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Exiger

Graph ML Engineer

Reposted 2 Days Ago
In-Office
London, England
Senior level
In-Office
London, England
Senior level
Develop and deploy graph-based ML models for supply chain risk analysis, collaborate with teams, and lead AI advancements.
The summary above was generated by AI

We are seeking a highly skilled and innovative Graph ML Engineer to join our team focused on revolutionizing supply chain discovery and risk analysis. As part of our advanced Data Science and AI group, you will develop and deploy cutting-edge machine learning solutions that uncover hidden risks, enable due diligence, and enhance transparency across global supply chains among other applications.


Key Responsibilities:

  • Design and implement graph-based machine learning models to map and analyse complex supply chain networks.
  • Develop and fine-tune Large Language Models (LLMs) for extracting and synthesizing supply chain-related data from diverse text sources.
  • Build and optimize Graph RAG models
  • Create predictive models to identify risk factors and forecast disruptions within supply chains.
  • Collaborate with cross-functional teams including data engineers, product managers, and domain experts.
  • Contribute to the development of Agentic AI systems for dynamic and autonomous supply chain analysis and decision-making.
  • Provide technical leadership in AI and Data Science, and engage with the company’s R&D culture
  • Contribute to potential publications and/or IP patents

Professional Experience Required:

  • Strong and proven programming skills (Python preferred, but other languages may also be considered).
  • Proven experience with graph theory and graph ML algorithms (e.g., GNNs, GraphRAG, link prediction).
  • Hands-on expertise with LLMs (and transformers-based models) and transfer learning, model fine-tuning, and prompt engineering.
  • Strong background in building and deploying predictive ML models in production environments.
  • (Bonus) PhD-level (or post-graduate research) experience in Computer Science, Machine Learning, Data Science, or a related quantitative field.
  • (Bonus) Relevant publications in top venues in the field.
  • (Bonus) Familiarity with agent-based systems and multi-agent AI frameworks (e.g. LangGraph, CrewAI) (e.g. LangGraph, CrewAI)

We're an amazing place to work. Why?

  • Discretionary Time Off for all employees, with no maximum limits on time off
  • Industry leading health, vision, and dental benefits
  • Competitive compensation package
  • 16 weeks of fully paid parental leave
  • Flexible, hybrid approach to working from home and in the office where applicable
  • Focus on wellness and employee health through stipends and dedicated wellness programming
  • Purposeful career development programs with reimbursement provided for educational certifications

#LI-Hybrid


Exiger is revolutionizing the way corporations, government agencies and banks manage risk and compliance with a combination of technology-enabled and SaaS solutions. In recognition of the growing volume and complexity of data and regulation, Exiger is committed to creating a more sustainable risk and compliance environment through its holistic and innovative approach to problem solving. Exiger's mission to make the world a safer place to do business drives its award-winning AI technology platform, DDIQ, built to anticipate the market's most pressing needs related to evolving ESG, cyber, financial crime, third-party and supply chain risk. Exiger has won 30+ AI, RegTech and Supply Chain partner awards.

Exiger’s core values are courage, excellence, expertise, innovation, integrity, teamwork and trust.

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or protected veteran status, or any other legally protected basis, in accordance with applicable law.

Exiger’s hybrid work policy is periodically reviewed and adjusted to align with evolving business needs.

Top Skills

Agent-Based Systems
Graph Ml Algorithms
Graph Theory
Large Language Models
Predictive Ml Models
Python
Transformers

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