AI Platform Engineer
About the Company
The company is an AI-native software platform serving the landscaping industry. It is building an intelligence layer that connects field operations with back-office systems, transforming photos, voice notes, videos, and operational data into structured workflows and actionable insights.
The platform operates as a multi-tenant SaaS solution running on Kubernetes. Each customer receives an isolated runtime environment while benefiting from a shared intelligence layer that includes AI agents, reusable skills, retrieval systems, integrations, and platform services that continuously improve as adoption grows.
Job Brief
We are seeking an AI Platform Engineer to help design, build, and scale the company's core AI platform. This role will work directly with the Founding Engineer on the systems that power AI agents, skills, retrieval, APIs, and tenant management across the entire platform.
This is a highly technical, hands-on engineering role focused on building new capabilities rather than maintaining legacy systems. The ideal candidate will take ownership of major platform components, contribute to architectural decisions, and help evolve the foundation that supports mobile applications, customer implementations, partner integrations, and future AI-driven products.
You will design, develop, and deploy solutions that are used by real customers and field teams, with a direct impact on the future direction of the platform.
Responsibilities
AI Platform Development
- Design, develop, and maintain core platform services that support AI agents, skills, retrieval systems, and tenant operations.
- Extend and improve the multi-agent orchestration framework, agent runtime environment, and tool-calling infrastructure.
- Build scalable and maintainable agent execution systems capable of supporting multiple tenants.
- Develop reusable AI capabilities that can be shared across customers and applications.
Skills & Module Marketplace
- Design and develop systems that allow AI capabilities to be defined, versioned, published, and consumed across the platform.
- Build and maintain module lifecycle management, deployment workflows, and version control mechanisms.
- Develop reusable role-based bundles and capability frameworks for platform customers.
Retrieval & Knowledge Systems
- Improve retrieval architectures used for documents, transcripts, operational records, equipment manuals, and customer-specific knowledge.
- Develop and optimize vector search, graph-based retrieval, and knowledge indexing systems.
- Improve citation accuracy, relevance ranking, and knowledge retrieval performance.
- Design systems that support scalable and reliable retrieval workflows.
Platform APIs & Services
- Design, develop, and maintain public and internal APIs used by mobile applications, administrative tools, partner integrations, and AI agents.
- Create clean, scalable, and observable API architectures.
- Develop secure and reliable service communication patterns across platform components.
- Ensure platform services meet performance, scalability, and reliability requirements.
Multi-Tenant Platform Operations
- Design and improve tenant onboarding, migration, backup, recovery, and lifecycle management processes.
- Optimize platform architecture to reduce operational overhead as the customer base grows.
- Develop scalable solutions that support secure tenant isolation and resource management.
Engineering Excellence
- Develop unit, integration, and performance tests to ensure platform reliability and maintainability.
- Review, debug, and improve code quality following software engineering best practices.
- Participate in architecture reviews, technical planning, and system design discussions.
- Produce technical documentation, diagrams, specifications, and implementation plans.
- Collaborate closely with engineering leadership to shape the future direction of the platform.
Technology Stack
- AI Frameworks: LangChain, LangGraph, PydanticAI, custom multi-agent orchestration systems.
- Backend: Python, FastAPI, and Frappe.
- Frontend: TypeScript and Next.js.
- Vector Databases: Qdrant and pgvector.
- Graph Databases: Neo4j.
- Database: PostgreSQL.
- Infrastructure: Kubernetes, Helm, and DigitalOcean Kubernetes Service (DOKS).
- Observability: Logging, monitoring, tracing, and platform telemetry.
Requirements
- Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related field.
- 5+ years of experience in software engineering, platform engineering, or AI-focused development.
- Proven experience building and deploying production-grade AI agent systems.
- Strong experience with LangChain and LangGraph.
- Experience developing AI systems that utilize tool calling, planning loops, memory, evaluations, and workflow orchestration.
- Strong Python development experience.
- Experience developing APIs and backend services using modern software engineering practices.
- Strong understanding of multi-tenant SaaS architecture and distributed systems.
- Experience working with queues, caching systems, observability tools, and scalable cloud architectures.
- Strong system design and software architecture skills.
- Experience working in production environments where reliability and maintainability are critical.
- Ability to work independently and solve complex technical problems with minimal supervision.
- Excellent communication and collaboration skills.
Preferred Qualifications
- Experience with retrieval-augmented generation (RAG) architectures.
- Experience working with vector databases such as Qdrant, Pinecone, Weaviate, or pgvector.
- Experience working with graph databases such as Neo4j.
- Familiarity with Kubernetes and Helm.
- Experience with TypeScript and Next.js development.
- Experience building vertical SaaS products.
- Experience supporting field operations, logistics, construction, landscaping, or other operationally intensive industries.
- Experience designing AI systems for real-world production environments.
Key Areas of Ownership
The ideal candidate should be comfortable leading the following critical workstreams:
- AI agent architecture, orchestration, execution environments, and tool-calling frameworks.
- Retrieval systems utilizing vector search, graph databases, and knowledge management workflows.
- Multi-tenant platform architecture, tenant lifecycle management, and platform scalability.
- Public API design and platform services that support applications, integrations, and AI-driven workflows.
- Skills and module marketplace architecture, including deployment, versioning, and capability management.
How We Work
- Small, highly experienced engineering team.
- High-trust, high-ownership environment where engineers are expected to take initiative and drive solutions.
- Async-first collaboration with focused synchronous discussions when needed.
- Strong engineering standards, code reviews, testing, and documentation practices.
- Emphasis on pragmatic decision-making, continuous learning, and shipping high-quality software.
- Tools and technologies are chosen based on practical value and may evolve as the platform grows.
What Success Looks Like
- Delivering production-ready AI capabilities that improve platform intelligence and customer outcomes.
- Building scalable systems that support growing tenant adoption without increasing operational complexity.
- Improving retrieval quality, agent reliability, and platform performance.
- Contributing architectural decisions that strengthen the long-term foundation of the platform.
- Helping transform the platform into a scalable ecosystem of reusable AI agents, skills, integrations, and services.