(JD) for an AI Professional/Specialist in the logistics industry:

Job Title: AI Professional / AI Specialist Logistics & Supply Chain

Job Summary The AI Qualified / AI Specialist will be responsible for designing, developing, implementing, and maintaining Artificial Intelligence and Machine Learning solutions that improve logistics and supply-chain operations.

The role will focus on applying AI to areas such as demand forecasting, route optimization, fleet management, warehouse operations, inventory optimization, shipment tracking, delivery-time prediction, customer service, fraud detection, and operational automation.

The ideal candidate should combine strong AI/ML expertise with a practical understanding of logistics, transportation, warehousing, and supply-chain processes.

Key Responsibilities

1. AI & Machine Learning Solutions

- Develop and deploy machine-learning and AI models for logistics and supply-chain applications.
- Build predictive models for demand, shipment volumes, delivery times, delays, and capacity requirements.
- Apply generative AI and Large Language Models (LLMs) to automate operational and customer-service processes.
- Develop AI-based decision-support systems for logistics managers and operations teams.
- Continuously evaluate and improve model accuracy, reliability, and business impact.

1. Route & Transportation Optimization

- Develop AI/ML solutions for route planning and optimization.
- Optimize vehicle allocation, delivery schedules, load planning, and transportation capacity.
- Predict traffic-related delays and recommend alternative routes.
- Develop Estimated Time of Arrival (ETA) prediction models.
- Support dynamic dispatching and real-time transportation decision-making.

1. Warehouse & Inventory Optimization

- Apply AI to warehouse demand planning, inventory optimization, and replenishment.
- Develop solutions for warehouse slotting and picking optimization.
- Use computer vision for package identification, damage detection, barcode recognition, and warehouse monitoring.
- Identify operational bottlenecks and recommend AI-driven improvements.

1. Data Analytics & Forecasting

- Analyze large datasets from ERP, TMS, WMS, GPS, IoT, telematics, and other logistics systems.
- Develop forecasting models for demand, inventory, fleet utilization, and shipment volumes.




- Identify trends, anomalies, inefficiencies, and operational risks.
- Build dashboards and analytical models to support management decisions.

1. Generative AI & Automation

- Develop AI assistants/copilots for logistics planners, customer-service teams, warehouse staff, and management.
- Automate document processing such as invoices, bills of lading, proof of delivery, shipping documents, and customs documentation.
- Implement AI-based email, chatbot, and customer-query automation.
- Use NLP to extract information from unstructured logistics documents and communications.

1. AI Implementation & Integration

- Integrate AI solutions with existing ERP, WMS, TMS, CRM, fleet-management, and IoT platforms.
- Work with software engineers and IT teams to deploy AI models into production.
- Develop APIs and data pipelines required for AI applications.
- Monitor deployed models and resolve performance or data-quality issues.

1. Governance, Security & Responsible AI

- Ensure AI solutions comply with organizational security, privacy, and governance requirements.
- Establish appropriate controls for AI-generated decisions and recommendations.
- Monitor model bias, accuracy, explainability, and reliability.
- Maintain documentation of models, datasets, assumptions, and performance metrics.

1. Business Collaboration

- Work closely with logistics, transportation, warehouse, procurement, finance, sales, and customer-service teams.
- Identify business problems that can be solved through AI.
- Translate operational requirements into AI/ML use cases.
- Present AI insights and recommendations to senior management in business-friendly language.

Supplier and carrier performance analytics

Required Technical Skills

- Python and/or R
- SQL
- Machine Learning and Deep Learning
- Generative AI and LLMs
- Natural Language Processing (NLP)
- Computer Vision, where applicable
- Time-series forecasting
- Optimization algorithms




- Data preprocessing and feature engineering
- Model evaluation and deployment
- REST APIs and system integration
- Cloud AI/ML platforms
- Data visualization and analytics
- MLOps and model monitoring
- Familiarity with databases, data warehouses, and data pipelines

Preferred AI/Technology Knowledge Experience with technologies such as:
- TensorFlow / PyTorch
- Scikit-learn
- Pandas / NumPy
- Hugging Face
- LLM APIs and AI platforms
- Vector databases and RAG architectures
- Docker and Kubernetes
- Git and CI/CD
- Cloud platforms such as AWS, Microsoft Azure, or Google Cloud
- Power BI, Tableau, or equivalent analytics platforms

Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, Engineering, Statistics, Mathematics, or a related field.
- Relevant professional experience in AI/ML, data science, analytics, or technology.
- Experience applying AI to real-world business or operational problems.
- Logistics or supply-chain experience is an advantage.

Key Performance Indicators (KPIs) Performance may be measured through:
- Reduction in transportation costs
- Improvement in delivery-time accuracy
- Reduction in late deliveries
- Improvement in vehicle/fleet utilization
- Reduction in fuel consumption
- Improvement in warehouse productivity
- Inventory reduction/optimization
- Forecast accuracy
- Reduction in manual processing
- AI model accuracy and reliability
- AI solution adoption by business users
- Return on investment (ROI) from AI initiatives

Key Competencies
- Analytical and problem-solving ability
- Strong understanding of AI/ML concepts
- Business and commercial awareness
- Logistics and supply-chain understanding
- Data-driven decision-making
- Innovation and continuous improvement
- Communication and stakeholder management
- Project management
- Ability to work with cross-functional teams
- Ability to convert business problems into practical AI solutions

Overall Objective The primary objective of the AI Professional / AI Specialist is to use artificial intelligence, machine learning, data analytics, and automation to make logistics operations faster, more efficient, predictable, cost-effective, and customer-centric.

📌 Artificial Intelligence Specialist (New Delhi)
🏢 Tiranga Logistics
📍 New Delhi