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Quartz

AI Engineer (Python)

Open Hall, NL, Canada

Full-time

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Applications open in a new tab.

Job details

Job type: Full-time
Apply by: Oct 18, 2026
Experience: 5+ years of software engineering experience, including meaningful ownership of a customer-facing LLM system in production
Posting language: English
Posted: Oct 5, 2026

Original job description

About Quartz

Quartz is reimagining wealth management through personalised intelligence. It's the first wealth platform that truly learns from you, understands your goals, and helps you make confident, informed decisions all in one secure platform. Get the experience of a personal banker that works with you 24/7, at a fraction of the cost.

About The Role

We're looking for an AI Engineer to own Charlie, the personal banker at the heart of Quartz. Charlie is live with UK members today, answering questions about their pensions, ISAs, savings and investments from data we aggregate across their accounts. You'll take responsibility for how Charlie reasons, what it is allowed to say, and how we know it is getting better. This is a production role, not a research one: you'll be judged on answer quality, safety and cost in front of real customers, in a regulated category where a confident wrong answer is worse than no answer.

What you'll be doing

Own and evolve the Charlie runtime end to end: agent roles and handoffs, prompt and tool architecture, model routing across providers, conversation memory, and the MCP integrations that give Charlie typed access to data in our Kotlin backend
Design and maintain the guardrails that keep Charlie accurate, compliant and on-brand, including what it must not do in a business that is not yet authorised to give regulated financial advice
Build and run our evaluation pipeline: define quality and relevance metrics, curate eval sets from real conversations, and gate releases on them so regressions never reach members
Drive reliability and cost: instrument every conversation, hunt down failure modes such as loops, duplicated actions and tool misuse, and keep inference spend proportional to the value delivered
Evaluate and integrate external capabilities (web search, market data, model providers) and make clear build-versus-buy recommendations
Work with product and the wider engineering team to turn wealth guidance logic into behaviour Charlie can execute safely, and feed what you learn from conversations back into the roadmap
Set the standards for how we build with LLMs at Quartz: testing, observability, prompt versioning and release discipline

What you'll need

5+ years of software engineering experience, including meaningful ownership of a customer-facing LLM system in production
Strong Python and FastAPI and strong relational data modelling in Postgres with SQL
Hands-on experience with agentic or tool-calling systems: function calling, retrieval, structured outputs, multi-step workflows
Practical experience with evaluation of non-deterministic systems: you have built eval sets, chosen metrics and defended a release decision with them
Solid grasp of guardrails and safety patterns for LLM applications, and the judgement to know when a model should decline
Startup or scale-up experience; you are comfortable owning a system alone and making it robust
Quick to learn, ambitious and driven by results
Fluency in English

Nice to have

Experience in financial services, wealth management or another regulated consumer domain, and an understanding of what an AI assistant can and cannot say there
Experience with workflow orchestration for long-running agent tasks (Temporal or similar)
Familiarity with search and retrieval APIs (for example Exa) and vector or hybrid retrieval over structured financial data
Cost engineering for LLM systems: caching, model routing, prompt compression
Exposure to a product-focused environment where you talk to users and read their conversations
Some Kotlin or JVM experience, or willingness to pick it up

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More about this job

Responsibilities

Own and improve the production AI assistant runtime, including agent workflows, prompts, tools, model routing, memory, and integrations with backend data. Establish safety guardrails and evaluation processes, improve reliability and cost, assess external capabilities, and set engineering standards for LLM development.

Requirements

Requires at least five years of software engineering experience, including meaningful ownership of a customer-facing LLM system in production, along with strong Python, FastAPI, PostgreSQL, and SQL skills. Candidates should have hands-on experience with agentic systems, evaluation of non-deterministic systems, and LLM safety, plus the ability to work independently in a startup or scale-up environment.

Skills

Python
FastAPI
PostgreSQL
SQL
LLM Systems
Agentic Systems
Function Calling
Retrieval
Structured Outputs
Evaluation Metrics
LLM Guardrails
Prompt Engineering
Model Routing
Conversation Memory
Observability
Cost Optimization

Visa sponsorship

Not detected in the job text

Keywords

Python
FastAPI
PostgreSQL
SQL
LLM
Agentic Systems
Function Calling
Retrieval
Structured Outputs
Multi-Step Workflows
Evaluation Pipeline
Guardrails
Prompt Architecture
Model Routing
Conversation Memory
MCP Integrations
Kotlin
Temporal
Web Search
Market Data
Vector Retrieval
Hybrid Retrieval
Caching
Prompt Compression
Observability
Prompt Versioning
Financial Services
Wealth Management
Regulated Consumer Domain

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