Intelligent Systems Platform Engineer

Hyper•Richmond, VA

1d•Onsite

About The Position

The Intelligent Systems Platform Engineer at Hyper Solutions plays a critical role in building and scaling Hyper Sense, Hyper’s AI-native Industrial IoT platform for mission-critical data center power and cooling infrastructure. This position is responsible for designing and operating the end-to-end platform that enables data ingestion, multi-modal data management, AI model deployment, and customer-facing intelligence products. This role operates at the intersection of industrial systems, data engineering, and applied AI. You will architect and manage a polyglot data environment that integrates telemetry, engineering data, field service history, and OEM documentation into scalable, production-grade data products. The Intelligent Systems Platform Engineer partners closely with data scientists, product managers, and customer-facing teams to deliver advanced analytics and intelligent aftermarket services. This position is well-suited for a systems-oriented engineer who thrives in building complex, data-intensive platforms from the ground up and is comfortable operating across cloud infrastructure, industrial systems, and AI-enabled data workflows.

Requirements

Bachelor’s degree in Computer Science, Electrical Engineering, Systems Engineering, or related field (Master’s preferred)
5+ years of experience designing and operating industrial IoT platforms, data pipelines, or OT/IT data infrastructure
Strong experience with data modeling, schema governance, and high-throughput data pipeline design
Experience designing and implementing polyglot data architectures across multiple data types
Hands-on experience building end-to-end data pipelines for machine learning and generative AI workflows
Familiarity with industrial communication protocols such as Modbus, BACnet, SNMP, OPC-UA, and MQTT
Experience working with cloud platforms such as AWS, Azure, or GCP for data and ML workloads
Experience collaborating with data scientists and supporting production ML model deployment
Strong understanding of data security, governance, and observability practices

Nice To Haves

Experience with predictive maintenance, condition monitoring, or industrial analytics systems
Familiarity with SCADA, DCIM, BMS, or CMMS platforms
Experience applying generative AI and RAG techniques to technical or industrial datasets
Experience with containerized environments and CI/CD pipelines for data platforms
Familiarity with data lakehouse or federated data architectures
Experience with data observability and monitoring tools
Knowledge of IEC 62443 or similar industrial cybersecurity frameworks
Relevant certifications such as AWS ML Specialty, Azure IoT Developer, or similar

Responsibilities

Design, build, and operate Hyper’s AI-native Industrial IoT platform end-to-end
Architect and manage a polyglot data platform leveraging appropriate storage technologies for time-series, relational, unstructured, and vector data
Build and maintain industrial data pipelines integrating IoT telemetry, engineering data, field service records, and OEM documentation
Develop scalable data pipelines supporting both traditional machine learning and generative AI workflows
Define and enforce data quality, governance, observability, and security standards across the platform
Design and maintain industrial data models and asset frameworks supporting monitoring, benchmarking, and trend analysis
Partner with data scientists to enable predictive maintenance, anomaly detection, and fault classification models in production
Collaborate with analytics product managers to deliver data products such as condition-based maintenance, fleet optimization, and energy efficiency insights
Translate customer and operational requirements into platform capabilities and data products
Capture and structure engineering and field service knowledge into standardized platform inputs
Evaluate and select platform components including IoT gateways, edge devices, cloud services, and data infrastructure tooling
Manage vendor and technology partner relationships to ensure alignment with platform roadmap and delivery expectations
Embed security best practices aligned with IEC 62443 and OT/IT convergence standards into platform design and operation
Maintain platform documentation including architecture diagrams, data lineage, and onboarding materials
Partner with Sales and Product teams to align platform capabilities with go-to-market strategy and customer outcomes