Forward Deployed Engineer

Role & Responsibilities: 

Key Responsibilities

• Embedded Delivery:

  • Embed with investment, finance, risk and operations teams to understand workflows at first hand, including the parts that are undocumented
  • Build, deploy and iterate on AI applications and agents directly against those workflows, working in short cycles with the users themselves
  • Take solutions from prototype through to production use, including integration with enterprise data, APIs and systems
  • Own the outcome rather than the handover, staying with a deployment until it is genuinely being used
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• Engineering:

  • Write production-quality code across the stack, from data pipelines and backend services to the interfaces users interact with
  • Build LLM and agentic components using established frameworks, applying the architecture standards set by the AI function
  • Instrument what is built so that quality, usage, cost and failure are visible after launch
  • Work within institutional security, data-governance and Responsible AI requirements from the first commit rather than at the end
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• Business Partnership:

  • Translate between business language and technical design, and push back where a request will not deliver the outcome intended
  • Demonstrate working software early and often, and use it to sharpen requirements
  • Train and support users through adoption, and build the internal advocates that adoption depends on
  • Feed recurring patterns back into the core AI platform so that solutions are reused rather than rebuilt
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• Internal & External Interfaces:

  • Works with the AI architect and AI engineers on standards, reusable components and platform direction
  • Partners with data engineering to secure reliable, governed data for each deployment
  • Engages business sponsors and end users continuously through the life of a deployment
  • Coordinates with Information Security, Compliance and Change functions to release into a controlled environment

Requirements

• Education & Qualifications:

  • Bachelor's or Master's degree in Computer Science, Engineering or a related technical discipline
  • Cloud or AI certifications advantageous
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• Experience Requirements:

  • 6 to 10 years in software or AI engineering, including work delivered directly with business users or clients
  • Hands-on experience building and shipping LLM or agentic applications into production
  • Experience in financial services, asset management, sovereign entities or enterprise-facing product engineering preferred
  • Track record of deployments that were adopted, not only delivered
  • Willingness to work on site with business teams and to travel where a deployment requires it
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• Technical Expertise:

  • Strong full-stack engineering ability, with strong Python and working command of a modern front-end framework
  • Agentic and LLM development using frameworks such as LangChain and LangGraph, including retrieval and orchestration
  • API design, integration with enterprise systems, and cloud deployment
  • Data engineering fundamentals, including pipelines, modelling and data quality
  • Evaluation and monitoring of deployed AI applications
  • Exceptional communication, and the credibility to sit with senior business stakeholders and be taken seriously
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