Senior AI Engineer

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.

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.

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.

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.

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).

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.
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.