Agentic AI Implementation Engineer in Automotive Supply Chain

An Implementation Engineer specializing in Agentic AI for the automotive supply chain is responsible for customizing, deploying, and maintaining autonomous AI systems that optimize the end-to-end automotive manufacturing and logistics processes. This role requires deep understanding of automotive supply chain intricacies, including parts sourcing, manufacturing schedules, logistics, and after-sales parts management, combined with expertise in autonomous agents and AI deployment.

Core Responsibilities1 Industry-Specific System Design & Customization

• Collaborate with automotive OEMs, Tier 1 suppliers, logistics providers, and IT teams to understand unique supply chain workflows.
• Design AI-driven autonomous agents for specific automotive operations such as:
• Just-in-time (JIT) parts procurement.
• Dynamic inventory management for critical components.
• Supplier negotiation agents for procurement contracts.
• Routing and logistics optimization for vehicle assembly parts.
• Real-time production scheduling adjustments.
• Tailor data integration with automotive ERP (e.g., SAP, Oracle), Manufacturing Execution Systems (MES), and IoT sensors on production lines and logistics assets.

2. Deployment of Autonomous Agents & AI Models

• Deploy predictive models for demand forecasting of automotive components.
• Implement autonomous negotiation agents that interact with suppliers and logistics providers.
• Develop multi-agent systems for managing interdepartmental tasks (production, quality control, procurement).

3 Integration with Automotive Ecosystems

• Build custom APIs and middleware for automotive-specific platforms:
• Vehicle assembly planning systems.
• Supplier portals.
• Fleet management for logistics.
• IoT systems monitoring production lines and warehouse conditions.
• Enable seamless communication between AI agents and legacy systems in the automotive environment.

4 Workflow Automation & Autonomous Decision-Making

• Implement workflows for:
• Automated inventory replenishment based on predictive analytics.
• Dynamic rerouting of logistics for vehicle parts during disruptions.
• Autonomous quality inspection alerts and actions.
• Supplier engagement and negotiation in response to market fluctuations.

5 Testing & Validation in Automotive Context

• Conduct simulations reflecting automotive supply chain scenarios:
• Component shortages.
• Logistic delays.
• Production line adjustments.
• Validate AI agent decisions against automotive KPIs like throughput, downtime, supplier lead times, and inventory costs.

6 Deployment & Continuous Optimization

• Manage staged deployment in automotive production environments.
• Monitor system and agent performance, including decision accuracy and response times.
• Tweak models and agent behaviors based on real-world feedback and evolving automotive market demands.

7 Documentation, Training & Support

• Document integrations, workflows, and agent behaviors in automotive-specific contexts.
• Train supply chain teams, logistics personnel, and manufacturing staff on interacting with autonomous systems.
• Provide ongoing support and iterative improvements.

Key Skills & Industry Knowledge

• Automotive Supply Chain Expertise:
  • Deep understanding of automotive parts procurement, logistics, inventory management, and assembly processes.
  • Familiarity with JIT, Lean manufacturing, and just-in-sequence (JIS) processes.
  • Knowledge of automotive-specific ERP, MES, and IoT platforms.
  • Technical Skills:
    • Proven experience deploying multi-agent systems and autonomous decision-making agents.
    • Expertise in automation tools, cloud platforms, and container orchestration (Kubernetes, Docker).
    • Skilled in Python, Java, or C++ for developing AI/ML components and integrations.
    • Experience with data pipelines involving automotive MES/ERP and IoT data.
    • AI/ML & Autonomous Agent Skills:
      • Experience with reinforcement learning, multi-agent coordination, and negotiation algorithms.
      • Familiarity with predictive analytics and anomaly detection for manufacturing and logistics data.
      • Regulatory & Safety Standards:
        • Knowledge of automotive industry standards, safety protocols, and compliance regarding AI deployment.
        Sample Tasks & Activities
        • Customizing and deploying predictive models for automotive parts demand.
        • Developing negotiation agents to automate supplier contract renewals based on market conditions.
        • Integrating autonomous logistics routing with fleet management systems.
        • Conducting scenario simulations of supply disruptions/releases.
        • Monitoring real-time IoT data from the factory floor and logistics assets.
        • Creating dashboards for supply chain visibility and agent decision summaries.

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