Job Title: Compensation & BenefitsThe Compensation & Benefits Manager is responsible for designing, implementing, and managing the organization's compensation and benefits programs to ensure they are competitive, equitable, and aligned with the company's strategic objectives. This role requires expertise in benchmarking, compliance, and innovation to attract, retain, and motivate top talent.Key Responsibilities Compensation ManagementDesign and oversee salary structures, pay grades, and bonus programs aligned with market trends and business goals. Conduct regular benchmarking and market surveys to ensure competitive positioning. Manage the annual compensation review process, including salary adjustments and incentive allocations. Develop and manage job evaluation systems to ensure internal equity and consistency. Benefits AdministrationDesign and implement employee benefits programs, including insurance, retirement plans, wellness initiatives, and other perks. Conduct periodic reviews of benefit programs to ensure compliance, cost-effectiveness, and employee satisfaction. Work with vendors and third-party administrators to manage benefits offerings and resolve issues. Regulatory ComplianceEnsure all compensation and benefits programs comply with local labor laws, tax regulations, and statutory requirements. Monitor changes in legislation and recommend necessary updates to policies and programs. Analytics & ReportingAnalyze compensation and benefits data to provide insights and support strategic decisions. Prepare regular reports for management, highlighting trends, risks, and opportunities. Utilize HR analytics tools to forecast compensation costs and optimize benefits utilization. Employee Communication & SupportDevelop clear communication materials to explain compensation and benefits programs to employees. Act as a point of contact for employee queries related to pay, benefits, and rewards. Conduct training for managers on compensation policies and practices. QualificationsEducation: Bachelor's degree in Human Resources, Business Administration, or related field. A Masters degree or MBA is preferred. Experience: 4-6 years of experience in C&B management, preferably in a mid-to-large organization. Strong expertise in compensation structures, job evaluation methodologies (e.g., Hay, Mercer), and benefits design. Skills: Advanced knowledge of HRIS and compensation management tools. Excellent analytical and project management skills. Strong understanding of labor laws and compliance requirements. Exceptional communication and stakeholder management skills. Key Competencies Strategic mindset with attention to detail. Problem-solving and decision-making ability. Ability to handle confidential and sensitive information with integrity. Adept at managing multiple projects and priorities in a fast-paced environment

Job SummaryWe are seeking a highly experienced and strategic ERP Leader to join our IT department. The ideal candidate will have 18-20 years of total experience, with 10-12 years specifically in ERP implementation and management. Should have led at least 2 S4/HANA migrations for medium/large scale SAP setups.ResponsibilitiesTeam Leadership and Management:Lead, mentor, and manage a team of SAP consultants, developers, and analysts.Provide coaching and development opportunities to team members to enhance their skills and career growth.Foster a collaborative and high-performance team culture, ensuring effective communication and knowledge sharing.SAP Project Management:Oversee the planning, execution, and delivery of SAP projects, ensuring they are completed on time, within scope, and budget.Develop detailed project plans, including timelines, milestones, resource allocation, and risk management.Coordinate with cross-functional teams and stakeholders to ensure alignment on project objectives and deliverables.Strategy and Planning:Work with senior leadership to define the SAP strategy and roadmap, aligning it with the organizations goals and objectives.Evaluate and recommend SAP solutions and technologies to optimize business processes and drive operational efficiency.Ensure continuous improvement of SAP systems, processes, and standards to meet evolving business needs.Data Analytics and reportsLead, mentor, and manage a team of data analysts and data scientists.Develop and maintain a data-driven culture within the organizationDevelop and implement a comprehensive data analytics strategy aligned with the companys goals.Identify key metrics and develop analytical models to measure business performance.Technical Oversight and Support:Provide technical guidance and support to the SAP team on design, development, configuration, and integration of SAP solutions.Ensure the stability, security, and performance of SAP systems through proactive monitoring, maintenance, and troubleshooting.Collaborate with IT and other departments to ensure seamless integration of SAP systems with other enterprise applications.Stakeholder Engagement and Communication:Act as the primary point of contact for SAP-related matters, building strong relationships with key stakeholders across the organization.Communicate project status, risks, and issues to stakeholders, providing regular updates and managing expectations.Lead workshops, training sessions, and presentations to educate stakeholders on SAP capabilities and best practices.Budget and Resource Management:Manage the SAP teams budget, ensuring efficient use of resources and cost-effective delivery of projects and