The AI Engineer is responsible for designing, developing, and deploying production-grade Artificial Intelligence solutions. The role focuses on leveraging Generative AI (LLMs) and Machine Learning to solve complex logistics and freight management challenges. Beyond model development, this position requires a strong grasp of MLOps to ensure that AI models are scalable, monitored, and seamlessly integrated into the enterprise ecosystem via APIs and middleware.

MLOps & Infrastructure

• Deployment & Orchestration: Containerize AI services using Docker and manage large-scale deployments on
• Kubernetes clusters.
• Pipeline Automation: Build and maintain robust CI/CD pipelines for automated model testing, deployment, and versioning.
• System Integration: Collaborate with System Architects to integrate AI modules into internal software and third-party vendor systems (Accounting, GPS, Freight Management) via APIs and Enterprise Service Bus (ESB)architectures.

Data Strategy & Monitoring

• Vector Infrastructure: Design and manage Vector Databases (e.g., Pinecone, Milvus, or Weaviate) to support semantic search and GenAI capabilities.

Performance Monitoring: Develop telemetry dashboards to monitor model health, data drift, and hardware utilization(CPU/GPU/RAM).

• Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or a related field.
• 2+ years of professional experience in Software Engineering or AI/ML roles.
• Proven track record of deploying at least one AI-driven product into a production environment.

Language & Soft Skills:

• English: Professional proficiency in reading and writing technical documentation and research papers.
• Strong analytical mindset with a focus on solving real-world operational problems.
• Proactive attitude toward learning and implementing emerging AI technologies