About the role

Job Title: Lead AI Engineer – Supply Chain Transformation

Role Overview

We are seeking a high-impact AI Specialist to spearhead the integration of Artificial Intelligence across our global supply chain. This is not just a "model-building" role; you will be the architect of an autonomous, self-healing supply chain. You will work across modules—from Procurement and Demand Planning to Logistics and Warehouse Management —to convert massive data streams into predictive foresight and automated execution.

Key Responsibilities (Cross-Module)

Demand Forecasting & Sensing: Develop multi-factor deep learning models (Transformers, LSTMs) that ingest traditional sales data plus external signals like weather, social trends, and geopolitical shifts to reduce "forecast bias."
Inventory & Warehouse Optimization: Implement Reinforcement Learning (RL) for dynamic slotting and safety stock optimization. Use Computer Vision (CV) to automate stock counting and quality inspections.
Logistics & Route Orchestration: Build real-time optimization engines for last-mile delivery and carrier selection, factoring in live traffic, fuel costs, and carbon footprint targets.
Strategic Sourcing & Procurement: Deploy Generative AI (LLM-based) agents to automate supplier risk assessments, contract analysis, and price negotiation simulations.
End-to-End Visibility: Create a "Digital Twin" of the supply chain to run "what-if" simulations, allowing the business to stress-test resilience against global disruptions.

Technical Skillset

1. AI & Machine Learning Core

Frameworks: Mastery of PyTorch or TensorFlow ; proficiency in Scikit-learn for baseline modeling.
Generative AI: Experience with LLMs (GPT-4, Claude, Llama 3) , Retrieval-Augmented Generation ( RAG ), and Agentic AI frameworks (e.g., LangGraph, AutoGen) for autonomous task execution.
Optimization: Strong grasp of Linear Programming (LP), Mixed-Integer Programming (MIP), and heuristic solvers (Gurobi, OR-Tools).

2. Data Engineering & MLOps

Languages: Expert-level Python ; familiarity with SQL and Java/C++ for production-grade systems.
Cloud Ecosystems: Hands-on experience with AWS SageMaker , Azure AI , or Google Vertex AI .
Deployment: Containerization via Docker/Kubernetes and building CI/CD pipelines for ML (MLflow, Kubeflow).

3. Domain Specifics

IoT & Edge AI: Ability to integrate data from IoT sensors and telematics for real-time asset tracking.
Digital Twins: Knowledge of simulation environment tools (NVIDIA Omniverse or similar).

Required "Soft" & Functional Skills

Supply Chain Domain Knowledge: You must speak the language of SKUs, Lead Times, Bullwhip Effects, and OTIF (On-Time In-Full) .
Cross-Functional Leadership: Ability to act as a bridge between Data Science, Operations, and Finance teams.
Ethical AI Governance: Ensuring models are transparent, explainable (XAI), and free from bias in supplier selection or labor scheduling.

Qualifications

Education: Degree in Computer Science or equivalent, Operations Research, Statistics, or a related field.
Experience: 3+ years in AI/ML development, with at least 2 years specifically applied to Supply Chain or Logistics environments.
Portfolio: Proven track record of deploying at least one AI solution that resulted in measurable ROI (e.g., 10% reduction in carrying costs or 15% improvement in route efficiency).

Skillsets Requirements

Category	Specific Skillsets	Key Technologies & Tools
AI & Machine Learning Core	Deep Learning, Transformer Architectures, Reinforcement Learning (RL), Time-Series Forecasting.	PyTorch, TensorFlow, JAX, Scikit-learn, Hugging Face, Prophet, DeepAR.
Generative AI & Agents	RAG (Retrieval-Augmented Generation), Prompt Engineering, Agentic Workflows, LLM Fine-tuning.	GPT-4, Llama 3, Claude 3.5, LangChain, LangGraph, CrewAI, Vector DBs (Pinecone, Milvus).
Operations Research	Linear & Mixed-Integer Programming (MILP), Constraint Programming, Heuristics.	Gurobi, CPLEX, Google OR-Tools, PuLP.
Data Engineering	Pipeline Orchestration, Big Data Processing, Distributed Computing.	Python (Pandas/NumPy), SQL, Apache Spark, Databricks, Snowflake, dbt.
Cloud & MLOps	Model Deployment, CI/CD for ML, Model Monitoring, Containerization.	AWS (SageMaker), Azure (AI Studio), Google Cloud (Vertex AI), Docker, Kubernetes, MLflow.
SCM Domain Expertise	Demand Sensing, Network Design, Bullwhip Effect Mitigation, Safety Stock Optimization.	SAP IBP, o9 Solutions, Blue Yonder, Kinaxis, Oracle SCM Cloud.
Logistics & Warehouse	Route Optimization, Last-mile Delivery, Computer Vision for Quality, Digital Twins.	Manhattan Associates, MercuryGate, project44, NVIDIA Omniverse, YOLO (Computer Vision).
Advanced Visualization	XAI (Explainable AI), Scenario Modeling, Real-time Control Towers.	Power BI, Tableau, Plotly/Dash, Streamlit, SHAP/LIME (for AI interpretability).