services.Negotiate with vendors and manage third-party relationships to ensure quality delivery of SAP services and solutions.Conduct regular performance reviews and provide feedback to team members to enhance productivity and performance.Qualifications and ExperienceBachelors degree in Engineering (B.E.) or Masters in Computer Applications (MCA). + Masters degree in Business Administration (MBA).18-20 years of total professional experience.10-12 years of hands-on experience in ERP implementation and management.Strong knowledge of SAP modules (e.g., FI/CO, MM, SD, PP, HCM) and integration with other enterprise systems.Experience leading and managing teams of SAP professionals, with a focus on coaching and development.Proficiency in SAP project management methodologies (e.g., ASAP, SAP Activate) and tools.Knowledge and Skills SAP certification in one or more modules or project management.In-depth knowledge of ERP systems and their applications in a manufacturing setting.Familiarity with Agile project management methodologies.Knowledge of cloud platforms (e.g., AWS, Azure, Google Cloud) and hybrid SAP environments.Stay current with the latest data analytics tools and technologies (e.g., Python, R, SQL, Tableau, Power BI).Understanding of SAP security and compliance requirements.Strong leadership and team management skills, with a focus on driving results and fostering a positive team environment.Strategic thinker with the ability to align SAP initiatives with business objectives.Excellent problem-solving skills and attention to detail.Adaptable and flexible, able to manage multiple priorities and work in a dynamic environment.

Role Summary:As the Edge AI Lead, you will oversee the design, optimization, and deployment of AI and machine learning models on edge devices, including NVIDIA Jetson, STM microcontrollers, and other embedded platforms. This role requires expertise in real-time operating systems (RTOS), the Robot Operating System (ROS), and hardware accelerators such as NPUs, TPUs, CPUs, and GPUs. You will lead a team to deliver high-performance, low-latency AI solutions and collaborate closely with robotics, embedded, and mechanical teams to create efficient, real-time autonomous systems.Core Responsibilities:1. Design and Development of Edge AI ModelsLead Edge AI Model Development: Drive the design and development of AI models optimized for edge devices and real-time processing. Focus on applications like object detection, SLAM (Simultaneous Localization and Mapping), sensor fusion, and predictive maintenance.Hardware-Aware Model Optimization: Develop models with an understanding of the constraints and capabilities of different hardware accelerators, including NPUs (Neural Processing Units), TPUs (Tensor Processing Units), CPUs, and GPUs. Optimize models through quantization, pruning, and knowledge distillation to fit the processing capabilities of edge devices.Integration with RTOS and ROS: Ensure models are compatible with real-time operating systems (RTOS) for reliable, time-sensitive applications. Leverage ROS for seamless integration with robotic systems, enabling efficient communication between AI models and control systems.2. Deployment and Integration of Edge AI ModelsEdge Device Deployment: Lead the deployment of AI models on edge devices, including platforms like NVIDIA Jetson, STM32 microcontrollers, ARM Cortex, and Qualcomm Snapdragon. Ensure seamless integration with RTOS and sensor interfaces for reliable real-time processing.Coordinate with Robotics and Control Teams: Collaborate with robotics and embedded control teams to ensure AI models are effectively integrated within robotic platforms. Address challenges related to sensor placement, communication protocols, and power distribution.Optimize Data Flow with ROS and RTOS: Ensure AI models are well-integrated with ROS for real-time data exchange and with RTOS for deterministic performance. Optimize communication pathways between sensors, processors, and AI models for smooth data flow.3. Edge AI Pipeline and MLOpsImplement MLOps for Edge AI: Develop MLOps pipelines specifically tailored for edge deployment, including CI/CD, automated testing, and performance monitoring. Ensure models can be continuously updated, retrained, and redeployed based on operational data.Model Lifecycle Management: Manage model versioning, deployment, and rollback strategies to ensure reliable, up-to-date models are deployed on edge devices. Develop feedback loops to monitor model performance and enable continuous improvement.Real-Time Monitoring and Troubleshooting: Implement monitoring tools to track metrics such as latency, accuracy, and resource usage in real time. Develop protocols for troubleshooting issues to maintain reliability and performance in production environments.4. Hardware Acceleration and Performance OptimizationLeverage Hardware Accelerators: Use tools like NVIDIA TensorRT, CUDA, OpenVINO, and Qualcomm AI Stack to maximize performance on GPUs, TPUs, NPUs, and other hardware accelerators. Optimize AI inference to take full advantage of the specific architecture of each edge device.Low-Level Programming for Efficiency: Utilize low-level programming languages like C, C++, and assembly to optimize code execution on embedded systems. Implement custom kernels and optimize memory usage to improve performance on constrained hardware.Energy and Power Optimization: Focus on energy-efficient model deployment to maximize battery life and reduce power consumption, particularly for mobile and autonomous