Principal Software Developer

Overview:

As Principal Software Developer on PowerPlan’s NXT platform, you’ll own the delivery of scalable, maintainable, cloud-native services while helping move a platform that regulated utilities have trusted for decades from legacy PowerBuilder systems to modern C#/Angular architecture on Azure. You’ll contribute to architecture decisions and shape the team’s engineering culture through the standards you set in code, testing, and collaboration.

We’re in the middle of one of the most significant transformations in our history. Decades of proven functionality are being reimagined on a modern SaaS foundation, while AI becomes central to how we build, deliver, and evolve our products. The next generation of the PowerPlan platform is being designed right now. Join us, and you’ll have the opportunity to influence the technology, engineering practices, and capabilities that will shape it for years to come.

You’ll also help build intelligence into the product itself — classical machine learning pipelines, RAG-powered search, and LLM-driven assistants delivered as production-grade services, not prototypes. If you thrive on challenging technical problems, enjoy untangling complex business ones, and want to help define where this platform goes next, this is the seat.

Responsibilities:

Success will be measured by how quickly you reach architectural fluency, how consistently you ship production-ready features, and whether AI becomes a dependable multiplier for the whole engineering team rather than a side experiment.

• Within your first 4–6 weeks, get fluent in the NXT architecture — your assigned modules, the legacy dependencies, the cloud-native components, and where ML models, LLM services, and data pipelines live today or are planned — then walk the Director of Software Development through what you’ve learned in a 15–20 minute presentation.
• Within your first six weeks, set your own course: a roadmap covering technical upskilling, backlog contribution targets, test coverage goals, and at least three improvements to team tooling or workflow, plus a plan for deepening your applied ML and GenAI skills, reviewed with the Software Development Manager.
• Ship production-ready features on an ongoing basis — built to NXT architecture standards, backed by meaningful automated tests, and needing minimal rework — including AI-powered capabilities such as prediction models, anomaly detection, RAG-based search, LLM-driven chatbots, and intelligent document processing.
• Drive the modernization story forward: deliver refactored services, simplify legacy integrations, show consistent, measurable progress toward PowerBuilder deprecation, and spot the places where ML predictions or GenAI assistants can replace manual work or open up new product value.
• Within six months, show measurable productivity gains — better cycle time, higher test coverage, or increased story throughput — from AI-assisted development, and leave behind reusable patterns, SDKs, or internal libraries that make it easier for teammates to bring ML models and LLM services into their own features.
• Keep what you own dependable: strong unit, integration, and regression coverage, incident response SLAs met, and fewer defects and interruptions over time. For AI features, that means monitored model performance, inference latency within SLA, and outputs validated against quality and safety thresholds — especially in regulated financial and tax contexts.

Qualifications:

• 7–10+ years of hands-on software development experience with enterprise-grade applications.
• Strong proficiency in C#/.NET, Angular or React, and cloud-native development on Azure.
• Experience modernizing legacy systems, including migrating from monoliths to modular services.
• Deep understanding of microservices, RESTful API design, and event-driven architectures.
• Working knowledge of CI/CD pipelines, automated testing, and DevOps practices.
• Working experience integrating classical machine learning models into application workflows — prediction endpoints, feature pipelines, model serving (e.g., ML.NET, Azure ML, ONNX), and feedback loops that improve model accuracy over time.
• Hands-on experience with Generative AI and LLM integration, including RAG architectures built on vector databases and embedding models, conversational features such as chatbots or copilot-style assistants, and prompt engineering with guardrails for enterprise and regulated environments.
• Familiarity with ML Ops pra…