ownership to everything from pipeline automation and model deployment to LLM observability and provider governance. We're looking for someone who cares about operability as much as capability — who understands that a model no one can monitor, retrain, or roll back isn't production-ready, regardless of its benchmark scores.

Build and maintain ML pipelines — design, automate, and operationalise end-to-end pipelines for model training, evaluation, and deployment using Azure-native tooling including Azure AI Foundry and Azure Machine Learning 3-4 years of experience into Machine learning, Azure, deployment, pipeline is needed. Own model deployment and serving infrastructure — manage containerised model endpoints, versioning, traffic management, and rollback mechanisms across environments Implement monitoring and observability — establish model performance monitoring, data drift detection, and alerting frameworks that give the team early warning of degradation in production Govern LLM usage across CUBE's platform — take ownership of LLM provider relationships (OpenAI, Azure OpenAI, Anthropic), manage API access and versioning, track token consumption, and optimise cost across workloads Manage the LLM gateway and prompt versioning — maintain tooling such as LangSmith or Helicone for tracing, evaluation, and prompt lifecycle management in production environments Support experiment tracking and model registry — ensure that experiments are reproducible, models are catalogued, and the path from experiment to production is governed and auditable Collaborate with data scientists and engineers — work closely with the Lead Data Scientist and Data Engineering team to understand modelling requirements and translate them into reliable production systems Champion engineering best practices — bring CI/CD, infrastructure as code, and automated testing discipline to ML workflows; reduce toil and manual intervention wherever possible Drive platform reliability and cost efficiency — monitor infrastructure spend, identify optimisation opportunities, and ensure platform SLAs are met Interested? If you are passionate about leveraging technology to transform regulatory compliance and meet the qualifications outlined above, we invite you to apply. Please submit your resume detailing your relevant experience and interest in CUBE.​ CUBE is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

## About CUBE

## Company Overview
- **One-liner**: CUBE provides Automated Regulatory Intelligence (ARI) solutions powered by a purpose-built AI engine (RegBrain), helping regulated firms across banking, insurance, and asset management stay compliant with ever-changing global regulations.
- **Entity Type**: Private (Private Equity backed; latest round led by Hg in March 2024)
- **Headquarters**: London, United Kingdom
- **Founded**: 2011
- **Founders**: Ben Richmond (CEO)

## Core Business
- **Primary industry**: RegTech (Regulatory Technology) – also classified as Technology, Information and Internet.
- **Target customers**: B2B – primarily Tier 1 & Tier 2 banks, insurers, asset/investment managers, payment firms; approximately 1,000 customers worldwide.
- **Mission / Purpose**: “To empower every regulated firm with the business intelligence to meet complex compliance challenges.”

## Products & Services
- **CUBE RegPlatform**: An end-to-end automated regulatory intelligence platform that captures, standardises, and contextualises regulatory data across financial crime, cybersecurity, privacy, tech risk, and more. Maps content onto customers’ compliance frameworks.
- **RegBrain**: Purpose-built AI engine (combining computer vision, generative AI, machine learning, and NLP) that tracks, analyses, and monitors laws, rules, and regulations in every country and published language, delivering always up-to-date regulatory views.
- **Regulatory Change Management (RCM)**: Services and tools to help clients manage regulatory updates and adapt their compliance posture.

## Market Standing
- **Valuation / Market Cap**: Not publicly disclosed.
- **Key Metric (Private)**: Total funding includes at least two major Private Equity rounds (led by Hg and Bregal Milestone). Reported annual revenue of GBP 2.7 million (likely an older or partial figure; reliable current revenue not publicly available).
- **Notable Investors / Partners**: Hg (lead in 2024 round), Bregal Milestone (lead in 2022 round). Also acquired Acin (2023) and The Hub.
- **Growth Signals**:
  - Global footprint across 6 main hubs, operating in 25 countries.
  - Employee count fluctuates between ~550 (LinkedIn) and ~700+ (company site), with 22 active job postings (as of mid-2025).
  - Recent acquisition of Acin and The Hub expanded capabilities and customer base.
  - Named market leader for regulatory intelligence; first deal with a Tier 1 US global bank in 2016 and deployed “the world’s biggest regulatory intelligence platform” at a major financial institution.

## Competitive Advantages
- **AI‐driven regulatory intelligence** – RegBrain automates the tracking, analysis, and monitoring of regulations across jurisdictions and languages, reducing manual effort and risk of missing changes.
- **Global coverage** – Covers regulations in every country and published language, a significant barrier for competitors.
- **End‑to‑end solution** – From data capture to compliance framework mapping, reducing the need for multiple vendors.
- **Expert‑validated insights** – Combines AI with human regulatory experts to ensure accuracy and context.

## Strategic Focus
- Expanding AI capabilities – further development of RegBrain, including agentic AI and generative AI features.
- Deepening content coverage and data quality across all regulated sectors.
- Growing the customer base beyond financial services into adjacent highly regulated industries.
- Strengthening global presence through strategic acquisitions (e.g., Acin, The Hub) and organic hiring.

## Why Work Here
- **Mission‑driven culture** – Emphasises “make it happen” mentality, giving employees autonomy to innovate and shape the company’s future.
- **Remote / Hybrid flexibility** – Many roles are listed as fully remote or hybrid (London hub). Global team across 19+ countries.
- **Development focus** – “Unlock endless opportunities for growth, collaboration and innovation”; internal mobility and skill‑building encouraged.
- **Diversity & Inclusion** – Actively collects DE&I data to improve hiring practices; values inclusive environment.
- **Engineering & AI‑heavy** – Large technical team (138 in Tech, 75 in Research, 51 in Product) working at the forefront of AI/ML applied to regulatory data.

## Sources
1. [cube.global – About Us](https://cube.global/about-us)
2. [cube.global – Careers page](https://cube.global/about-us/careers)
3. [careers.cube.global – main careers site](https://careers.cube.global/)
4. [LinkedIn – CUBE company profile](https://www.linkedin.com/company/cube-global)
5. [jobs.ashbyhq.com/CUBE](https://jobs.ashbyhq.com/CUBE)

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