AI Engineer

We are building an AI transformation function from the ground up. We are hiring an AI Engineer to be the technical engine of this function - a hands-on builder who takes AI use cases from validated idea to production, end to end.

This is a build role. You work closely with the Head of AI Transformation, who owns use case discovery and prioritization. You own the rest - product architecture, the infrastructure each use case needs, delivery estimates, and the build itself. You architect it, build it, integrate it, ship it, and run it.

You will work alongside the engineering teams who own the products, partnering with them on integration into live systems. You are expected to be the strongest AI builder in the room.

What you'll do

• Take prioritized AI use cases from proof-of-concept to production, and own the full lifecycle. Areas include customer support, sales enablement, compliance and KYC workflows, marketing content, internal operations, and analytics assistants.
• Design and build the LLM applications and agents behind these use cases - RAG pipelines, agentic workflows, tool and function calling, structured outputs, multiagent orchestration - on top of frontier model APIs (Anthropic Claude, OpenAI, and others).
• Build the fullstack around the AI: backend services, APIs, and the front-end surfaces users actually touch.
• Own deployment and operations. Deploy to the selected cloud provider, monitor, and keep things running.
• Integrate AI into the systems where work happens: CRM, ticketing and support platforms, communication tools, internal databases, and trading-adjacent back-office systems.
• Build evaluation harnesses and define quality metrics per use case. Run evals before and after changes. Instrument solutions so business impact is shown with evidence.
• Help define AI safety, data-handling, and guardrail standards at company level, not just per project, and build to them. Manage sensitive customer and financial data, prompt-injection and data-leakage risks, and design for traceability so that AI-driven actions and outputs can be logged, audited, and explained in a regulated financial services environment.
• Work closely with the Head of AI Transformation on product architecture, infrastructure, and estimates during use case evaluation: what's buildable now, what's fragile, what the real cost and timeline look like.

What we're looking for

• Proven track record of shipping AI use cases to production - not demos, not notebooks. You can walk us through 2-3 real implementations: the business problem, what you built, what broke, and what impact it had.
• 5+ years of software engineering experience, with 2+ years building LLM-based applications.
• Strong fullstack development - backend services and APIs, plus enough front-end to ship a usable interface.
• DevOps capability - CI/CD, containers, and deployment to a major cloud provider (AWS, GCP, orAzure). You deploy and operate what you build.
• Deep working knowledge of the modern LLM application stack: prompt engineering, RAG, multi-agent/subagent orchestration, tool and function calling, and evaluation harnesses.
• Strong general engineering fundamentals: APIs, backend services, data handling, deployment, monitoring. We don't mind which languages or frameworks you prefer - we care that you understand the key components and choose pragmatically.
• Demonstrated command of the failure modes of agentic development - context and window management, model output that's plausible but wrong, loss of state across sessions, silent divergence from plan - and the engineering practices that detect and contain each.
• Experience integrating AI into existing business systems and workflows, not just standalone apps.
• Ability to operate autonomously with high ownership: ambiguous input, working software output.
• Strong communication in English - you'll explain technical trade-offs to non-technical stakeholders.

Nice to have:

• Data pipeline experience - ETL/ELT, orchestration, warehouse or lakehouse work.
• Experience in fintech, brokerage, banking, or another regulated industry.
• Experience with voice AI, multilingual applications, or customer-facing chatbots at scale.

What this role is not

• Not a research role - we apply frontier models, we don't train them.
• Not an ML/data-science role - no model training pipelines or classical ML expected as core work.
• Not a maintenance role - you'll be building new things most of the time.

Why join us

• Ground-floor AI transformation - you're building the function, not maintaining someone else's.
• A path to lead. This is the founding AI engineering hire. As the function grows, the right person is positioned to become the lead and architect of the AI department.
• Real autonomy: short decision paths, no legacy AI infrastructure to inherit. Can be fully remote, with flexibility on location and working setup.
• A portfolio of diverse use cases across support, compliance, marketing, and operations - you won't be stuck on one product surface.