AI Last-Mile Logistics Optimisation Coordinator

Santriya Technologies; Chennai; On-site; INR 800,000 - 1,100,000; Full time

Job summary

Santriya Technologies Ltd. seeks an AI Last-Mile Logistics Optimisation Coordinator to coordinate last-mile capacity, routing and delivery exceptions using AI optimization and geospatial intelligence with human oversight for local constraints and customer impact.

The role focuses on planning delivery waves, validating routes, and overseeing performance and exceptions to ensure on-time delivery. The position emphasizes collaboration with operations and uses AI copilots and geospatial tools to

Responsibilities

• Plan delivery waves, route capacity and carrier/driver allocation against promised windows.
• Use AI routing, geocoding and ETA tools while validating access, restrictions, weather and local operating realities.
• Monitor route progress and intervene on capacity, failed-delivery or service-risk exceptions.
• Coordinate customer communication and reattempt/re-route decisions.
• Track driver/carrier performance, utilization, distance and cost.
• Investigate address, geocode, packaging or process causes of failed delivery.
• Support peak planning and scenario testing.
• Escalate safety, security, high-value or regulated deliveries per policy.

Skills

Delivery planning

Route optimization

AI routing

Geospatial analysis

Customer communication

Job description

Job Summary

Santriya Technologies is seeking an AI Last-Mile Logistics Optimisation Coordinator to coordinate last-mile capacity, routing and delivery exceptions using AI optimization and geospatial intelligence with human oversight for local constraints and customer impact.

Product & Documentation Operating Model

Product & documentation operating model: You will be assigned one or more company products, client solutions or operational services. The approved product documentation, SOPs, policies, process maps, knowledge articles, release notes, controls, scripts and AI runbooks are the source of truth. Use only approved enterprise AI tools to retrieve, summarize, draft, classify, prioritize or automate work. Validate material AI outputs against current documentation and authoritative system data, record exceptions, and elevate conflicts or missing guidance. Do not invent product features, prices, customer entitlements, engineering limits, regulatory positions or safety instructions.

Key Responsibilities

• Plan delivery waves, route capacity and carrier/driver allocation against promised windows.
• Use AI routing, geocoding and ETA tools while validating access, restrictions, weather and local operating realities.
• Monitor route progress and intervene on capacity, failed-delivery or service-risk exceptions.
• Coordinate customer communication and reattempt/re-route decisions.
• Track driver/carrier performance, utilization, distance and cost.
• Investigate address, geocode, packaging or process causes of failed delivery.
• Support peak planning and scenario testing.
• Escalate safety, security, high-value or regulated deliveries per policy.

AI-enabled ways of working

• Use approved GenAI copilots, enterprise search/RAG, analytics and workflow agents to reduce repetitive work and improve decision preparation.
• Keep prompts, outputs and automated actions within approved data-access, confidentiality and retention rules.
• Correct inaccurate summaries, classifications or recommendations before they become customer, operational or system records.
• Feed recurring AI errors, knowledge gaps and process friction into product, documentation and control improvement.

Human accountability

• AI is a copilot, not the accountable decision-maker. The role holder remains responsible for judgement, quality, privacy, security and appropriate escalation.
• Credit, fraud blocking, legal/regulatory, financial advice, safety-critical, clinical, employment and other high-impact decisions must follow delegated authority and required human review.

Success Measures

• On-time delivery and ETA accuracy improve
• Miles/cost per stop reduce
• Failed deliveries decline
• Peak capacity decisions become more proactive