AI Implementation and Enablement Lead

Qualifications

Degree in Computer Science with Generative AI, Large Language Models (LLMs), prompt engineering, and Retrieval - Augmented Generation (RAG). Working proficiency in Python and SQL, alongside API integration (FastAPI/Flask) and vector databases. Exposure to major cloud platforms like AWS, Microsoft Azure, or Google Cloud and containerization tools like Docker.

Experience

3 to 8 years of practical experience in digital transformation, technical product deployment, or change management. At least two AI/ML or automation solutions taken to live production use.

Skills

• Technical AI Proficiency
• Change Management Enablement
• Governance, Risk, Compliance (GRC)
• Product Project Management
• Hands-on SAP data extraction and integration - CDS views, OData services, BAPI/RFC, or SAP HANA / Datasphere / BW; comfortable navigating PP, QM, MM, PM and FICO data structures
• Strong Python, SQL, pandas, scikit-learn; experience with at least one deep learning framework
• Practical LLM and RAG experience - prompt design, evaluation, cost control, and clear-eyed about failure modes
• Cloud5 deployment (AWS/Azure/GCP or SAP BTP), APIs, Docker, basic MLOps and monitoring
• Demonstrated ability to train and carry non-technical users - you will spend real time on the shop floor and in the accounts department

Job Responsibilities

• Identify and build: Run structured discovery with foundry operations, quality, sales and finance to build a prioritised use-case pipeline, sized by effort and expected return
• Deliver solutions across the business: Foundry operations quality - Rejection and defect analysis - correlating porosity, shrinkage, cold shut, inclusions and blowholes against furnace, moulding, sand and pouring parameters; Charge mix optimisation against live scrap and ferroalloy prices, within chemistry limits; Melt and energy analytics - power consumption per tonne, tap-to-tap time, furnace efficiency; Sand plant analytics using sand lab test data (GCS, compactability, moisture, LOI); Spectrometer and heat-record analysis for chemistry drift; Vision-based surface inspection at fettling, or AI-assisted review of radiography/MPI results; Predictive maintenance on induction furnaces, moulding lines, compressors and cranes; Yield and OEE analysis by pattern, grade and line
• Sales: Demand forecasting by customer and casting; enquiry-to-order conversion analysis; Casting cost estimation and quotation support - a major time sink in job-order foundries; Customer schedule variance and delivery-risk prediction
• Finance Accounting: Vendor invoice and PO data extraction, three-way match automation; Ledger and bank reconciliation, duplicate and anomaly detection; Receivables prioritisation and collections risk scoring; Automated MIS and management reporting from SAP data
• Enable the organisation: Run practical, role-specific AI training for staff across functions - what these tools do well, where they fail, and how to use them without creating risk; Build internal assistants and prompt libraries for repetitive work: drafting, summarising, report writing, data lookup; Publish and enforce a simple internal AI usage policy covering confidential drawings, costings, customer data and financial records; Track adoption and act as internal point of contact for AI questions
• Own the plumbing and the governance: Extract and model data from SAP alongside plant-floor, lab and spreadsheet sources; Evaluate tools and vendors; make honest build vs. buy calls; Monitor deployed models, and ensure human review on anything touching financial, quality-certification or compliance output

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