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.
Key Responsibilities: · The Advanced Analytics Professional supports the collection, analysis, interpretation, and presentation of data to support the strategic decision making · Supports the acquisition, processing, integration, and cleaning of data from multiple sources · Uses a variety of tools to automate data collection and build reports guided by procedures · Undertakes initial data investigation and data analysis to identify trends in data, deriving insights to help deliver business improvement · Designs and builds data visualization to engage audience in a compelling way and to enable effective storytelling · Supports the development and refinement of data dashboards and reports · Supports the presentation of data insights to relevant stakeholders for planning and decision support · Supports in the implementation of ways to improve working processes within the area of data analytics · Owns contribution to team and data governance processes, policies & regulations. Follows best practices and agile methodology, owning sprint goals and participating in sprint activities and governance Design and develop advanced, interactive dashboards and analytics solutions using Qlik Sense to meet complex business needs. Develop and maintain sophisticated data models, visualizations, and KPI metrics for diverse departments and functions. Perform advanced data analysis, including trend analysis, forecasting, and predictive modeling to generate actionable insights. Integrate multiple data sources, including structured and unstructured data, into Qlik Sense to provide a comprehensive view of enterprise data. Collaborate with data engineers, data scientists, and business stakeholders to define analytical requirements and translate them into technical solutions. Optimize and fine-tune Qlik Sense applications for maximum performance and usability. Conduct data validation, quality checks, and troubleshooting to ensure accuracy and integrity of analytics solutions. Share insights and recommended actions clearly and effectively with both technical and non-technical audiences. Stay updated with the latest advances in analytics, data science, and Qlik Sense features to enhance capabilities and methodologies. Train and mentor junior analysts and end-users on advanced Qlik Sense functionalities and analytical techniques. Key Skills: · Data Analysis / Data Preparation - Advanced · Dataset Creation / Data Visualization - Advanced · Data Quality Management - Intermediate · Programming / Scripting - Intermediate · Data Storytelling - Intermediate · Business Analysis / Requirements Analysis - Intermediate · Data Dashboards - Advanced · Business Intelligence Reporting - Advanced · Database Systems - Intermediate · Agile Methodologies / Decision Support- Foundation Technical Skills: · Cloud - GCP Fundamental (Compute, BI Tools, Data Query) - Intermediate · Coding Lang – Sql, R, Python - Advanced · Libaries - SkLearn, Tensorflow, Matplotlib etc- Intermediate · Database - Big Query, Sql, ETL Intermediate · Visualization tools – Qlik – Advanced to Expert Job Summary: We are looking for a highly skilled Advanced Analytics Professional with expertise in Qlik Sense to design, develop, and deliver advanced analytics solutions that drive data-driven decision-making. The ideal candidate will leverage deep analytical skills and extensive knowledge of Qlik Sense to create sophisticated dashboards, data models, and predictive analytics for complex business problems. Required Skills and Qualifications: Extensive experience with Qlik Sense development, including complex scripting, data modeling, and visualization. Strong understanding of advanced analytics concepts including statistical analysis, predictive modeling, and data mining. Proficiency in data analysis tools such as R, Python, or SAS is a plus. Strong SQL skills for data extraction and transformation. Familiarity with data warehousing and ETL processes. Knowledge of machine learning algorithms and deployment is an advantage. Excellent problem-solving, analytical, and critical-thinking skills. Strong communication skills for translating complex data insights into clear business narratives. Relevant certifications (e.g., Qlik Sense Data Architect, Data Analyst, or similar) are preferred. Preferred Qualifications: Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Business Analytics, or related fields. Experience working in industries such as finance, healthcare, retail, or manufacturing. Knowledge of cloud platforms and big data technologies.
As a Principal ServiceNow Architect at Cognizant, you will be the technical authority and strategic leader across all ServiceNow modules. You'll drive platform-wide architecture decisions, mentor fellow architects, and lead development teams to deliver scalable, enterprise-grade solutions. This is a high-impact role within the Center of Excellence, where innovation, governance, and delivery excellence converge. - Define and own the end-to-end architecture across all ServiceNow modules: ITSM, ITOM, ITAM, HRSD, SecOps, GRC, CSM, SPM, and App Engine. - Lead technical governance, architecture reviews, and design authority boards. - Collaborate with enterprise architects and business stakeholders to align platform strategy with business goals. - Guide and mentor Solution Architects, Developers, and Team Leads across multiple delivery streams. - Establish best practices, reusable frameworks, and accelerators for rapid delivery. - Drive platform upgrades, performance tuning, and adoption of new ServiceNow capabilities (e.g., AI/ML, Process Optimization, RPA Hub). - Architect complex integrations using IntegrationHub, Flow Designer, REST/SOAP APIs, and third-party tools. - Ensure adherence to CSDM, ITIL, and security standards across implementations. - Lead technical discovery workshops, proof-of-concepts, and pre-sales solutioning when required. - 12-14 years of IT experience with 10+ years in ServiceNow development and architecture. - Deep hands-on expertise across all major ServiceNow modules. - Strong command of JavaScript, GlideScript, Flow Designer, and integration frameworks. - Proven experience in multi-module implementations and cross-functional platform leadership. - Expertise in CMDB modeling, IRE, MID Server architecture, and cloud discovery. - Experience with Agile/SAFe delivery, DevOps pipelines, and CI/CD for ServiceNow. - Strong stakeholder management, communication, and team leadership skills. (Note: Preferred Certifications are mentioned in the JD but not explicitly listed in the provided JD),