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.

• Exposure to designing knowledge base solutions, such as:

  • Retrieval-Augmented Generation (RAG)

  • embedding-based search

  • hybrid search approaches

  • reranking or relevance evaluation

• Experience or academic background in one of the following:

  • fine-tuning small language models (SLMs)

  • training or adapting NLP models

  • Reinforcement learning concepts applied to language systems

• Exposure to event-driven or reactive systems

• Interest in supply chain domains (logistics, manufacturing, procurement, etc.).

• Knowledge of the life sciences supply chain is a plus, but not required.

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What Success Looks Like

• You can take a defined task (e.g., building a new RAG retriever, improving evaluation coverage, or implementing a new agent tool) and deliver a working solution with support from senior engineers.

• You write clean, testable code and steadily improve your ability to debug real-world production issues.

• You contribute to AI system reliability through experiments, evaluation improvements, and thoughtful engineering.

Who You Are

• Curious, motivated, and excited to build AI-driven products that ship to real users.

• Comfortable working with a mix of predictable engineering tasks and emerging AI workflows.

• Strong team player with a growth mindset and a willingness to learn.