Design and implement intelligent agents that automate complex, repetitive workflows across specialized enterprise software platforms.
Architect backend services that orchestrate agentic workflows, handling concurrency, reliability, and observability at scale.
Build agentic frameworks and tools to power enterprise agentic use.
Build systems that integrate with state-of-the-art AI models (LLMs, vision models, document parsing) to generate workflows, extract structured data, and drive adaptive execution logic.
Scale Backend Infrastructure
Own backend services that power our agent deployments.
Build robust APIs and data pipelines that connect field operations to back-office systems.
Ensure production reliability, performance monitoring, and incident response for mission-critical customer workflows.
Collaborate & Ship
Work directly with our CTO and founding team to break down ambitious product goals into shippable milestones.
Bring cutting-edge agentic capabilities into production.
Deploy and iterate rapidly based on real customer feedback from enterprise partners.
Use AI tools throughout your daily workflow to accelerate development and solve complex problems.
Required Qualifications
Professional engineering experience building production systems
Fluency in TypeScript or Python or Rust Bonus for more than one.
AI-native mindset: You use AI tools (Claude, Cursor, GitHub Copilot, etc.) every day to accelerate your work and tackle hard problems
Proven problem-solving ability: You can take ambiguous requirements, break them down, and ship working solutions
Startup DNA: You thrive in fast-moving environments where you own outcomes, not just tasks
Hands-on experience with building scalable distributed systems and backend services
Integrating with AI/ML models (OpenAI, Anthropic, or similar APIs)
Deploying and managing production services
Database design and optimization
Nice to Have
Experience with distributed workflow orchestration (Temporal.io, Cadence, or similar)
Infrastructure as code and containerization (Docker, Kubernetes)
Background in automation, RPA, or desktop application integration
Prior experience at an early-stage startup (seed to Series A)
Familiarity with industrial/enterprise software, legacy systems, or domain-specific engineering tools