Lead AI Engineer

Join to apply for the Lead AI Engineer role at Karbon . Karbon is the global leader in practice management software for growth‑minded accounting firms. We provide an award‑winning, highly collaborative cloud platform that streamlines work and communication, enabling the average accounting firm using Karbon to save 18.5 hours per week, per employee. We have customers in 34 countries and have grown into a globally distributed team, with our people based throughout the US, Australia, New Zealand, Canada, the United Kingdom, and the Philippines. We are well‑funded, ranked #1 on G2, have a fantastic team culture built on our values, are growing rapidly, and making a global impact. About this role We are seeking innovative Lead AI & ML engineers who thrive in a fast‑paced environment. You will have the unique opportunity to take on greenfield projects within a supported team, backed by executive‑level investmentp> Karbon is at the start of its AI journey. You will have the opportunity to shape our product and processes. The successful candidate will be capable of designing, implementing and maintaining AI models in a distributed production environment. You will be expected to develop bespoke AI solutions (Not just using LLMs) to automate workflows, generate insights and create efficiencies for our users. Responsibilities Design AI systems – analyse problems and apply machine learning to solve them. Machine learning and AI development – develop a wide range of machine learning applications. Model evaluation and selection – consider wider impacts beyond basic evaluation metrics. Productionise AI – own the process past deployment by owning production monitoring. Data management – work with data engineers to build and maintain data pipelines. Collaboration – work in a cross‑functional team with data engineers, analysts and full stack developers. Qualifications 7+ years of experience developing and deploying AI/ML solutions. Experience in a tech lead role (architectural decision making, completing tech designs, coaching junior engineers). Strong proficiency in LLM development and previous experience building autonomous agents (ADK, LangGraph, Agents SDK, etc.). Strong proficiency in Python and relevant ML frameworks (Sklearn, PyTorch, TensorFlow, spaCy, etc.). Strong understanding of traditional machine learning techniques (linear/logistic regression, random forest, GBM, etc.). Strong understanding of the machine learning development lifecycle, including MLOps, deployment and monitoring. A bachelor’s degree in Computer Science, Artificial Intelligence, Statistics, or equivalent experience is needed (master’s or PhD advantageous). It would be advantageous if you have: Experience developing and maintaining data pipelines (Snowflake, DBT, etc.). Previous experience in backend software development (in particular C#). Previous experience in Azure is advantageous. Benefits 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 commitment to diversity and inclusion Karbon embraces diversity and inclusion, aligning with our values as a business. We recruit and reward people based on capability and performance. We don’t discriminate based on race, gender, sexual orientation, gender identity or expression, lifestyle, age, educational background, national origin, religion, physical or cognitive ability, and other diversity dimensions that may hinder inclusion in the organization. Generally, if you are a good person, we want to talk to you. If there are any adjustments or accommodations that we can make to assist you during the recruitment process, please contact for a confidential discussion. At this time, we request that agency referrals are not submitted for this position. We appreciate your understanding and encourage direct applications from interested candidates. #J-18808-Ljbffr