• Own the full ML lifecycle, including data ingestion, model training, validation, deployment, monitoring, retraining, and retirement. • Transition AI/ML models from prototypes into scalable, production-ready systems. • Build, deploy, and maintain CI/CD pipelines for ML models, ensuring reproducibility, scalability, and reliability. • Design and implement cloud-based infrastructure (AWS, Azure, or equivalent) for training, inference, and monitoring of AI models. • Automate repetitive ML lifecycle tasks to improve efficiency, consistency, and reliability in retraining and deployment workflows. • Integrate large language models (LLMs), generative AI, and NLP solutions into IMO Health’s Clinical AI products, focusing on unstructured clinical data. • Develop scalable inference pipelines and APIs to deliver AI capabilities to customer-facing solutions. • Apply containerization (Docker, Kubernetes) and Infrastructure-as-Code to manage production environments. • Implement monitoring, alerting, and performance dashboards to ensure model quality, detect drift, and maintain operational SLAs. • Optimize deployed models for latency, throughput, reliability, and cost efficiency. • Participate in system design and architecture discussions, providing expertise in MLOps and AI deployment best practices. • Collaborate in an Agile environment with cross-functional teams, aligning technical solutions with product and business goals.

• Own the full ML lifecycle, including data ingestion, training, validation, deployment, monitoring, retraining, and retirement. • Transition AI/ML prototypes into scalable, production-ready systems with CI/CD pipelines, automation, and observability. • Lead system design and architecture discussions, providing guidance on ML systems, MLOps, and AI infrastructure. • Develop and maintain AI-driven applications and inference services, optimizing for performance, scalability, reliability, and cost. • Integrate LLMs, generative AI, and NLP solutions into IMO Health products, focusing on unstructured clinical data. • Implement monitoring, alerting, logging, and dashboards to ensure model quality, detect drift, and maintain operational SLAs. • Build, maintain, and optimize CI/CD pipelines, automation scripts, and Infrastructure-as-Code for production ML systems. • Apply containerization (Docker, Kubernetes) and cloud infrastructure best practices to manage production environments. • Mentor and guide engineers, enforce technical standards, and drive reduction of technical debt. • Conduct root cause analysis of production defects and implement durable fixes. • Advocate for non-functional requirements (availability, scalability, reliability, maintainability) and design systems accordingly. • Collaborate cross-functionally with Product, Data Science, Architecture, and Engineering teams to align AI solutions with business goals.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking a skilled AI Developer to help modernise and optimise operations across the container and logistics ecosystem. You will build intelligent systems that enhance container tracking, route optimization, yard operations, demand forecasting, predictive maintenance, and supply chain visibility. This role focuses on applying AI to real-world shipping and containerized freight challenges — improving operational efficiency, reducing costs, and increasing transparency across global logistics networks. What You Will Be Doing: - Design and deploy AI/ML models for: - Container tracking and ETA prediction - Route and load optimization - Yard and terminal operations automation - Predictive maintenance for container fleets and equipment - Demand forecasting and capacity planning - Develop real-time analytics systems for supply chain monitoring - Integrate AI solutions with TMS (Transportation Management Systems), WMS (Warehouse Management Systems), and IoT devices - Work with telematics, GPS, RFID, and sensor data - Build APIs and scalable services to support operational platforms - Collaborate with operations, logistics planners, and engineering teams - Ensure data security and compliance across global shipping regulations - Optimise models for production-scale performance Qualifications - Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related field - 3+ years of experience in AI/ML development - Strong Python programming skills - Experience with machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) - Experience working with time-series data and large operational datasets - Knowledge of optimization algorithms (linear programming, heuristic optimization) - Experience with cloud platforms (AWS, Azure, or GCP) - Familiarity with REST APIs and microservices architecture - Understanding of MLOps and model deployment Requirements - Experience in logistics, shipping, freight, or container operations - Familiarity with supply chain analytics and transportation systems - Experience with IoT data processing - Knowledge of geospatial analytics (GIS tools, mapping APIs) - Experience building real-time streaming systems (Kafka, Spark, etc.) - Understanding of global trade operations and port logistics - Experience working in distributed, remote teams Benefits - Fully remote, flexible work environment - Competitive salary with performance incentives - Opportunity to work on AI systems impacting global trade and logistics - Access to large-scale operational datasets - Professional development and industry exposure - Collaborative, cross-functional international team Impact You’ll Make: - Improve container turnaround time - Reduce empty container repositioning costs - Increase supply chain visibility - Optimize port and yard efficiency - Support sustainable and data-driven logistics operations

• Work across multiple projects as a software developer within agile sprint-based delivery • Assist Technical Leads and Architects in the design of AI software solutions • Undertake .NET software development against assigned user stories to required quality standards • Quality assure developed AI solutions and rubber-stamp alpha testing and software releases • Research and evaluate existing and emerging Microsoft services and tools • Create prototypes and demonstrations to support solution development • Assist Support Engineers with debugging across development, UAT, and production environments • Produce technical reports, handover documentation, and transition artefacts • Maintain and repair existing applications where escalated • Support the Technical Team with continuous improvement of the Engineering function