Canbury
Senior Data & AI Engineer
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About Canbury Insights
Canbury is a technology-enabled sustainability consultancy applying AI tools to deliver research, data, and analysis thoroughly, effectively, and cost-efficiently. We work with leading institutional investors, companies, and NGOs on specialized sustainability topics, including climate change, nature, social inequality, systems change, and policy change.
Alongside the advisory business, we build and run our own data platform. It combines structured ESG data with AI-assisted analysis of company disclosures. This opportunity offers the chance to work on and shape the strategy of a product used by institutional clients.
You can find more on our service lines and examples of our work at canbury.io.
The Role
This is a hands-on engineering role that sits close to client delivery. Roughly half the work is building and hardening the pipelines and data models behind the platform and our core delivery work. The other half is applying them to live client engagements, which means understanding what has actually been asked for, shaping the technical approach with our consultants, and standing behind the output.
You will:
- Build and maintain AI-assisted assessment pipelines that extract and evaluate evidence from company disclosures, with the quality assurance and citation discipline our clients expect
- Own data models and ingestion for portfolio and issuer data
- Work alongside consultants, and sometimes directly with clients, to turn methodology into something reproducible and auditable, and to scale established methodologies across new engagements
- Take responsibility for output quality, including evaluation frameworks, benchmarking of pipeline performance, and the review steps that catch errors before a client does
- Contribute to platform engineering more broadly: APIs, backend services, deployment, and observability
- Help shape engineering standards as the team grows, and support more junior engineers
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What We Are Looking For
Essential
- Around four to five years of professional software or data engineering experience, with strong Python
- Practical production experience with large language models, including prompt design, evaluation, and the work of making probabilistic output reliable enough to ship
- Solid relational data modelling and SQL, and experience building pipelines that other people depend on
- Cloud experience (Microsoft Azure or GCP), and comfort owning a piece of work end to end from design through to deployment and monitoring
- The ability to work with non-technical colleagues and clients, explain trade-offs clearly, and push back where a request does not make sense
- Comfort with ambiguity and with small-company reality, where scope is not always defined for you
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Desirable
- Financial services, investment management, or ESG and sustainability domain exposure
- Experience with document processing and information extraction at scale
- Experience introducing evaluation or testing practice into an AI product
What Success Looks Like
- Within three months: contributing independently to the delivery of an assessment pipeline for a live client engagement, and trusted to make day-to-day technical calls without close supervision.
- Within twelve months: owning a pipeline or data domain outright, with our approach to quality assurance and evaluation measurably better for their involvement, and consultants coming to them directly when scoping new work.
Why the Role is Interesting
Small team, and an unusually direct line between the work and the client. The AI work is substantive rather than decorative, applied to a domain where accuracy carries real consequences. There is genuine influence available over technical direction at an early stage.
Interview Process
Three stages after the initial recruiter screen:
- Introductory call with the Chief Technology Officer
- Technical exercise and discussion with the engineering team
- Final conversation with one of the founders
We aim to move quickly and to give a decision within a week of the final stage.
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