Why This Job is Featured on The SaaS Jobs
AI engineering roles in SaaS are shifting from isolated model building to product-embedded systems that must run reliably inside a multi-tenant application. This Associate AI Engineer position stands out because it is framed around production impact: contributing to AI models in a distributed environment, building agentic capabilities, and partnering with data and software teams rather than operating as a separate research function. The presence of a defined stack and explicit engineering standards signals a software-first approach to applied ML.
For a SaaS career, the durable learning here is the end-to-end loop from problem definition through deployment and post-release support. Experience with evaluation trade-offs, data pipeline collaboration, and operational ownership maps directly to how modern SaaS companies ship AI features safely and iteratively. Exposure to LLM patterns such as RAG and chaining also builds transferable intuition for integrating probabilistic systems into deterministic product workflows.
This role fits early-career engineers who prefer clear expectations, strong technical fundamentals, and accountability for customer-facing outcomes. It will suit someone comfortable working autonomously within a collaborative review culture, and interested in combining Python-based ML work with the realities of cloud deployment, observability, and long-lived codebases.
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
Associate AI Engineer
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 (Qualifications)
- 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.
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
