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.

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.