Associate AI Engineer

Karbon is the global leader in AI-powered practice management software for accounting firms. We provide an award-winning cloud platform that helps tens of thousands of accounting professionals work more efficiently and collaboratively every day. With customers in 40 countries, we have grown into a globally distributed team across the US, Australia, New Zealand, Canada, the United Kingdom, and the Philippines. We are well-funded, ranked #1 on G2, growing rapidly, and have a people-first culture that is recognized with Great Place To Work® certification and on Fortune magazine's Best Small Workplaces™ List.

Our Engineering Standards

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.

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 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 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.

Engineers contribute positively to a culture of professionalism, transparency, low bureaucracy, and mutual respect, strengthening team performance through authenticity, curiosity, and collaboration.

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.

• 1-3 years of experience developing and deploying AI/ML solutions
• Experience working with LLMs (RAG, Chaining, MCP, etc) 
• Proficiency in Python and relevant ML frameworks (Sklearn, Pytorch, Tensorflow, spaCy, etc.)
• Good understanding of traditional machine learning techniques (linear/logistic regression, randomForest, GBM, etc.)
• Familiarity with machine learning development lifecycles
• 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:

• Experience in agentic frameworks (ADK, LangGraph, Agent SDK etc.)
• Previous MLOps experience
• Experience developing and maintaining data pipelines (Snowflake, DBT, etc) 
• Previous experience in backend software development (in particular C#)
• Knowledge of deep learning architectures
• Experience deploying machine learning models to complex production environments (Previous experience with Azure is advantageous)

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.