About this role
Job Description:
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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