Senior Software Engineer II

We're looking for a hands-on Software Engineer to help build and scale the next generation of AI-powered solutions.

In this role, you'll combine deep Generative AI engineering with direct customer impact, developing production-grade applications that support field services, technical operations and the continued evolution of the AiDASH SaaS platform.

Approximately 75% of your time will focus on designing, building and operating Generative AI solutions, including agentic automation platforms, product-integrated GenAI capabilities and internal AI applications.

The remaining 25% will involve partnering with electric-utility customers to deliver tailored solutions, address operational needs and support data-related work, including GIS data corrections.

This opportunity is well suited to someone who brings strong experience with LLM-based applications, backend and distributed systems, cloud services and secure multi-tenant architectures.

You'll also have the opportunity to deepen your knowledge of AiDASH's proprietary platforms and the utility vegetation-management domain while collaborating with teams across engineering, product and professional services.

What you'll be doing

Generative AI Engineering | Approximately 75%


* Build and expand AiDASH's proprietary agentic automation platforms, including the Virtual Field Services Engineer, which supports customer customization development with human review, and the Virtual Support Engineer, designed to scale technical support capabilities.


* Design and deliver Generative AI capabilities within the AiDASH SaaS product, contributing across the full development lifecycle, from technical design and development to testing, deployment and production support.


* Develop enterprise data assistants and conversational web experiences, including a customer 360 assistant that provides clear, reliable answers grounded in current data from internal APIs, SQL and NoSQL databases, documents and search indexes.


* Build retrieval and orchestration services using retrieval-augmented generation, tool calling and structured outputs to connect LLMs with existing product APIs at runtime.


* Create intelligent workflows that transform conversations, documents, meeting notes and incoming requests into structured tasks, work items and notifications, with human approval steps and audit logging built in.


* Apply Generative AI to internal engineering operations, including the development of AI-assisted DevOps tools.


* Integrate, evaluate and monitor LLMs in production, including prompt and model configuration, versioning, automated evaluation and rollback capabilities.


* Track and continuously improve answer quality, source accuracy, latency, safety and cost.


* Implement robust authentication, authorization, tenant-level data separation, audit logging, error handling, retries and observability for AI services handling enterprise data.


* Develop proofs of concept and MVPs, then h...