Senior Machine Learning Engineer (Knowledge Graph expert)


City of London
Permanent
Negotiable
Financial Technology
PR/561251_1761824489
Senior Machine Learning Engineer (Knowledge Graph expert)

Our client, a leading multi-strategy hedge fund managing over $20 billion of AUM, is seeking a Senior ML Engineer to join their high-performing Applied AI team, driving a new era of intelligent systems that underpin the organisations most critical decision-making. You will be developing production-grade AI systems that empower portfolio managers, analysts, and researchers with intelligent, data-driven capabilities to design scalable systems that integrate cutting-edge AI models, including LLMs and leveraging expertise in Knowledge Graphs and Graph Databases (Neo4j preferred).

Responsibilities:

  • Design and build intelligent data retrieval systems that power AI-driven investment tools.
  • Collaborate with ML researchers to prototype, develop, and deploy new AI/ML products.
  • Work with frontend engineers to integrate backend systems into user-facing applications.
  • Lead architectural decisions and contribute to the evolution of AI infrastructure.
  • Participate in the full software development lifecycle, from design through deployment and support.
  • Mentor junior engineers and contribute to a culture of technical excellence.
  • Support critical infrastructure through on-call rotations and incident response.

Requirements:

  • 10+ years of professional software engineering experience, with 4+ years focused on ML systems
  • Must have expertise in Knowledge Graphs and Graph Databases (Neo4j preferred)
  • Advanced proficiency in Python, including ML libraries (e.g., PyTorch, scikit-learn)
  • Strong experience with distributed systems, data engineering, and API development
  • Proficiency in both SQL and NoSQL databases
  • Familiarity with Docker, Kubernetes, and CI/CD pipelines
  • Experience integrating LLMs and RAG systems into production environments
  • Familiarity with OpenAI, Anthropic Claude, or similar AI platforms
  • Experience with vector databases and semantic search
  • Understanding of AI agent architectures and multi-agent systems
  • Exposure to observability tools like Grafana, Prometheus, or Sentry

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