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 transparent-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

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 AI Implementation and Enablement Lead (Kolkata)
🏢 STRIDE INDIA
📍 Kolkata