Agentic AI Implementation Engineer in Automotive Supply Chain

Implementation Engineer

• 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.

• 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).

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.