Applying analytical models to predict business outcomes with tools such as DataRobot and languages like Python or R.
You will be part of a collaborative team that provides our clients with solutions that are practical as well as visionary and have an impact from the back office to the boardroom.
Demonstrated experience across a broad range of industries such as Energy and Resources, Public Sector, Financial Services, Life Sciences and Health Care, Consumer Business, Manufacturing, Telecommunications or Consumer Business.
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Act as a full stack "seed" within your team — champion full stack practices, mentor peers, and help scale AI-augmented development across the organization
Proactively identify efficiency bottlenecks in cross-team collaboration and propose improvements to product, design, and engineering processes
Contribute to the full product lifecycle — from ideation and design to deployment and iteration
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Contribute to technical design discussions, architecture reviews, and engineering best practices
Take ownership of features from conception through deployment and ongoing maintenance
We seek engineers who combine technical excellence with strong collaborative instincts—people who are excited to build meaningful products as part of a cohesive team.
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Own infrastructure as code — Terraform across multiple environments, CI/CD, and a server-less cloud footprint.
Build full-stack — backend services, an internal web app, and data-processing pipelines.
Own security and data protection — treat it as first-class: data isolation, least-privilege access, encryption, careful handling of credentials and sensitive user data. Security is a core requirement of everything we ship, not an afterthought.
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Develop knowledge-aware AI solutions using techniques such as retrieval-augmented generation, prompt orchestration and contextual reasoning where appropriate.
Ensure AI solutions follow security, governance, version control and audit requirements throughout the deployment lifecycle.
Analyse business and operational datasets to identify trends, optimisation opportunities and automation use cases.
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Agent identity and access model A defined approach for agent identities, sub-agent identities, scoped credentials, just-in-time access, secrets handling and approval-bound permissions.
Cyber knowledge and memory prototype A working context layer using selected cyber data sources such as assets, vulnerabilities, alerts, incidents, playbooks, code repositories or tickets.
Evidence and source-trust model A repeatable approach for grounding agent outputs in traceable evidence, with source references, confidence indicators, freshness checks and trust boundaries.
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* Understand the importance of creating products that can handle increasing user loads and traffic, and they implement security measures to protect user data and system integrity.
* Prioritize writing code that is easy to understand, modify, and debug, not only for themselves but for future developers.
* Work closely with other developers, designers, product managers, and stakeholders to translate business requirements into technical solutions.
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Drives the implementation of models to uncover patterns and predictions creating business value and innovation.
Partners with data engineering teams across multiple business lines to build data pipelines, improve data assets, quality, metrics, and insights.
Creates visualizations and other forms of communication that effectively showcase insights and findings to both technical and non-technical audiences.
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Arm the consultants. A co-pilot covering sourcing, screening, matching and outreach — built with the people who use it daily, measured on the time it gives back
We are pragmatic. Managed APIs over self-hosting, proven services over custom builds, low-code where it wins, code where it matters. You choose the stack — and defend every choice with numbers: accuracy, cost, time saved. Candidate data is handled to PDPA standards from day one; trust is our product.
Shipped work. The requirement that matters most: repos, products, pipelines or automations in real use. We assess what you've built — whether you learned it at university, in industry, or self-directed every single day
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