Senior AI Engineer

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 - Design, build, and deploy AI systems that solve important customer problems and produce measurable business outcomes.
• Experiment with AI - build prototypes, evaluation harnesses, and reference implementations to see what works and what doesn’t.
• Productionise AI - You will contribute to building end-to-end agentic solutions in our application
• AI evaluation and observability - You look beyond the basic evaluation metrics and consider wider impacts.
• Technical Decisions - Make sound technical decisions across models, agents, retrieval, tools, data, reliability, observability, latency, cost, safety, security, and governance.
• 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:

• Minimum 5+ years of experience in a software engineering, or machine learning engineering
• Experience developing agentic AI solutions and deploying them to production environments (Langchain, OpenAI SDK, Google ADK is advantageous) 
• Are highly proficient in Python and comfortable working across an AI application stack; experience with C#, and React is advantageous.
• Strong understanding of how to evaluate AI systems systematically using representative data, graders, production signals, and human judgment.
• 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:

• Examples of Agentic AI products you have developed and have launched to customers
• Previous MLOps experience and platform experience with Azure
• Previous experience with Databricks would be advantageous
• Strong stakeholder engagement and communication skills, with the ability to translate strategic objectives into practical technical direction and execution.

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.
• AI Microservices: Python (ADK, FastMCP) powering our AI agents
• 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

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.