Senior Machine Learning Engineer

Liminal is a global market intelligence and strategic advisory firm specializing in digital identity, financial crime and compliance, and IT security technology solutions across industries while also catering to the private equity and venture capital community. Founded in 2016, Liminal offers strategic and analytical services supporting executive decision-making at all product and business lifecycle stages. We advise some of the world’s most prominent business leaders, investors, and policymakers on building, acquiring, and investing in the next generation of solutions and technologies. We provide access to proprietary data and analysis, strategic frameworks, and integrated insights on the industry’s only market intelligence platform. 

Every major company in the world has started focusing on the next generation of digital identity technologies as a necessity for continued growth and security. Our team works with a myriad of organizations, from Fortune 100s to startups, across industries including financial services, technology, telecommunications, and the P2P economy. At Liminal, we help businesses build solutions, execute strategies, invest intelligently, and connect with key decision-makers. We know that it’s in the sharing of discovery and insights that groundwork is laid, problems are solved, and entire sectors advance at the speed of light. Keeping information to ourselves delays progress for all. At Liminal, we don't just respond to the market; we define it.

About the role

This role focuses on the end-to-end lifecycle of AI and machine learning solutions, from ideation and development to deployment and ongoing maintenance of production-grade systems. Reporting to the AI Solutions Architect and partnering closely with the Chief Product Officer and Chief Innovation Officer, the Senior Machine Learning Engineer will play an important role in building scalable, robust, and efficient AI-driven software solutions. This individual must be able to bridge exploratory projects with production systems, ensuring seamless transitions and operational excellence.

What you'll do

• AI Solution Development and Deployment:
• • Collaborate with cross-functional teams to define and design AI solutions aligned with business objectives.
  • Build, deploy, and maintain production-grade machine learning models and systems that drive efficiency and innovation across multiple departments.
  • Ensure AI solutions meet performance, scalability, and reliability standards in a production environment.
• Model Optimization and Maintenance:
• • Continuously improve machine learning models through fine-tuning, retraining, and incorporating new data and feedback.
  • Monitor and optimize the performance of deployed models and systems, implementing updates and resolving issues proactively.
  • Work with the AI Solutions Architect to develop pipelines for automated retraining, testing, and deployment of AI models.
• Cloud Infrastructure and Integration:
• • Leverage cloud platforms (e.g., AWS, Azure, Google Cloud) for model deployment, orchestration, and monitoring.
  • Implement and manage robust infrastructure to support AI applications, ensuring scalability and fault tolerance.
• Pipeline Development and Data Engineering:
• • Develop and maintain data pipelines for efficient data ingestion, preprocessing, and feature engineering.
  • Support integration of AI models into existing software systems, collaborating with product and engineering teams.
• Exploratory Analysis and Proof of Concept Development:
• • Conduct exploratory analyses to identify opportunities for AI innovation.
  • Design and prototype solutions, validating feasibility through proof of concept (PoC) projects.
  • Work closely with cross-functional stakeholders to develop experimental projects and transition them into fully developed systems.
• Performance Monitoring and Troubleshooting:
• • Implement monitoring tools to track model performance, system reliability, and data integrity.