robots. Apply power management strategies to balance performance and energy efficiency.5. Leadership and Team ManagementLead and Mentor the Edge AI Engineering Team: Build and guide a team of Edge AI engineers, providing technical direction, mentorship, and hands-on support. Set clear performance expectations, provide regular feedback, and support professional growth.Project and Resource Management: Oversee project timelines, resource allocation, and task prioritization for the Edge AI team. Ensure alignment with broader organizational goals and coordinate with cross-functional teams to achieve project milestones.Foster a Culture of Innovation and Technical Excellence: Promote a culture of continuous learning, experimentation, and technical rigor within the team. Encourage knowledge-sharing and collaboration to solve complex edge AI challenges.6. Collaboration with Cross-Functional TeamsWork Closely with Embedded Systems and Software Teams: Collaborate with embedded systems and software engineers to ensure compatibility of AI models with embedded platforms, RTOS, and other system software. Address integration challenges related to memory constraints and real-time requirements.Coordinate with Robotics, Data, and Mechanical Teams: Work closely with robotics engineers, data scientists, and mechanical engineers to align on data needs, model integration, and design constraints. Ensure that Edge AI solutions support and enhance the capabilities of the robotic systems.Align with Head Robotics and AI Software Leadership: Collaborate with the Head Robotics and AGI/DL Software Head to ensure Edge AI development aligns with broader AI and robotics objectives. Ensure edge solutions support real-time perception, decision-making, and autonomous functionality.7. Innovation and Research in Edge AI and Embedded SystemsExplore Emerging Edge AI Technologies: Stay current with advancements in edge AI hardware, software, and frameworks, including low-power inference engines, neural accelerators, and lightweight edge computing frameworks. Assess and implement new technologies to enhance performance and efficiency.Lead R&D Projects on Edge AI Challenges: Initiate research projects to address unique edge AI challenges such as low-power image recognition, real-time SLAM, and multi-sensor fusion on edge devices. Lead proof-of-concept projects to validate new approaches and methodologies.Contribute to Open-Source and Industry Engagement: Encourage team participation in open-source projects and engagement with the AI/ML community. Promote contributions to Edge AI tools, libraries, and frameworks to stay connected with industry trends and drive community-driven innovation.8. Compliance, Safety, and Quality AssuranceEnsure Safety and Compliance Standards: Work closely with quality and compliance teams to ensure that Edge AI models meet industry safety standards, particularly for autonomous and industrial applications.Quality Control and Documentation: Implement quality control processes for Edge AI models, ensuring reliability, consistency, and long-term performance. Maintain comprehensive documentation on deployment protocols, optimization techniques, and troubleshooting procedures.Develop and Enforce Best Practices: Create and maintain best practices for edge AI development, deployment, and maintenance. Ensure protocols are well-documented, accessible, and regularly updated.Required Qualifications:Education: Bachelor's or Master's degree in Computer Science, Electrical Engineering, Robotics, or a related field. Advanced degrees or certifications in AI, embedded systems, or robotics are preferred.Experience:8+ years of experience in AI, machine learning, or embedded systems, with at least 3 years in edge computing or embedded AI.Demonstrated experience deploying and optimizing AI/ML models on edge devices, with a focus on low-latency, high-performance environments.Strong experience in leading teams and managing projects involving edge AI, embedded systems, or robotics.Technical Skills:Machine Learning and AI: Proficiency in ML/DL frameworks (e.g., TensorFlow Lite, PyTorch, ONNX) with extensive experience in model optimization for edge devices.Real-Time Systems (RTOS) and ROS: Experience working with real-time operating systems (RTOS) and the Robot Operating System (ROS) to enable reliable real-time processing and seamless communication in robotic systems.Hardware Acceleration and Low-Level Programming: Expertise in using tools like TensorRT, CUDA, OpenVINO, and low-level programming languages (e.g., C, C++) to optimize AI inference on hardware accelerators (NPU, TPU, CPU, GPU).Embedded Systems and Edge Platforms: Deep knowledge of edge computing platforms such as NVIDIA Jetson, STM32 microcontrollers, ARM Cortex, and Qualcomm Snapdragon. Understanding of RTOS, middleware, and embedded programming.Preferred Qualifications:Robotics and Autonomous Systems Knowledge: Understanding of robotics control systems, perception systems, and integration of AI within autonomous systems.Energy-Efficient AI: Experience with optimizing AI models for low-power applications, particularly for mobile and autonomous robotics where power consumption is critical.Project Management and Agile Practices: Familiarity with Agile methodologies and experience in project management to oversee complex, cross-functional projects effectively.Open-Source and Community Engagement: A track record of contributions to open-source AI/ML or embedded systems projects and active participation in the Edge AI community.

