Artec Solutions Pvt Ltd

Location: Noida onsite Job description A Design Engineer specializing in NX Surface Modelling typically works with Siemens NX software to develop and refine complex 3D models for industries like automotive, aerospace, and electronics . Heres a general job description: Job Responsibilities Develop and optimize surface models using Siemens NX . Collaborate with cross-functional teams to ensure design feasibility. Conduct design reviews and implement feedback for improvement. Prepare technical documentation and detailed drawings. Support prototyping and testing activities. Ensure designs meet quality standards and regulatory requirements. Qualifications Bachelor’s degree in Mechanical Engineering or a related field. Proven experience with Siemens NX software . Strong understanding of mechanical design principles . Excellent problem-solving and analytical skills. Ability to work on multiple projects simultaneously . Skills Required Siemens NX CAD expertise. Surface Modelling (Levels A, B, C). Finite Element Analysis (FEA) . Prototyping & Technical Documentation . Project Management & Product Development . Role: Product Designer Industry Type: IT Services & Consulting Department: UX , Design & Architecture Employment Type: Full Time, Permanent Role Category: Other Design Education UG: B.Tech/B.E. in Any Specialization Show more Show less

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 Responsibilities 1 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.