Senior Software Engineer - Artificial Intelligence/Machine Learning

The ideal candidate will have extensive expertise in Python and leading AI/ML libraries (e.g, TensorFlow, PyTorch, Scikit-learn) and will be adept at open-source optimization (e.g, Pyomo, OR-Tools) to handle complex scheduling, routing, and resource allocation tasks.

Data engineering proficiency-including Apache Spark, Hadoop, and Kafka-is paramount for building and managing data lakes that store both structured and unstructured data, supporting real-time data processing and analytics.

This individual should also have experience deploying AI solutions in on-premises (Docker, Kubernetes) and cloud environments (AWS, GCP) with strong knowledge of DevOps (CI/CD, Git), security, and performance optimization.

Familiarity with LLM and NLP frameworks such as SpaCy and NLTK is critical for developing Generative AI, chatbots, and automated customer service systems.

Ultimately, expertise in a broad range of data engineering and AI technologies is essential for driving innovation and optimizing planning processes within the logistics industry, particularly in port and terminal operations.

Principal Responsibilities

• Develop multi-agent AI frameworks geared toward container terminal operations and large-scale logistics optimization.
• Design and implement end-to-end ML pipelines, including data ingestion, feature engineering, and real-time model deployment.
• Optimize machine learning and deep learning algorithms for scheduling, resource allocation, and route planning in port environments.
• Research and evaluate emerging AI techniques
• Collaborate with cross-functional teams to integrate AI solutions seamlessly into existing operational and IT infrastructures.
• Build and maintain robust data pipelines (data lakes, streaming frameworks) using Spark, Hadoop, and Kafka for large-scale data handling.
• Deploy AI solutions on both cloud platforms (AWS, GCP) and on-premises infrastructures, ensuring reliability, security, and compliance in high-volume environments.
• Implement and fine-tune optimization models and solvers (OR TOOLS, COIN OR, SCIP etc.)
• Conduct in-depth research and prototyping in reinforcement learning, agent-based modelling, and specialized ML techniques.
• Apply reverse engineering methods to debug, interpret, and enhance existing ML models for continuous performance gains.
• Monitor and report key metrics (KPIs, ROI) to assess the effectiveness of AI-driven solutions in real-world operations.
• Enforce ethical and transparent AI usage by adhering to governance frameworks, version control, and regulatory guidelines.
• Contribute to industry events through technical presentations, research publications, and active knowledge sharing.
• Mentor and support developers on best practices for coding, model design, and efficient use of AI libraries.
• Use lean principles to streamline development processes, reduce technical debt, and boost solution maintainability.
• Integrate NLP and LLMs for customer service, documentation automation, and large-scale text analysis in logistics.
• Collaborate on system integrations to ensure end-to-end functionality of AI modules across port operations.
• Enable real-time intelligence for vessel scheduling, yard operations, and gate control using streaming data and on-the-fly analytics.
• Maintain comprehensive domain knowledge of port and terminal terminologies to align AI solutions with operational realities.
• Contribute technical insights to the organization's digital transformation roadmap, supporting strategic AI initiatives.
• Align AI R&D initiatives with the organization's strategic vision, monitoring long-term impacts of AI-driven innovations, and influencing the overall digital transformation roadmap in the port and shipping sector.
  
  

(ref:hirist.tech)