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Principal ML Ops Engineer

Quantexa

Quantexa

Software Engineering, Operations, Data Science
London, UK
Posted on Monday, June 19, 2023

At Quantexa we believe that people and organizations make better decisions when those decisions are put in context – we call this Contextual Decision Intelligence. Contextual Decision Intelligence is the new approach to data analysis that shows the relationships between people, places and organizations - all in one place - so you gain the context you need to make more accurate decisions, faster.

Founded in 2016 Quantexa helps organizations make their data more meaningful, and is the world’s leading software company providing a single networked view of internal and external data as an input to human and AI driven decision making. From compliance, fraud, anti-money laundering and credit risk to customer intelligence and master data management, Quantexa partners with Global Tier 1 Insurers and Banks, Government Agencies, Telecoms and Technology companies to deliver Contextual Decision Intelligence.

Since being founded we’ve:

• Grown to 700+ employees

• Achieved “Unicorn” status being valued at over $1B in 2023

• Recognised global tech leader by multiple researchers including both Gartner and Forrester

As part of our ongoing growth we’re standing up an ML Ops team to more efficiently build, maintain and deploy the increasing number of AI models we provide with our platform. We’re recruiting the Team Lead to establish and run this new team. You’ll be supporting our two Data Scientist teams who create the generic Machine Learning Models and Data Science based components for use in our solutions. Our Analytical Innovation Team focuses on structured data, especially graph-based models and detection of risk. Our NLP group teaches machines to understand natural language, building products to help extract meaning and insight from text, while also conducting exploratory research that we believe will drive improvements in our products and advance the state-of-the-art.

The key objectives for the ML Ops role will be to support both these teams:

  • To make it faster, and more predictable for both Quantexa’s in-house Data science teams to build, deploy, monitor and maintain the various types of Machine Learning model we are creating.
  • Make deployment of models on site and in cloud simpler, faster and more consistent.
  • Where possible, and appropriate, the tools created should be extensible for use by clients for their custom ML models based on Quantexa Platform.

The key objectives for the ML Ops team will be to:

    • To make it faster, and more predictable for both teams to build, deploy, monitor and maintain the various types of Machine Learning model we are creating.
    • Make deployment of models on site and in cloud simpler, faster and more consistent.
    • Enable the tools created to also be used by our clients for their custom ML models based on Quantexa Platform.

Responsibilities

    • Establish the Quantexa ML Ops team and the team’s interfaces to other parts of Quantexa.
    • Advance our approaches to deployment of ML models within our platform.
    • Work with Architects and DS teams to select appropriate model architectures and patterns, including detailed dependencies. Optimise models to meet non-functional requirements. Document new approaches for reuse.
    • Building tools and processes to stand up required modelling environments on cloud, including environments with specialised capabilities such as GPUs.
    • Compose frameworks to support the Deployment, Testing, Monitoring and Governance of models.
    • Input into selection of 3rd party products and tools, as required, including any Vendor selection
    • Provide support for teams using ML models. Examples include liaising with cloud and DS teams to resolve issues with live models, supporting client deployment of models in partnership with DS teams; Providing Model API support to Quantexa’s platform teams.