Founding AI/ML Architect

About the Company

This organisation is a logistics intelligence company building an adaptive, real time platform that learns city dynamics and optimises how goods move through dense urban environments.

They have built a core orchestration layer that powers forecasting, routing intelligence, and real time fleet decision making at national scale.

The company is moving from pilot to large scale rollout across India and serves high velocity delivery networks.

They also invest heavily in spatiotemporal research, simulation modeling, and next generation mobility stack development.

Why Explore a Career Here

This is a high impact role where you will be working on:

• Building India’s first deeply integrated spatiotemporal forecasting engine
• Designing intelligence that optimises millions of fleet decisions per day
• Leading the AI roadmap for nationwide logistics automation

If you are looking for a fast paced environment with ownership and strong learning, this could be a great fit.

Responsibilities

• Design and implement advanced demand, supply, load, and rider forecasting systems
• Build temporal models including LSTMs, Transformers, and diffusion based predictors
• Develop spatial and spatiotemporal architectures such as GNNs, hex grid models, and mobility prediction systems
• Build scalable ML training pipelines for massive temporal and geospatial datasets
• Deploy low latency inference systems powering real time decisions
• Establish model lifecycle workflows including drift detection, versioning, and automated improvement
• Lead the AI roadmap and mentor the ML team while translating complex logistics problems into solvable ML architectures

Requirements

• 5 plus years of experience building and deploying ML systems in production
• Strong proficiency in deep learning, classical ML, forecasting, and temporal modeling
• Hands on experience with GNNs, spatial modeling, mobility intelligence, or geospatial ML
• Exposure to large scale time series pipelines or operations research
• Understanding of scalability, distributed systems, and real time inference performance
• Bonus: simulation modeling, digital twins, network optimisations, open source ML contributions

Preferred Skills

• Strong Python engineering fundamentals
• Experience with cloud platforms, microservices, and distributed systems
• Familiarity with MLOps tools such as experiment tracking, CI/CD, monitoring, and lineage
• Ability to translate business constraints into effective modeling strategies
• Experience working with large operational or mobility datasets