Job Role:
A dedicated startup is being formed to industrialize and scale a secure, AI-enabled, multi-source decision-support software offering. The platform is a multi-sensor fusion and agentic AI solution connecting to diverse data sources (for example geospatial layers, imagery, video, and other operational signals). This role will support the delivery of a scalable product and contribute to establishing the processes, standards, and collaboration practices required for sustainable growth.
Own the technical architecture of a fast-scaling software platform that combines geospatial workflows with AI capabilities, including LLM-enabled and agentic components. You set architectural direction, ensure engineering consistency and quality, and help teams deliver secure, scalable, and maintainable systems in a startup-paced environment.
Job Responsibilities:
- Define target architecture and reference patterns across platform, data, GIS services, LLM/agentic layer, and user applications.
- Translate product and operational requirements into end-to-end technical designs (APIs, services, data flows, model interfaces).
- Lead architectural reviews and decision-making (trade-offs, ADRs, standards, technical debt roadmap).
- Design integration approaches for data sources, sensors, SDKs/APIs, and third-party systems.
- Establish non-functional requirements and quality gates: performance, latency, reliability, observability, security, and cost.
- Guide engineering on scalable data and GIS patterns (spatial databases, tiling, indexing, spatial queries, ETL/streaming).
- Guide engineering on LLM/agentic patterns (tool calling, orchestration, evaluation, guardrails, prompt/version control).
- Partner with DevOps/MLOps to define deployment architectures, CI/CD, environment strategy, and release readiness.
- Support incident reviews and root-cause analysis; drive structural fixes and resilience improvements.
- Contribute to recruiting: role definition input, technical interviews, onboarding ramp plans, and mentoring.
- Operating principles
- Startup mode: hands-on, pragmatic, and delivery-oriented; comfortable pivoting while maintaining architectural integrity.
- Bias for clarity: lightweight documentation, explicit decisions, and repeatable engineering standards.
- Build for iteration: modular designs that enable fast experimentation without breaking core platform stability.
Qualifications and Experience:
- Typically 5+ years in software engineering, and 4+ years in architecture.
- Track record delivering platform architectures that scaled in users, data volume, and features.
- Bachelor's degree in Computer Science, Engineering, or equivalent experience.
- Tools (examples)
- GitHub/GitLab, CI/CD pipelines, IaC (Terraform or equivalent)
- Jira/Linear, Confluence/Notion
- Cloud services (AWS/Azure/GCP), container platforms (Kubernetes)
- Success measures
- Architecture adoption and consistency across teams (reduced rework, fewer integration surprises).
- Platform reliability and performance improvements (SLO/SLA attainment, reduced incident recurrence).
- Delivery velocity with controlled technical debt (predictable releases, clear trade-offs).
- Security and compliance readiness (design-time controls, auditability, least-privilege patterns).
