Teserac, Inc.

Full-stack Engineer

• Build operator-facing features for Neuron™ in React and TypeScript, including real-time dashboards, alerting, alarm and ticket workflows, capacity planning views, and power-flow visualizations
• Build interfaces for the AI teammate, including interaction panels, recommendation displays, approval workflows, and audit trails
• Keep large, data-dense views fast and responsive across devices
• Build and maintain telemetry ingestion from customer sites through edge collectors, MQTT over AWS IoT Core, queue-based workers, and normalization into a canonical metric model
• Write integration modules for Siemens Desigo, Schneider PME and EcoStruxure, Niagara, Eaton, BACnet, Modbus, and SNMP sources
• Own point mapping, unit and phase semantics, deduplication, and backfill correctness
• Develop against PostgreSQL and TimescaleDB, including hypertables, continuous aggregates, compression, and retention
• Design safe schemas and migrations for tables growing into billions of rows
• Identify and resolve performance issues at real customer data volumes
• Write tested, reviewed code and contribute to CI/CD and automated testing
• Troubleshoot production issues across the stack and share on-call as the platform scales
• Collaborate directly with the Principal Software & Architecture Lead, Product, and Solution Engineering
• Own features end to end from telemetry ingestion through time-series storage to operator dashboards

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Full Stack Developer

AI Engineer

• Build intelligent systems at the intersection of applied AI and critical infrastructure
• Work across the full AI development lifecycle, from data pipelines and model integration to agentic orchestration, evaluation, and production support
• Develop multi-agent orchestration and LLM-driven triage workflows
• Apply time-series modeling to anomaly detection, failure prediction, and health forecasting on multivariate telemetry
• Build retrieval-augmented knowledge systems for operations teams
• Develop data and ML pipelines, including ingestion, ETL, and dataset construction
• Fine-tune and post-train language models for operational use cases
• Build AI observability, evaluation frameworks, and production performance benchmarking
• Design, develop, and maintain AI-powered applications and automation workflows
• Integrate and optimize LLM APIs for production use cases
• Build and refine retrieval and knowledge-augmentation pipelines
• Implement monitoring, tracing, and debugging capabilities for AI systems
• Read and synthesize relevant research and translate ideas into practical experiments
• Contribute to AI architecture decisions and production hardening
• Stay current with the evolving AI/ML landscape
• Collaborate closely with a small, fast-moving engineering team

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