Company overview: TraceLink is the world’s largest Agentic Business Network, enabling life sciences and healthcare companies to build and manage a scalable digital workforce of governed, no-code AI agents that execute and coordinate mission-critical supply chain operations alongside human teams. Powered by the Integrate-Once™ OPUS platform, TraceLink links more than 300,000 network participants, enabling multi-enterprise processes at global scale. Founded in 2009 with the simple mission of protecting patients, today Tracelink has 5 global offices, over 800 employees and more than 1700 customers in over 60 countries around the world.

Our expanding product suite continues to protect patients and now also enhances multi-enterprise collaboration through innovative new applications such as MINT. Tracelink is recognized as an industry leader by Gartner and IDC, and for having a great company culture by Comparably.

About The Role

We are looking for an early-career Agentic AI Engineer to help build and evolve AI-powered systems that automate and improve supply chain workflows. In this role, you’ll work alongside experienced engineers and data scientists to develop agentic AI / GenAI features , integrate knowledge-based retrieval (RAG) patterns, and contribute to testing and validation approaches for AI systems that can behave in non-deterministic ways. This is a strong opportunity for someone who is eager to grow in both software engineering and applied GenAI , and wants to work on real-world enterprise problems in supply chain and (optionally) life sciences.

Key Responsibilities

Support the design and implementation of agentic AI / GenAI systems that assist in automating supply chain workflows. Build and maintain backend services and integrations using Python and/or Java . Contribute to multi-agent workflows , such as tool execution, routing, agent collaboration patterns, and task orchestration.

Assist in creating testing and validation strategies for AI systems, including evaluation datasets, regression testing, and behavior monitoring. Help implement and improve knowledge base systems , including RAG pipelines , grounding strategies, and retrieval quality improvements. Contribute to experimentation with: lightweight fine-tuning approaches for small language models (SLMs) reinforcement-learning-inspired improvement loops for NLP/GenAI tasks (where applicable) Partner with product and domain teams to understand supply chain needs and translate them into working software.

Participate in code reviews, documentation, and operational support to ensure high-quality production systems.

Required Qualifications

Master’s/Bachelors degree in Data Science, Artificial Intelligence, Machine Learning, Computer Science, or a closely related discipline. 0–2 years of professional experience in software engineering, AI engineering, or ML engineering (internships and co-ops count) OR equivalent experience Strong programming skills in Python and/or Java , including writing production-quality code. Familiarity with cloud platforms such as AWS, GCP, or Azure (academic, personal, or internship experience is acceptable).

Interest or exposure to Generative AI concepts, such as LLMs, agent workflows, tool calling, or multi-step reasoning. Understanding of core engineering fundamentals: APIs and services basic distributed systems concepts debugging and performance basics data structures algorithms Ability to learn quickly, take feedback well, and collaborate effectively in a team environment.

Preferred Qualifications

Coursework, projects, or hands-on experience with agentic or multi-step AI systems , including non-deterministic behavior patterns.