Senior AI Engineer

Role & Responsibilities: 

Key Responsibilities

• Build Responsibilities:

  • Design and build agentic systems, autonomous decision agents and intelligent workflows to production-grade quality.
  • Engineer LLM prompts, retrieval and orchestration workflows, optimising for accuracy, latency, cost and reliability.
  • Build orchestration workflows and integrate agents with enterprise data, APIs and applications, ensuring secure and reliable operation.
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• Model Lifecycle:

  • Implement training and fine-tuning, evaluation, versioning and monitoring in partnership with the AI architect and established MLOps practices.
  • Apply traditional machine learning alongside LLM approaches where it is the better tool for the problem.
  • Maintain strong command of the Python stack and tools such as PyTorch, TensorFlow and MLflow across the model lifecycle.
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• Quality & Performance:

  • Establish evaluation, guardrails and monitoring to safeguard agent quality, cost and performance.
  • Lead remediation of issues in production, from detection through to resolution.
  • Hold the line on reliability as agents move from prototype into institutional use
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• People & Leadership Responsibilities:

  • Set engineering standards for the AI squad and enforce them in review.
  • Mentor AI engineers and raise the technical quality of what the team ships.
  • Work closely with the AI architect to translate architectural direction into working systems.
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• Internal & External Interfaces:

  • Partners with the Senior AI Architect on design, standards and delivery sequencing.
  • Works with data engineering teams to secure reliable, governed data for agents.
  • Engages business and operational stakeholders to define what an agent must do and how success is measured.
  • Collaborates with security and governance functions on safe deployment.

Requirements

• Education & Qualifications:

  • Bachelor's or Master's degree in Computer Science, Engineering, Data Science or AI/ML.
  • AI/ML certifications preferred.
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• Experience Requirements:

  •  6 to 10+ years in AI/ML or software engineering, with hands-on agentic and LLM development.
  • Track record shipping production-grade AI and agentic applications.
  • Experience in financial services, sovereign enterprises or investment organisations preferred.
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• Technical Expertise:

  • Agentic and LLM engineering, building agents and LLM applications using frameworks such as LangChain and LangGraph.
  • Strong command of Python and of tools such as PyTorch, TensorFlow and MLflow, plus traditional ML such as LightGBM and XGBoost.
  • Orchestration and integration with enterprise data, APIs and applications.
  • Evaluation, guardrails and monitoring for agent quality, cost and performance.
  • Engineering leadership, setting standards and mentoring AI engineers.