Job Description:
We are seeking a high-caliber Supply Chain & Manufacturing Forward Deployed AI Engineer to lead rapid prototyping, discovery, and proof-of-concept (PoC) delivery. In this role, the incumbent will actively de-risk the design, development, integration, testing, and delivery of production-ready, AI-powered solutions across our global Supply Chain and Manufacturing functions.
This is a unique, highly impactful hybrid role, combining agile software delivery, frontline business engagement, hands-on AI product engineering, and modern AI/ML systems architecture. The ideal candidate will embed directly with frontline operations at the point of solution consumption to intimately understand operational bottlenecks, translate them into structured technical requirements, and write the critical lines of code that drive tangible business value.
The ideal candidate is equally comfortable facilitating workshops on the manufacturing floor, managing rapid agile delivery cycles, designing technical architecture, and writing robust, clean Python code.
Key Responsibilities
Frontline Engagement & Discovery
On-Site Discovery: Partner directly with frontline Supply Chain, Manufacturing, Logistics, Procurement, and Planning teams to intimately map out operational bottlenecks
Translate Ambiguity: Facilitate discovery workshops and process reviews to translate messy business problems into clean user stories, functional specs, and technical data models
Own the Agile Lifecycle: Act as the Scrum Master for your prototyping work, managing backlog prioritization, rapid feedback iterations, risks, dependencies, and assumptions.
Rapid AI Prototyping & Development
Code & Deploy: Design, write, and deploy robust working prototypes using Python and modern cloud technologies
Build Advanced AI Architectures: Construct enterprise-grade Retrieval-Augmented Generation (RAG) pipelines, semantic search engines, and multi-agent system workflows
Design for Scale: Focus on engineering prototypes that are "designed for scale" to ensure seamless code transition and handoff to core engineering teams
Reusable Tooling: Build reusable AI components, libraries, and accelerators to streamline future supply chain use cases
Systems Integration & Data Engineering
Platform Connections: Design and build API integrations connecting AI applications directly to shop-floor platforms (e.g., Poka, Weaver, MES)
Enterprise Infrastructure: Integrate AI systems with core enterprise platforms (SAP ERP), document repositories, and knowledge bases
Data Pipelines: Ingest, clean, structure, and connect messy, real-world data from databases, telemetry streams, and unstructured files
Cross-Functional Security: Partner with Security, Data, and Architecture teams to ensure robust, compliant, and secure integration patterns.
Typical Use Cases
Intelligent Manufacturing Knowledge Assistants integrated with platforms like Poka and Weaver to assist line operators.
AI-Powered Shopfloor Voice Assistants & hands-free Standard Operating Procedure (SOP) guidance.
Supplier & Procurement Intelligence Solutions utilizing agentic workflows to parse contracts and market data.
Predictive Decision Support Systems for complex Supply Chain Planning and Logistics network optimization.
Multi-Agent Digital Workers automating complex document analysis, compliance checks, and operational reporting.
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, Engineering, Information Systems, Supply Chain, Manufacturing, or a highly quantitative field.
Experience: 5+ years of professional experience delivering software, advanced analytics, or digital transformation initiatives with at least 2+ years of hands-on experience building GenAI solutions.
Domain Expertise: Experience working inside Supply Chain, Manufacturing, Operations, Logistics, or Industrial environments
Core Software & AI Stack:
Production-grade Python and clean coding practices
Hands-on experience with modern AI/ML tooling: LLM APIs (GPT-4, Gemini, Claude), vector databases, and frameworks like LangChain, LangGraph, or LlamaIndex
Experience with cloud platforms (e.g., Azure OpenAI, Google Cloud/Vertex AI)
Core Data Skills: Strong SQL skills with the ability to query, manipulate, and validate complex enterprise datasets
Delivery & Soft Skills: Superb communication and facilitation skills. Comfortable walking a manufacturing floor, gaining trust from operators, and presenting technical architectures to executive leadership
Preferred Qualifications
Hands-on experience with enterprise ERP systems (SAP) and industrial systems (Poka, Weaver, MES)
Experience with AI Evaluation and Guardrail frameworks (e.g., Ragas, TruLens, Phoenix) to systematically evaluate model outputs
Strong engineering discipline: familiarity with version control (Git/GitHub), containerization (Docker), and basic CI/CD workflows
Experience with data pipelines/analytics platforms (e.g., Snowflake, Databricks, Apache Spark, Airflow)
Success Measures
Velocity & Outcome Ownership: Swiftly converting ambiguous user pain points into functional, de-risked AI prototypes
High Adoption & Smooth Handoff: High transition rate of validated prototype code to core engineering teams for full-scale production
Measurable Business Value: Delivering clear, quantifiable improvements to operational metrics (e.g., reduction in line downtime, faster SOP lookups, improved logistics query speed).
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