Principal Software Developer

PowerPlan, Inc • Atlanta, GA

3d • Hybrid

About The Position

As Principal Software Developer on PowerPlan's NXT platform, you will own the delivery of scalable, maintainable, cloud-native services and help modernize a platform used by regulated utilities for decades, moving it from legacy PowerBuilder systems to a modern C#/Angular architecture on Azure. You will contribute to architecture decisions and influence engineering culture through standards for code, testing, and collaboration.

The role is part of a major transformation to reimagine established functionality on a modern SaaS foundation and make AI central to product development and delivery. You will help build production-grade intelligence into the product, including classical machine learning pipelines, RAG-powered search, and LLM-driven assistants.

You will develop fluency in the NXT architecture, deliver production-ready features, advance platform modernization, and help make AI a dependable multiplier for the engineering team. You will also establish reusable patterns and tools for integrating machine learning models and LLM services, while ensuring dependable operation and appropriate quality and safety controls, particularly in regulated financial and tax contexts. This is a hybrid role combining onsite work at the corporate office with work from home; onsite attendance may be required for scheduled office days, team or client meetings, and special events.

Requirements

• 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.
• Experience integrating classical machine learning models into application workflows, including prediction endpoints, feature pipelines, model serving (such as ML.NET, Azure ML, or ONNX), and feedback loops that improve model accuracy over time.
• Hands-on experience with Generative AI and LLM integration, including RAG architectures using 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 practices, including model versioning, A/B testing of models, monitoring model drift, and managing production inference pipelines.
• Experience using AI-assisted development tools such as GitHub Copilot, Cursor, or Windsurf, and familiarity with LLM-based tooling.
• Strong communication skills and ability to collaborate across architecture, product, QA, and CloudOps.
• Ownership mindset, including following through, escalating blockers early, and raising engineering standards.

Responsibilities

• Learn the NXT architecture, assigned modules, legacy dependencies, cloud-native components, and current or planned locations of ML models, LLM services, and data pipelines; present findings to the Director of Software Development within the first 4–6 weeks.
• Create a roadmap within the first six weeks covering technical upskilling, backlog contribution targets, test coverage goals, at least three team tooling or workflow improvements, and a plan for developing applied ML and GenAI skills; review it with the Software Development Manager.
• Continuously ship production-ready features that follow NXT architecture standards, include meaningful automated tests, and require minimal rework.
• Deliver AI-powered capabilities, including prediction models, anomaly detection, RAG-based search, LLM-driven chatbots, and intelligent document processing.
• Modernize services, simplify legacy integrations, make measurable progress toward PowerBuilder deprecation, and identify opportunities for ML predictions or GenAI assistants to replace manual work or create product value.
• Within six months, demonstrate measurable productivity gains from AI-assisted development and create reusable patterns, SDKs, or internal libraries to help teammates integrate ML models and LLM services.
• Maintain dependable services through strong unit, integration, and regression test coverage, meeting incident response SLAs, and reducing defects and interruptions over time.
• For AI features, monitor model performance, maintain inference latency within SLA, and validate outputs against quality and safety thresholds, particularly for regulated financial and tax contexts.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: No Education Listed