Core Responsibilities:1. Strategic AI/ML Architecture & VisionArchitectural Roadmap: Develop a comprehensive AI/ML architecture roadmap aligned with the company's strategic goals. Identify the right technologies, tools, and frameworks needed to build and scale AI-powered manufacturing automation solutions.Platform Strategy: Define the platform strategy for AI/ML deployment in manufacturing environments, with a focus on scalability, modularity, and performance. Establish guidelines for developing reusable components, libraries, and APIs to support diverse AI applications.Thought Leadership: Act as an internal and external AI thought leader, representing the company's AI initiatives in conferences, industry panels, and client meetings. Advocate for best practices in AI/ML architecture and contribute to the company's knowledge base.2. AI/ML Solution Design & DevelopmentEnd-to-End Solution Design: Lead the design and development of end-to-end AI solutions for manufacturing, from data ingestion and processing to model deployment and inference. Ensure that solutions are architected to meet performance, scalability, and reliability requirements.Model Selection & Optimization: Guide the team in selecting appropriate machine learning and deep learning models based on project requirements. Optimize model performance through hyperparameter tuning, model compression, and deployment strategies.Generative AI Integration: Research and integrate generative AI models for use cases such as predictive maintenance, defect detection, and process optimization. Explore innovative applications of generative AI within manufacturing to enhance automation and provide data-driven insights.CUDA Optimization for Deep Learning: Leverage CUDA and GPU acceleration to optimize the training and inference of deep learning models, ensuring high-performance execution for large datasets and real-time manufacturing applications.3. Infrastructure and DeploymentContainerization with Docker: Design and implement containerized AI/ML solutions using Docker to ensure consistency, scalability, and ease of deployment across cloud and edge environments. Establish best practices for container orchestration and resource management.Cloud and Edge Deployment: Develop strategies for deploying AI models on cloud platforms (e.g., AWS, Azure, GCP) and edge devices to support real-time processing in manufacturing environments. Utilize containerization and orchestration tools like Kubernetes for scalable, multi-environment deployments.Data Pipeline Architecture: Design data pipelines that facilitate real-time processing, storage, and retrieval for AI models. Architect data lakes, warehouses, and streaming frameworks to handle high-volume, high-velocity data in manufacturing environments.4. Cross-Functional Collaboration & LeadershipCollaborate with Engineering and Product Teams: Work closely with software engineering, data science, robotics, and hardware teams to ensure seamless integration of AI components. Provide architectural guidance and support to align efforts across teams.Technical Leadership & Mentorship: Mentor AI/ML engineers, data scientists, and junior architects on best practices in AI architecture, model development, and deployment. Foster a culture of innovation, technical excellence, and collaboration within the team.Stakeholder Engagement: Engage with stakeholders, including product managers, clients, and senior leadership, to understand business requirements and translate them into technical specifications for AI/ML solutions.5. DevOps, MLOps, and Agile PracticesEstablish MLOps Pipelines: Design and implement robust MLOps practices to automate model training, testing, deployment, and monitoring. Leverage CI/CD pipelines to streamline the AI/ML lifecycle, ensuring consistent, repeatable results in production.Agile Methodologies: Promote Agile methodologies within the AI/ML team to enhance adaptability and responsiveness. Lead sprint planning, retrospective sessions, and other Agile practices to ensure efficient project execution.DevOps Integration: Collaborate with the DevOps team to integrate AI/ML models into production environments seamlessly. Develop strategies for deploying models on edge devices and cloud platforms, with a focus on high availability and low latency.6. Performance, Security, and CompliancePerformance Tuning: Oversee the optimization of AI/ML algorithms and models to meet the real-time performance requirements of manufacturing automation systems. Utilize techniques such as model pruning, quantization, and distributed processing.Security and Privacy: Ensure that AI systems adhere to best practices in security and data privacy, particularly when handling sensitive manufacturing data. Implement measures to protect against adversarial attacks and data breaches.Compliance and Standardization: Align AI architecture with industry standards, regulatory requirements, and