AI Implementation and Enablement Lead-Kolkata -
Qualification
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 cloud5 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 Required
• 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
• Ability to turn a vague operational complaint into a defined, measurable problem
Pls. forward resumes to:
stride.india.hr@gmail.com
Contact: 9830715233/
9830411678/
033 2424 0073
Job Resposibility
1. 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
• Build proof-of-concepts fast, discard what doesn't work, and take the rest to live production use
2. 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
3. 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
4. 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.
CTC
As per industry standard
