Founding AI Architect – Spatiotemporal Intelligence

Founding AI Architect – Spatiotemporal Intelligence

Location:

Pune, India (On-site)

Type:
Full-Time | Founding Leadership Team

Company Overview

Dispatch Network is building the most efficient last-mile network in India from the ground up using technology and AI-powered optimization to drive efficiency and earnings for delivery partners. We operate across food delivery, quick commerce, grocery, ecommerce, and pharma.

Dispatch Network is building a logistics intelligence platform that learns and predicts how goods move through cities in real time. Our systems combine demand signals, fleet telemetry, geospatial context, and operational constraints into a continuously improving decision layer.

We are moving from pilot to national scale. The AI architecture built in this role will form the foundation of network behavior across multiple cities.

Role Overview

We are hiring a Founding AI Architect to design and lead the development of Dispatch’s spatiotemporal intelligence stack from the ground up.

This is a founding systems role — focused not on incremental modeling, but on creating novel forecasting, spatial reasoning, and decision intelligence architectures tailored to dense, real-world logistics environments.

You will own both the technical vision and the execution: building core models, production systems, and the team required to scale them.

Key Responsibilities:

Spatiotemporal Intelligence Architecture

- Design and build forecasting systems for demand, supply, rider availability, and network load
- Develop temporal and sequence models for high-resolution logistics prediction
- Architect spatial and spatiotemporal modeling frameworks across urban grids and networks
- Incorporate geospatial topology, mobility patterns, and behavioral signals into predictive systems
- Develop new modeling approaches where off-the-shelf methods fall short

Real-Time Decision Systems

- Power routing, assignment, and network balancing engines with predictive intelligence
- Design low-latency inference systems supporting live dispatch decisions
- Optimize utilization, fulfillment reliability, and idle distance through predictive inputs

Production ML & Infrastructure

- Build scalable training pipelines for large spatiotemporal datasets
- Establish retraining, evaluation, and drift detection systems
- Deploy and monitor models in high-availability production environments
- Integrate intelligence systems with APIs, services, and operational tooling

Team & Capability Building

- Hire and lead ML scientists and engineers
- Define modeling standards, research rigor, and development workflows
- Build internal tooling for experimentation, diagnostics, and performance analysis
- Mentor the team across modeling, systems design, and productionization

Strategic & Cross-Functional Leadership

- Define Dispatch’s AI roadmap across forecasting and spatial intelligence
- Translate real-world logistics constraints into solvable modeling systems
- Partner with product, engineering, and operations to deploy intelligence into live workflows
- Communicate system architecture, tradeoffs, and impact to founders and leadership

Required Experience:

- 6+ years building and deploying production ML systems
- Hands-on experience with temporal, spatial, or spatiotemporal modeling
- Proven track record shipping forecasting or optimization systems with real-world impact
- Experience building scalable training and inference pipelines
- Strong Python engineering and data systems fundamentals
- Exposure to MLOps: experiment tracking, model governance, monitoring

Technical Depth:

Strong candidates will have experience in multiple areas

- Time-series forecasting (deep learning and classical methods)
- Spatial modeling and geospatial indexing systems
- Graph-based or network prediction models
- High-resolution demand or mobility forecasting
- Large-scale telemetry or geospatial datasets
- Distributed training and low-latency inference systems

Leadership Requirements:

- Experience hiring or leading ML teams
- Ability to define technical direction in ambiguous problem spaces
- Strong collaboration across engineering, product, and operations
- Capability to own systems from research to production

Preferred Background:

- Logistics, mobility, or transportation systems
- Simulation or digital twin environments
- Operations research or applied optimization
- Urban computing or large-scale geospatial platforms

Role Mandate:

This role is responsible for inventing, building, and scaling Dispatch’s core intelligence layer.

Success will depend on the ability to:

- Develop novel modeling systems where industry playbooks do not exist
- Translate complex city dynamics into deployable ML architectures
- Build and lead a high-calibre team capable of scaling these systems nationally