We are seeking an energetic and technically sharp Junior Forward Deployed AI Engineer to support the rapid prototyping, discovery, and proof-of-concept (PoC) delivery of AI-powered solutions across our global Supply Chain and Manufacturing functions.

This is a dynamic, fast-paced role where you will work alongside senior engineers to bridge the gap between AI development and real-world shop floor operations. You will spend time embedding directly with frontline teams—visiting manufacturing sites and warehouses—to understand their day-to-day challenges, and then turn those insights into working, AI-enabled software prototypes.

The ideal candidate is a proactive builder who is comfortable working with Python, eager to learn cutting-edge GenAI frameworks, and excited about collaborating directly with operational business teams.

Key Responsibilities

Frontline Engagement & Discovery

• Frontline Shadowing: Participate in site visits and shadow frontline teams (Logistics, Manufacturing, Procurement) to understand operational bottlenecks first-hand.

• Requirements Translation: Support senior engineers in translating user feedback and "messy" real-world problems into structured user stories, functional specs, and process flows.

• Active Scrum Participant: Maintain and update the team's agile backlog, actively participate in daily standups, and assist in coordinating sprint planning.

Rapid AI Prototyping & Development

• Hands-on Coding: Write clean, documented, and functional Python code to build, refine, and test AI-enabled prototypes.

• Support RAG & Agentic Systems: Work under the guidance of senior engineers to construct and tune Retrieval-Augmented Generation (RAG) pipelines, semantic search indices, and multi-agent system workflows.

• Iterate & Refine: Actively modify prototypes based on immediate feedback from factory floor workers and warehouse operators.

• Adopt Best Practices: Learn and apply enterprise software development standards to ensure prototype code is structured for eventual production handoff.

Systems Integration & Data Engineering

• API Configuration: Assist in building and configuring APIs to connect AI applications to shop floor platforms (e.g., Poka, Weaver) and document repositories.

• Data Wrangling: Clean, structure, and prepare unstructured documents, telemetry feeds, and SQL database exports for ingestion into AI systems.

Typical Use Cases

• Standard Operating Procedure (SOP) Assistants designed to help operators look up technical guidelines hands-free.

• Manufacturing Knowledge Assistants that index equipment manuals to speed up machine maintenance.

• Procurement Intelligence Tools that extract and summarize key terms from supplier contracts.

• Workflow Automation Tools to help administrative logistics teams auto-generate shift handover reports.

Career Growth:

This role will be an excellent fit for someone who:

• Enjoys solving varied, real-world problems.

• Likes interacting with customers and understanding business needs.

• Wants to work on cutting-edge AI applications rather than purely research.

• Thrives in fast-paced environments where you own projects from design to deployment.

Required Qualifications

• Education: Bachelor’s degree in Computer Science, Data Science, Software Engineering, or a related technical discipline.

• Experience: 1–3 years of professional experience in software engineering, data engineering, or digital technology delivery.

• Coding Foundation: Robust baseline experience writing Python (Pandas, NumPy, requests) and a strong understanding of software engineering fundamentals (OOP, REST APIs).

• AI Familiarity: Hands-on experience (which can include academic projects, bootcamps, or personal portfolios) using LLM APIs (OpenAI, Gemini) and basic RAG architectures.

• Data Skills: Proficient with SQL and comfortable querying relational databases.

• Soft Skills & Mobility: Excellent communication skills with the confidence to converse with factory operators. Highly curious, eager to learn, and willing to travel occasionally to manufacturing facilities.

Preferred Qualifications

• Basic experience with AI frameworks such as LangChain, LlamaIndex, or LangGraph.

• Familiarity with cloud platforms (e.g., Google Cloud Platform/Vertex AI, Microsoft Azure).

• Familiarity with version control (Git/GitHub) and containerization (Docker).

• Academic background or brief exposure to Supply Chain, Manufacturing, or Logistics environments.

Success Measures

• Technical Growth: Rapid ramp-up on advanced enterprise AI architectures and frameworks (e.g., LangGraph, Vertex AI).

• Prototype Delivery Velocity: Successfully completing assigned development tasks within the sprint cycle.

• User-Centric Execution: Translating frontline operator feedback into functional code modifications.

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