compliance guidelines. Maintain documentation and standards that support reproducibility, traceability, and auditability of AI solutions.Required Qualifications:Education: Master's or Ph.D. in Computer Science, Data Science, Engineering, or a related field with a focus on AI/ML.Experience: 12+ years of experience in AI/ML, with at least 5 years in an architectural or senior technical leadership role.Proven track record of architecting and deploying AI/ML solutions, preferably within manufacturing, industrial automation, or a similar domain.Hands-on experience with a broad range of machine learning, deep learning, and data processing frameworks (e.g., TensorFlow, Keras, PyTorch, Apache Spark).Experience with ML tools and libraries such as scikit-learn, XGBoost, LightGBM, and Hugging Face Transformers.Technical Skills: AI/ML Expertise: Deep understanding of supervised, unsupervised, reinforcement, and generative learning techniques, as well as expertise in model evaluation, tuning, and optimization.CUDA and GPU Processing: Proficiency in GPU acceleration using CUDA for model training and inference optimization.Data Engineering: Proficiency in data pipeline design, big data processing, and storage solutions (e.g., Kafka, Hadoop, Snowflake).Cloud and Edge Deployment: Experience deploying AI models on cloud (e.g., AWS, Azure, GCP) and edge computing platforms. Understanding of distributed computing and containerization (Docker, Kubernetes).Programming Skills: Strong programming skills in Python, along with experience in Java, C++, or other relevant languages for AI and data processing.MLOps and DevOps: Proficiency in MLOps practices and tools (e.g., MLflow, Kubeflow, DVC) for model versioning, experiment tracking, and automated deployment. Experience with CI/CD pipelines and DevOps practices.Model Monitoring and Maintenance: Experience with tools and practices for monitoring model performance in production, detecting drift, and implementing automated retraining pipelines.A/B Testing and Experimentation: Familiarity with designing and implementing A/B testing frameworks for AI/ML models to evaluate performance improvements and new features.Scalability and Performance Optimization: Advanced knowledge of techniques for scaling AI/ML systems to handle large-scale data and high-throughput requirements in manufacturing environments.Ethical AI and Bias Mitigation: Understanding of ethical considerations in AI development and deployment, including techniques for identifying and mitigating bias in models and datasets.Regulatory Compliance: Knowledge of AI-specific regulations and standards relevant to the manufacturing industry, and experience in implementing compliant AI systems.Open Source Contributions and Management: Experience contributing to and managing open-source AI/ML projects, understanding of open-source licensing, and ability to evaluate open-source tools for integration into the company's AI stack.Security in AI Systems: In-depth knowledge of security best practices for AI/ML systems, including: Techniques for securing model training and inference pipelinesMethods to protect against model inversion and membership inference attacksStrategies for secure model deployment and updates in production environmentsAI-specific Security Frameworks: Familiarity with AI-specific security frameworks and guidelines, such as those provided by NIST or other industry-standard organizations.Secure Data Handling: Expertise in implementing secure data handling practices throughout the AI lifecycle, including data collection, storage, processing, and deletion, in compliance with data protection regulations.Industry-Specific Knowledge: Familiarity with manufacturing processes, automation systems, and Industry 4.0 concepts to better apply AI/ML solutions in the manufacturing context.Risk Management: Experience in identifying and mitigating risks associated with AI implementation in critical manufacturing environments.Explainable AI (XAI): Knowledge of techniques for making AI models more interpretable and transparent, which is crucial for regulatory compliance and stakeholder trust.Cross-Functional Communication: Strong ability to communicate complex AI concepts to non-technical stakeholders, including executives, clients, and regulatory bodies.Continuous Learning: Demonstrated commitment to staying updated with the latest advancements in AI/ML, particularly those relevant to manufacturing and automation.Software Architecture: Strong understanding of software design patterns, microservices architecture, and API design principles.Database Technologies: Experience with both SQL and NoSQL databases, including designing schemas for efficient data storage and retrieval in AI applications.Data Visualization: Familiarity with data visualization libraries and tools (e.g., Matplotlib, Seaborn, Plotly, Tableau) for effectively communicating insights from AI models.