AI Engineer – Generative AI & Agents

Build generative AI applications and agent-based workflows that support employees and improve brokerage operations. Develop reliable retrieval, reasoning and tool-use capabilities around approved company knowledge and data. Turn use cases such as support assistance, policy search and operational investigation into tested, observable and maintainable software.

Key responsibilities

• Develop Python services and APIs for internal assistants, client-support copilots, document processing, case summarisation and operational analysis.
• Build retrieval-augmented generation pipelines using approved policies, procedures, product specifications and knowledge content, with source attribution and document version awareness.
• Implement agent workflows with structured outputs, tool schemas, bounded execution, state management, approval checkpoints and clear termination conditions.
• Develop brokerage-specific responses that respect entity, product, language and client context; prevent unsupported claims about trading conditions or account status.

Quality and model optimisation

• Compare model and retrieval approaches using representative datasets; select configurations based on grounded accuracy, task completion, latency and operating cost.
• Improve chunking, embeddings, metadata filters, reranking and context construction while preserving source permissions and preventing cross-client information leakage.
• Maintain versioned prompts, model configurations and evaluation datasets; use fine-tuning only where a validated business case and suitable approved data exist.
• Work with QA to test hallucinations, prompt injection, sensitive information disclosure, ambiguous instructions and failure to request human assistance.

Production delivery and support

• Integrate approved enterprise tools through controlled interfaces, using authenticated user context and explicit authorisation for actions that modify records or trigger workflows.
• Instrument application traces, retrieval results, model usage and tool outcomes with appropriate redaction, enabling diagnosis without exposing unnecessary client information.
• Implement graceful failure, timeouts, retries and human handoff when information is missing, confidence is insufficient or a requested action exceeds permissions.
• Maintain code, tests and operational documentation; investigate production failures and use reviewed feedback to improve application performance over time.

Essential experience and qualifications

• 4+ years in software, data or ML engineering, including at least 2 years building applied NLP, generative AI or LLM-based systems.
• Evidence of shipping and supporting production AI applications, including retrieval or agent workflows; prompt-only experimentation is insufficient.
• Strong Python, API development, automated testing, Git and software design skills.
• Hands-on experience with LLM APIs or open-weight models, embeddings, vector or hybrid search, structured outputs and tool calling.
• Understanding of evaluation design, retrieval quality, prompt injection risks, context limitations and the variability of model outputs.
• Ability to work with SQL, enterprise documents and permission-controlled data and to explain application limitations to business stakeholders.
• Bachelor’s degree in Computer Science, AI, Engineering or a related field, or equivalent practical experience.

Preferred experience

• Financial services experience, particularly brokerage operations, trading support or client service.
• Experience with an agent orchestration framework, multilingual retrieval and document extraction or OCR.
• Experience with model adaptation, inference optimisation and observability for LLM applications.

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