Why This Job is Featured on The SaaS Jobs
AI capability is increasingly becoming a core product surface in SaaS, and this Senior AI Engineer role sits directly in that shift by focusing on models that run inside a production, distributed application. The listing signals a mature engineering environment with clear standards around quality, maintainability, and operational ownership, alongside an Azure-based stack and established observability practices. That combination is notable in SaaS, where AI work often fails to translate into reliable product outcomes without strong systems discipline.
From a career perspective, the role offers compounding SaaS-relevant experience across the full lifecycle: problem framing, model development, evaluation beyond headline metrics, and productionisation into agentic features that users actually touch. Collaboration with data engineering on pipelines and the emphasis on trade-offs, technical debt, and post-release support align with the realities of long-lived SaaS platforms. The exposure to MLOps considerations, monitoring, and iterative improvement is broadly transferable across AI-enabled SaaS teams.
This position is best suited to engineers who prefer end-to-end accountability over isolated experimentation, and who can operate autonomously within agreed objectives. It will appeal to practitioners who enjoy cross-functional design discussions, can communicate risks clearly, and want their ML work to be shaped by product constraints, reliability expectations, and measurable customer impact.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Our Engineering Standards
Balance Speed and Quality
Engineers are expected to balance delivery speed with a strong commitment to quality, meeting agreed timelines while producing reliable, maintainable, and well-tested solutions. Sound judgment in making trade-offs between velocity and long-term sustainability is essential.
Collaborate Effectively
Engineering is collaborative by default. Team members are expected to contribute constructively in design discussions, reviews, and planning, communicate clearly about progress and risks, and support shared team outcomes in both hybrid and distributed environments.
Build and Maintain Systems
Engineers are responsible for building new capabilities while maintaining and improving existing systems. This includes designing scalable solutions, reducing technical debt, supporting operational stability, and contributing to continuous improvement.
Operate with Autonomy
A high degree of autonomy is expected. Given clear objectives, engineers should independently translate problems into actionable technical approaches, proactively identify improvements, and continuously expand relevant technical expertise.
Ownership and Accountability
Ownership is fundamental. Engineers are accountable for the quality, performance, and customer impact of their work from design through post-release support, and are expected to follow through on commitments.
AI-Enabled Engineering
AI is reshaping how software is built, and we are committed to leveraging it as a force multiplier for creativity, impact, and capability. Engineers are expected to confidently apply strong technical fundamentals while embracing AI tools and approaches to enhance productivity, problem-solving, and innovation. Curiosity, adaptability, and enthusiasm for integrating AI into meaningful product development are essential.
Contribute to Team Culture
Engineers contribute positively to a culture of professionalism, transparency, low bureaucracy, and mutual respect, strengthening team performance through authenticity, curiosity, and collaboration.
About the Role
Karbon is at the cutting edge of AI and data products, and this role puts you at the centre of that progress. You'll have a direct hand in shaping both our product and the processes that power it. The ideal candidate will be confident contributing to Karbon's AI models in a distributed production environment, and equally skilled at building bespoke AI solutions — automating workflows, surfacing insights, and creating real efficiencies for our users
What you will own:
- Designing AI systems - you know how to analyse problems and apply machine learning to solve them
- Machine learning - you will be expected to develop a wide range of machine learning applications.
- Productionise AI - You will contribute to building end-to-end agentic solutions in our application
- Model evaluation and selection - You look beyond the basic evaluation metrics and consider wider impacts.
- Data management - Work with data engineers to build and maintain data pipelines
- Collaboration - You can work in a cross-functional team with data engineers, analysts and full stack developers.
What Sets You Apart!
If you’re the right person for this role, you have:
- 3+ years of experience developing and deploying AI/ML solutions
- Strong proficiency in Python and relevant ML frameworks (Sklearn, Pytorch, Tensorflow, spaCy, etc.)
- Strong understanding of traditional machine learning techniques (linear/logistic regression, randomForest, GBM, etc.)
- Strong understanding of machine learning development lifecycles
- Experience deploying machine learning models to production environments (Previous experience with Azure is advantageous)
- A Bachelor’s degree in Computer Science, Artificial Intelligence, Statistics, or equivalent experience is needed (Masters or PhD advantageous).
It would be advantageous if you have:
- Knowledge of deep learning architectures
- Previous MLOps experience
- Previous experience working with LLMs
- Experience developing and maintaining data pipelines (Snowflake, DBT, etc)
- Previous experience in backend software development (in particular C#)
Ideal for engineers who thrive in structured environments, complex domain systems, and enterprise-scale reliability challenges.
Our Core Technology Stack!
We build modern, scalable software on a thoughtfully designed stack:
- Frontend: TypeScript and JavaScript across Ember (today), React, and React Native.
- Backend: .NET / C# (Web API, .NET Core) powering distributed services.
- Data: SQL Server with performance and integrity at scale.
- Cloud: Microsoft Azure.
- Observability: Metrics, logging, alerting, and dashboards in Datadog — because we believe you can’t improve what you don’t measure.
Our architecture continues to evolve as we scale. We invest in event-driven systems, well-defined microservices, and containerized deployments (Azure Container Apps) to build resilient, decoupled, and high-performing software.
If you care about clean service boundaries, reliable systems, and shipping with confidence — you’ll feel right at home here.
Why Work at Karbon?
- Gain global experience across the USA, Australia, New Zealand, UK, Canada and the Philippines
- 4 weeks annual leave plus 5 extra "Karbon Days" off a year
- Flexible working environment
- Work with (and learn from) an experienced, high-performing team
- Be part of a fast-growing company that firmly believes in promoting high performers from within
- A collaborative, team-oriented culture that embraces diversity, invests in development, and provides consistent feedback
- Generous parental leave
