Abacus AI is the world's best AI super assistant and general-purpose agent!  Customers use Abacus AI as their AI control center from where they can create, deploy, and monitor AI agents. These agents can do any task from creating documents to automating workflows and complex decisions.

We are leaders in agentic research, and our state-of-the-art AI agent, Abacus AI Agent, can create entire software systems, including vibe-code SaaS apps, from agentic templates.  

Thousands of AI-native businesses have been started on our platform!  With over 2 million customers and several F500 Companies, we are automating white-collar work and building the AGI control center of the future.

Abacus.AI

Abacus AI is your very own AGI control center from where you can create, deploy and monitor AI agents. 

Customer Success EngineerCompany: Abacus.AILocation: RemoteType: Full-time
About Abacus.AI:Abacus.AI is an enterprise AI platform that transforms how businesses build and deploy AI systems. Our platform automates AI development through advanced generative AI technology, eliminating the need for extensive technical expertise.Our core offerings include ChatLLM Teams with access to leading AI models, Abacus AI Agent for autonomous task execution, and customizable AI agents for chatbots, workflows, and enterprise automation. We also contribute to the AI community through LiveBench.ai benchmarking and open-source projects like Dracarys and Smaug.We make sophisticated AI and machine learning capabilities accessible to organizations regardless of size or technical resources.
Role Overview:We're looking for someone with a hands-on technical delivery background: A current or former Solutions Architect, Forward Deployed Engineer, Implementation/Integration Engineer, Solutions Engineer, or Technical Account Manager with real build experience. The right person has spent their career deploying software into customer environments: scoping architecture, writing integration code, debugging in production, and sitting shoulder-to-shoulder with customer engineering teams. This is not a coordination role. You'll be in the codebase and on the architecture whiteboard.
Key Responsibilities:Lead Architecture & Integration Design: Partner with customer engineering and platform teams to design how Super Assistant fits into their existing stack: Data sources APIs, retrieval layers, and downstream systems. Run technical design sessions, produce reference architectures, and make the build-vs-configure calls.Ship Vibe-Coded Apps & Integrations: Rapidly prototype working applications and integrations directly in front of the customer using AI driven development.Drive Super Assistant Rollout as Technical Lead: Own deployment across customer teams end to end.Translate Requirements into Technical Execution: Convert customer business goals into concrete technical scope: Keep the internal FDE team's execution tightly coupled to what the customer actually needs in production.Feed the Product Roadmap: Bring field-level technical patterns, integration gaps, and platform limitations back to Product and Engineering, with enough specificity to influence prioritization.Lead Executive & Technical QBRs: Present deployment progress, architecture evolution, usage and performance metrics, and realized ROI to both engineering leads and executive stakeholders.Expansion Discovery: Partner with the Sales team to identify new use cases for our AI capabilities, helping customers expand their footprint and see deeper value from the Abacus platform.
Required Qualifications:Strategic Technical Communication: Exceptional ability to bridge the gap between technical and non-technical stakeholders; you can explain LLM performance or RAG architecture to an executive just as easily as you can discuss API integration with a developer.Generative AI & LLM Literacy: Solid understanding of the GenAI landscape, including Hands-on experience with Prompt Engineering, RAG (Retrieval-Augmented Generation) architectures, and the nuances of deploying LLMs in a corporate environment.Functional Machine Learning Knowledge: A strong conceptual grasp of the ML lifecycle from data prep, model training, and evaluation metrics allowing you to collaborate effectively with internal Data Science teams and guide customers through technical deployment.Program & Rollout Management: Proven track record of managing technical software rollouts (like an AI "Super Assistant") within large organizations, focusing on user adoption, monitoring ongoing health, and steering account progress through QBRs.
What We Offer:Competitive salary and equity packageOpportunity to work with cutting-edge AI technologyCollaborative and innovative work environmentProfessional development and learning opportunities