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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