Senior AI Engineer

Senior AI Engineer – AI Transformation

Location: Fully Remote

About the Role

A growing international financial services company is building an AI Transformation function from the ground up and is looking for a Senior AI Engineer to become the technical engine behind this initiative.

This is a hands-on build role for an engineer who can take AI use cases from validated concept through to production. You will own the technical delivery end to end — including architecture, infrastructure, development, integrations, deployment, evaluation and ongoing operation.

You will work closely with the Head of AI Transformation, who leads use-case discovery and prioritisation, while partnering with existing engineering teams to integrate AI solutions into live products and business systems.

The role is suited to someone who has moved beyond prototypes and notebooks and has a proven track record of shipping production-grade LLM applications.

Key Responsibilities

• Take prioritised AI use cases from proof of concept through to production and own the full technical lifecycle.
• Build AI solutions across areas such as customer support, sales enablement, compliance, KYC, marketing, internal operations and analytics.
• Design and develop modern LLM applications and AI agents using RAG, agentic workflows, tool/function calling, structured outputs and multi-agent orchestration.
• Work with frontier AI model APIs, including OpenAI, Anthropic and other leading providers.
• Build the full technical layer around AI applications, including backend services, APIs, integrations and user-facing interfaces.
• Deploy, monitor and operate AI applications across major cloud environments.
• Integrate AI capabilities with existing business systems such as CRM, ticketing platforms, communication tools, internal databases and financial-services back-office systems.
• Develop evaluation frameworks and quality metrics for individual AI use cases, running evaluations before and after changes.
• Instrument AI applications to measure reliability, quality and measurable business impact.
• Help establish standards for AI safety, data handling, guardrails, traceability and auditability across the organisation.
• Design solutions that address risks including prompt injection, data leakage, incorrect model outputs, sensitive customer/financial data and unauthorised AI-driven actions.
• Build appropriate logging and audit mechanisms so AI outputs and actions can be traced and reviewed.
• Work with the Head of AI Transformation to assess technical feasibility, architecture, infrastructure requirements, delivery estimates, cost and timelines.
• Identify technical limitations and failure points early and determine pragmatic approaches to production delivery.
• Collaborate with product and engineering teams to integrate AI capabilities into existing workflows and systems.

What We're Looking For

• 5+ years of software engineering experience, including at least 2+ years building LLM-based applications.
• Proven experience taking AI solutions from prototype/POC into production.
• Ability to demonstrate 2–3 real-world AI implementations, including the problem addressed, technical solution, challenges encountered and measurable outcome.
• Strong full-stack engineering capability, with solid backend/API development skills and sufficient front-end experience to deliver usable interfaces.
• Practical DevOps experience covering CI/CD, containers and cloud deployment across AWS, GCP or Azure.
• Strong knowledge of the modern LLM application stack, including:
• Prompt engineering
• Retrieval-Augmented Generation (RAG)
• Agentic workflows
• Multi-agent/sub-agent orchestration
• Tool and function calling
• Structured outputs
• LLM evaluation and monitoring
• Strong software engineering fundamentals across APIs, backend services, data handling, deployment and monitoring.
• Understanding of common agentic AI failure modes, including context/window limitations, hallucinated or plausible-but-incorrect outputs, state loss, inconsistent reasoning and divergence from intended workflows.
• Experience implementing engineering controls to detect, mitigate and contain these failure modes.
• Experience integrating AI into existing business systems and operational workflows, rather than only developing standalone applications.
• High level of autonomy and ownership, with the ability to turn ambiguous requirements into working software.
• Strong English communication skills and the ability to explain technical decisions and trade-offs to non-technical stakeholders.

Nice to Have

• Experience with ETL/ELT, data pipelines, orchestration, data warehouses or lakehouse environments.
• Background in fintech, financial services, brokerage, banking or another regulated industry.
• Experience building voice AI, multilingual AI applications or customer-facing chatbots at scale.
• Exposure to AI governance, security, compliance or responsible AI practices in regulated environments.

What This Role Is Not

• Not a research role: you will apply and integrate frontier models rather than train foundation models.
• Not an ML/data science role: classical machine learning and model-training pipelines are not the core focus.
• Not a maintenance role: the majority of your time will be spent building and launching new AI capabilities.

Why Join

• Build from the ground up: help establish an AI engineering function rather than inherit an existing AI infrastructure.
• Founding AI engineering position: opportunity to grow into a senior technical leadership or architecture position as the function expands.
• High autonomy: short decision-making paths and significant ownership over technical choices.

If this opportunity aligns with your experience and career goals, please feel free to apply or reach out to discuss the role further.