Capgemini
Overview
Client is seeking an experienced AWS IoT and Edge Deployment Specialist to support the buildout of a smart manufacturing platform. This role is critical to implementing a production environment that integrates on-premises industrial equipment with AWS cloud and edge services. The candidate should have deep knowledge of the AWS IoT Greengrass ecosystem, industrial device integration, security best practices, and how telemetry data supports ML workflows using SageMaker. Responsibilities
Lead the deployment and configuration of AWS IoT Greengrass and related edge services on industrial hardware Design and implement secure data collection pipelines from factory devices (e.g., OPC-UA, Modbus) using IoT SiteWise, Greengrass components, and custom connectors Support OTA (over-the-air) updates, configuration management, and remote debugging strategies Design end-to-end observability and logging for edge and cloud-connected assets using CloudWatch, CloudTrail, and custom metrics Ensure edge and cloud interactions meet security and compliance best practices (IAM roles, device certificates, encryption, auditability) Collaborate with ML and data teams to enable edge-to-cloud telemetry flow for model training in SageMaker and inference deployment to edge Help define and apply standards for data formats, labeling, versioning, and metadata tagging from edge sensors Support validation and field testing efforts during deployment cycles across diverse factory environments Contribute to documentation and runbooks to support maintainability and scalability of the edge-cloud architecture Required Qualifications
3 years of experience with AWS IoT services (Greengrass, IoT Core, SiteWise, IoT Jobs, IoT Device Defender) Strong understanding of industrial protocols (OPC-UA, MQTT) and device integration patterns Working knowledge of AWS security best practices for IoT, including certificates, policy management, and secure OTA Experience building data collection or telemetry pipelines for downstream ML use cases (e.g., predictive maintenance, anomaly detection) Familiarity with AWS services such as S3, Timestream, SageMaker, and CloudWatch Excellent communication and documentation skills, with ability to work across DevOps, ML, and plant engineering teams Nice to Have
AWS Certified Solutions Architect or AWS Certified Machine Learning – Specialty Familiarity with Docker, container orchestration on edge (via Greengrass), and OTA pipelines Experience with manufacturing compliance standards Knowledge of data governance and lifecycle management for sensor data Understanding of cloud-to-edge feedback loops (e.g., inference triggers, alerts, autonomous control actions) Disclaimer
Capgemini is an Equal Opportunity Employer encouraging inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law.
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Client is seeking an experienced AWS IoT and Edge Deployment Specialist to support the buildout of a smart manufacturing platform. This role is critical to implementing a production environment that integrates on-premises industrial equipment with AWS cloud and edge services. The candidate should have deep knowledge of the AWS IoT Greengrass ecosystem, industrial device integration, security best practices, and how telemetry data supports ML workflows using SageMaker. Responsibilities
Lead the deployment and configuration of AWS IoT Greengrass and related edge services on industrial hardware Design and implement secure data collection pipelines from factory devices (e.g., OPC-UA, Modbus) using IoT SiteWise, Greengrass components, and custom connectors Support OTA (over-the-air) updates, configuration management, and remote debugging strategies Design end-to-end observability and logging for edge and cloud-connected assets using CloudWatch, CloudTrail, and custom metrics Ensure edge and cloud interactions meet security and compliance best practices (IAM roles, device certificates, encryption, auditability) Collaborate with ML and data teams to enable edge-to-cloud telemetry flow for model training in SageMaker and inference deployment to edge Help define and apply standards for data formats, labeling, versioning, and metadata tagging from edge sensors Support validation and field testing efforts during deployment cycles across diverse factory environments Contribute to documentation and runbooks to support maintainability and scalability of the edge-cloud architecture Required Qualifications
3 years of experience with AWS IoT services (Greengrass, IoT Core, SiteWise, IoT Jobs, IoT Device Defender) Strong understanding of industrial protocols (OPC-UA, MQTT) and device integration patterns Working knowledge of AWS security best practices for IoT, including certificates, policy management, and secure OTA Experience building data collection or telemetry pipelines for downstream ML use cases (e.g., predictive maintenance, anomaly detection) Familiarity with AWS services such as S3, Timestream, SageMaker, and CloudWatch Excellent communication and documentation skills, with ability to work across DevOps, ML, and plant engineering teams Nice to Have
AWS Certified Solutions Architect or AWS Certified Machine Learning – Specialty Familiarity with Docker, container orchestration on edge (via Greengrass), and OTA pipelines Experience with manufacturing compliance standards Knowledge of data governance and lifecycle management for sensor data Understanding of cloud-to-edge feedback loops (e.g., inference triggers, alerts, autonomous control actions) Disclaimer
Capgemini is an Equal Opportunity Employer encouraging inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law.
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