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Introduction:

Model Development: Design and develop AI/ML models to optimize terminal operations, e.g. Dwell Time Prediction, Yard Planning and Usage Optimization, and Resource Allocation Automation (e.g. Cranes, Internal Tractors, etc).

Responsibilities:

Key Responsibilities

Model Development: Design and develop AI/ML models to optimize terminal operations, e.g. Dwell Time Prediction, Yard Planning and Usage Optimization, and Resource Allocation Automation (e.g. Cranes, Internal Tractors, etc).

Data Analysis: Collect, preprocess, and analyze large datasets from terminal operations to identify patterns and provide insights and recommendations.

Automation: Implement AI-driven solutions for automating processes such as berth allocation, crane job assignment, and traffic flow within the terminal.

Real-Time Systems: Develop and deploy real-time AI models for dynamic decision-making in terminal operations, such as Congestion Prediction and Shortest Route calculation.

Performance Monitoring: Monitor and evaluate the performance of deployed models, ensuring Accuracy, Scalability, and Reliability in production environment.

Research and Innovation: Stay updated on the latest advancements in AI/ML and propose innovative solutions to improve operational efficiency and sustainability.

Collaboration: Work closely with cross-functional teams, including data scientists, data engineers, software engineers, and operations managers, to integrate AI/ML solutions into existing systems.

Requirements:

Desired Expertise and Qualifications

Bachelors degree in an IT-related discipline. Data Science, Artificial Intelligence, Machine Learning, or a related field is a plus

6+ years of experience in developing and deploying AI/ML models in real-world applications

Experience with Time-Series data, CV (Computer Vision), NLP (Natural Language Processing and Prompt Engineering are a plus

Familiarity with logistics, supply chain, or terminal operations is highly desirable

Proficiency in Python, R, or similar programming languages

Strong experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn

Experience with specific models like LLM (Large Language Model), LSTM (Long Short-Term Memory), ARIMA (Autoregressive Integrated Moving Average), XGBoost, Random Forest, RL (Reinforcement Learning), GNNs (Graph Neural Networks), CNNs (Convolutional Neural Networks), ANNs (Artificial Neural Networks) or anomaly detection models (e.g. Autoencoders, Isolation Forests)

Knowledge of big data tools (e.g. Hadoop, Spark, Databricks) and cloud platforms (e.g. AWS, Azure, Google Cloud)

Experience with database systems (SQL, NoSQL) and data preprocessing techniques

Experience with IoT and sensor data integration is a plus

Strong problem-solving and analytical skills

Ability to work in a fast-paced, dynamic environment

Fluent in written and verbal English, demonstrated communications and stakeholder management skills