Senior Software Engineer, Platform & Agentic Systems

Tomorrownow

2d•Remote

About The Position

The Senior Software Engineer, Platform & Agentic Systems is a senior, hands-on engineering role at the heart of TomorrowNow’s technology stack. You will be responsible for the continued development, scaling, and evolution of the Global Access Platform (GAP) — our core agromet intelligence hub — and its growing agentic systems layer. GAP is going fully agentic. This role will drive that transition, building out the infrastructure, APIs, tooling, and interfaces that will enable AI agents, partner systems, and farmer-facing applications to reason over, act upon, and learn from the world’s best agricultural weather intelligence. You will work in close collaboration with our GIS and geospatial engineering partners to evolve GAP into a world-class agromet intelligence platform — one that powers farmer advisory services at scale across Africa. This is a full-stack role spanning backend platform engineering, API development, data architecture, frontend product development, and agentic AI systems design. It also carries product ownership — you will help define what gets built, not just how. We are looking for a senior software engineer who combines deep technical capability with product intuition and a genuine interest in the intersection of AI, geospatial systems, and agricultural impact. Remote-first, with preference for East Africa or European time zones.

Requirements

A bachelor’s degree (required) or MSc (advantageous) in one of the following or a closely related field: Computer Science, Software Engineering, or Computing Geoinformatics, Spatial Data Science, or Earth Observation with strong software engineering skills Data Engineering, Applied Mathematics, or Computational Science with a software specialization
5–8+ years of professional software engineering experience, with at least 3 years on production backend systems
Demonstrated experience building and scaling cloud-native APIs, data platforms, or geospatial systems in production
Experience with agentic AI frameworks, LLM tool use, or agentic tool-serving systems, or strong demonstrable interest and rapid self-upskilling in this area
Track record of owning full delivery cycles — from architecture through to deployed, production-quality software
Experience collaborating with external engineering partners or contractors on shared technical delivery
Strong Python proficiency — production-grade backend development using Django, FastAPI, or equivalent
Cloud-native data engineering: Zarr, Xarray, NetCDF, GeoTIFF, and large-scale raster/time-series pipelines
Relational and geospatial databases: PostgreSQL/PostGIS
REST API design, implementation, and documentation (OpenAPI/Swagger)
Experience with cloud infrastructure: GCP, AWS, or Azure; containerisation with Docker/Kubernetes
Experience with agentic tool-serving frameworks (e.g. Model Context Protocol) or equivalent LLM tool-use infrastructure
Experience building LLM-integrated applications: tool-use pipelines, agent orchestration, context management
Familiarity with Claude API, OpenAI API, or equivalent LLM APIs for building AI-powered applications
Ability to design robust, testable, well-scoped agent tools that function reliably as part of agentic workflows
Proficiency in modern frontend development: React, TypeScript, and associated tooling
Ability to build clean, functional data dashboards and partner-facing interfaces
Product instinct — comfort contributing to roadmap decisions, user experience trade-offs, and feature prioritisation
Git/GitHub: branching strategies, code review, CI/CD pipelines
Testing: pytest, unit/integration/e2e strategies, test automation
Technical documentation: architecture diagrams, API references, developer guides
AI-assisted development — fluent use of LLM coding assistants to accelerate delivery
Ownership-Driven: Takes full ownership of systems — from architecture through to production reliability. Doesn’t leave problems for others.
Full-Stack Breadth: Comfortable moving between backend data pipelines, API layers, frontend interfaces, and agentic AI systems without losing depth.
Product-Minded: Thinks about who uses the system and why — not just how it’s built. Naturally contributes to product decisions.
Collaborative: Works effectively with diverse technical partners (Kartoza, Predictia, TAHMO) and cross-functional colleagues (agromet scientists, program managers).
Clear Communicator: Produces documentation and architecture summaries that non-engineers can understand. Comfortable presenting technical systems to partner and donor audiences.
Adaptable & Curious: At ease in a fast-moving, mission-driven startup. Leans into new technologies — especially at the frontier of agentic AI.

Nice To Haves

Exposure to geospatial, weather, climate, or agricultural data systems is a significant advantage
Familiarity with geospatial processing libraries (GDAL, Rasterio, Shapely, GeoPandas) is an advantage
Experience with geospatial platforms, earth observation pipelines, or agricultural data systems
Familiarity with Icechunk, Zarr v3, or other cloud-native array storage systems
Working knowledge of weather or climate data formats and NWP/ML forecast products
Experience building or integrating with SMS, IVR, USSD, or low-bandwidth delivery channels
Prior exposure to the smallholder farmer advisory or climate services value chain in Africa
Experience developing in African technology contexts

Responsibilities

Own and extend the GAP backend architecture — APIs, data pipelines, processing infrastructure, and service integrations.
Design and implement scalable data ingestion and transformation pipelines for multi-source weather and climate products
Evolve the Zarr/Icechunk forecast data architecture for high-performance transactional reads, writes, and versioning.
Build and maintain robust REST and agent-facing APIs consumed by advisory engines, delivery partners, and AI agents
Contribute to platform DevOps, deployment infrastructure, and production reliability.
Implement security, authentication, and access control patterns appropriate for a multi-tenant partner platform
Lead the design and development of GAP’s agentic systems layer — building, testing, and deploying tools and services that enable AI agents to act on agromet intelligence
Architect agentic workflows that enable AI agents to autonomously query, reason over, and act on agromet intelligence
Implement Icechunk integration with the agentic layer for transactional, versioned forecast data access
Build agent orchestration patterns, tool-use pipelines, and context management for multi-step advisory generation
Ensure agentic systems are robust, well-documented, and extensible by partner engineering teams and LLM-based clients.
Stay at the frontier of agentic AI systems development and introduce new patterns as the field evolves.
Design and build frontend interfaces for GAP — dashboards, partner portals, advisory visualizations, and internal tools
Develop React-based (or equivalent) user interfaces that surface complex agromet intelligence in clear, actionable forms
Collaborate with the agromet science team to translate advisory outputs into well-designed, farmer-centric data products
Contribute to product definition — help shape the roadmap for what gets built, with a focus on partner and farmer impact.
Build and maintain API documentation, developer-facing tooling, and integration guides for partner teams.
Serve as the primary engineering counterpart on GAP agentic systems development and platform integration
Lead joint architecture and sprint planning sessions, code reviews, and technical design decisions with partner engineers.
Ensure shared systems meet TomorrowNow’s standards for quality, performance, and extensibility
Support technical scoping and delivery oversight for contracted development milestones
Interface with other external technical partners, including the validation dashboard and station networks
Implement comprehensive testing strategies — unit, integration, and end-to-end — for all platform components
Produce clear technical documentation for all systems: architecture diagrams, API references, integration guides, and runbooks
Monitor production systems, identify performance bottlenecks, and drive continuous improvement.
Contribute to internal knowledge sharing and technical mentoring across the engineering team.

Benefits

Competitive salary and benefits
Remote work environment with flexible hours
Opportunities for professional growth and development
A high-performance, collaborative, and mission-driven work culture
Entrepreneurial Environment: Join a mission-driven organisation where systems are evolving, initiative is valued, and your work directly shapes the product.

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