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, youll 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 :

- Masters degree in Data Science, Artificial Intelligence, Machine Learning, Computer Science, or a closely related discipline.

- 1 to 3 years of professional experience in software engineering, AI engineering, or ML engineering (internships and co-ops count).

- 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 :

a. APIs and services

b. basic distributed systems concepts

c. debugging and performance basics

d. 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 :

a. Retrieval-Augmented Generation (RAG)

b. embedding-based search

c. hybrid search approaches

d. reranking or relevance evaluation

- Experience or academic background in one of the following :

a. fine-tuning small language models (SLMs)

b. 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.