IIAD / SAD / LAD Artificial Intelligence Engineer

Our client is an American multinational shipping & receiving and supply chain management company founded in 1907. Operating in more than 220 countries and territories, they are committed to moving the world forward by delivering what matters. Beginning as a small messenger service and a $100 loan, they now have more than 500,000 users around the globe. They are a transportation and logistics leader, offering innovative solutions to customers, big and small. They understand and appreciate their responsibility to help build safer, stronger and more resilient communities founded on justice and economic opportunity for all, supported by a healthy, sustainable global environment.

Responsibilities:

Agentic AI Development:  

• Design, build, and deploy agentic AI systems using frameworks such as LangChain, Lang Graph, and related libraries.  
• Develop and deploy multi-agent systems capable of autonomous decision-making, reasoning, planning, and collaboration. 

RAG Pipelines: 

• Implement and optimize retrieval-augmented generation (RAG) systems, ensuring agents can access and incorporate external knowledge sources for grounded, accurate responses. 

LLM Engineering: 

• Fine-tune and prompt-engineer LLMs for task-specific reasoning, planning, and dynamic adaptation. 
• Work with LLM/SLM APIs, embeddings, and advanced generative AI techniques. 

Enterprise AI Platform: 

• Lead the development of enterprise-grade AI platforms integrating LLMs, RAG, embeddings, and agentic AI protocols. 
• Implement and standardize Model Context Protocol (MCP) for consistent context management across models and agents. 

MLOps & Observability: 

• Establish and enforce best practices for MLOps, monitoring, and observability, ensuring scalable and maintainable AI solutions. 
• Applied AI Prototyping: 

Rapidly prototype, experiment, and iterate to improve AI agent capabilities. 

Collaboration & Research: 

• Participate in the full research cycle: literature review, data exploration, experimentation, and presentation of findings. 
• Collaborate effectively with other engineers, researchers, and data scientists. 
• Contribute to the documentation and standardization of technical code and practices. 

Requirements:

• Bachelor’s degree in Computer Science, Engineering, or a related quantitative field. Master’s or Ph.D. is a strong plus. 
• 5+ years overall experience in software development, data science, or machine learning. 
• 1+ year of hands-on experience developing AI applications with LLMs and systems such as retrieval-based methods, fine-tuning, or agent-based architectures. 
• 1+ year of experience with frameworks like LangChain, LlamaIndex, OpenAI, or similar tools.