Senior Software Developer

Overview This role is a high-impact opportunity to shape the next generation of PowerPlan's cloud-native SaaS platform (NXT). The Senior Software Developer owns delivery of scalable, maintainable services while playing a critical role in the company's modernization effort from legacy PowerBuilder systems to modern C#/Angular architectures - and in embedding intelligent capabilities across the platform through classical machine learning pipelines and Generative AI (GenAI) solutions. The developer will contribute to architecture decisions, accelerate engineering quality, and influence the team's engineering culture through high standards in code, testing, and collaboration. This includes designing and integrating ML-driven prediction and feedback loops, building Retrieval-Augmented Generation (RAG) pipelines, and delivering LLM-powered features such as intelligent chatbots and contextual assistants within the application stack. This is an ideal role for someone who thrives in challenging technical environments, enjoys solving complex business problems, and wants to help define the future of a platform trusted by regulated utilities for decades. COMPANY PowerPlan provides enterprise cloud solutions that enable energy and regulated utility organizations to manage financial, tax, and operational workflows with precision and confidence. We are modernizing a trusted legacy platform into a modular, cloud-native architecture powered by Azure services, modern front-end frameworks, and increasingly AI-enabled workflows - from predictive analytics and automated decision support to GenAI-powered user experiences. We value curiosity, collaboration, and engineering excellence, and we empower developers to punch above their weight, take ownership, and grow with a product suite undergoing major technical transformation. ResponsibilitiesKEY PERFORMANCE OBJECTIVES (First 12 Months)OBJECTIVE 1: Master System Architecture & Project Context (First 4-6 Weeks)Outcome: Within 4-6 weeks, develop a complete understanding of assigned modules, system interactions, legacy dependencies, and cloud-native components, and the current state of ML and GenAI integrations across the platform. Deliver a 15-20 minute knowledge presentation to the Director of Software Development demonstrating architectural fluency, including a map of where ML models, LLM services, and data pipelines currently exist or are planned. Impact: Ensures rapid ramp-up, prevents rework due to unclear assumptions, and enables independent contribution to the NXT platform. How: Meet with architects, SMEs, QA, and end users; review codebases, ADRs, data flows, and sprint artifacts; explore NXT services and legacy PowerBuilder components; audit existing ML models, feature stores, embedding pipelines, and LLM integration points; synthesize findings into a clear architectural narrative. OBJECTIVE 2: Establish a Growth & Contribution Roadmap (First 6 Weeks)Outcome: Develop a 6-week roadmap covering technical upskilling, user interaction expectations, backlog contribution targets, test coverage goals, and at least three improvements to team tooling or workflow. Include a personal development plan for deepening applied ML/GenAI skills relevant to the NXT platform (e.g., RAG architecture patterns, prompt engineering, ML model serving). Review with the Software Development Manager. Impact: Reinforces a culture of proactive ownership and continuous improvement while increasing predictability and delivery quality - and ensures the team builds durable AI/ML capability, not just one-off experiments. How: Analyze sprint metrics, collaborate with cross-functional partners, identify gaps in automation or documentation, evaluate CI/CD and testing workflows, assess current ML pipeline maturity and GenAI integration readiness, and define measurable, time-bound goals. OBJECTIVE 3: Deliver High-Quality, Production-Ready Features - Including AI/ML-Powered Capabilities (Ongoing)Outcome: Consistently design, develop, and deliver cloud-native features aligned with NXT architecture standards. Code meets definition-of-done criteria, includes meaningful automated tests, and requires minimal rework across sprints. This includes building and maintaining features powered by classical ML (prediction models, anomaly detection, recommendation engines) and GenAI (RAG-based search, LLM-driven chatbots, intelligent document processing) as part of the core product experience. Impact: Ensures platform stability, improves time-to-value for customers, and builds a maintainable foundation aligned with long-term architectural goals - where AI capabilities are first-class, production-grade services, not prototypes. How: Collaborate with Product, Architecture, QA, and CloudOps; write clean C#/Angular code; integrate ML inference endpoints and LLM APIs into application services; design feedback loops that capture user interactions to retrain or fine-tune models; participate in code reviews; follow CI/CD practices; contribute to documentation and design records. OBJECTIVE 4: Advance the Company's Modernization Strategy & AI Integration Strategy (Ongoing)Outcome: Contribute to the broader modernization effort by delivering refactored services, improving code quality standards, and identifying opportunities for automation or simplification of legacy integrations. Proactively identify where classical ML predictions, GenAI assistants, or LLM-powered workflows can replace manual processes, accelerate user workflows, or unlock new product value. Demonstrate consistent, measurable progress toward long-term PowerBuilder deprecation goals. Impact: Strengthens architectural consistency, reduces maintenance burden, and accelerates the migration to a scalable, cloud-native platform, and positions the product as an AI-enhanced solution in the regulated utility market. How: Work closely with architects, CloudOps, and InfoSec to ensure modernization aligns with performance and security requirements; apply event-driven and microservice patterns; enhance observability; design and deploy RAG pipelines using vector databases and embedding models; build conversational interfaces backed by LLMs with appropriate guardrails for regulated environments; and proactively mitigate architectural risks. OBJECTIVE 5: Raise Team Productivity Through AI-Assisted Development (First 6 Months)Outcome: Within 6 months, demonstrate measurable productivity gains (improved cycle time, higher test coverage, or increased story throughput) directly linked to AI-assisted development tools. Additionally, contribute reusable patterns, SDKs, or internal libraries that make it easier for the broader team to integrate ML models and LLM services into their own features. Impact: Transforms AI from experimental tooling into a stable productivity multiplier across the engineering team, enabling faster feature delivery and modernization progress - while building institutional knowledge around applied ML/GenAI engineering. How: Use AI tools for coding, testing, documentation, and refactoring; experiment with prompts and workflows; track before/after results; mentor peers; share RAG, chatbot, and ML integration patterns; and contribute patterns to AI team guidelines. OBJECTIVE 6: Maintain High Operational Reliability Across Owned Services - Including ML/AI Components (Ongoing)Outcome: Deliver services with strong unit, integration, and regression test coverage; meet incident response SLAs; and demonstrate reductions in defects and operational interruptions over time. For ML and GenAI features, ensure model performance is monitored, inference latency is within SLA, and outputs are validated against quality and safety thresholds - especially in regulated financial and tax contexts. Impact: Improves customer satisfaction, reduces production risk, and enables more capacity for new feature development - while ensuring AI-powered features meet the reliability and compliance standards expected by utility customers. How: Monitor logs and alerts; participate in on-call rotations; conduct root-cause analysis; refactor fragile areas; implement model monitoring, drift detection, and LLM output validation; collaborate with CloudOps/InfoSec to satisfy operational and compliance standards. QualificationsWHAT YOU BRING7-10+ 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 migration 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 - including building prediction endpoints, feature pipelines, model serving (e.g., ML.NET, Azure ML, ONNX), and feedback loops that capture outcomes to improve model accuracy over time. Hands-on experience with Generative AI and LLM integration, including: Designing and implementing Retrieval-Augmented Generation (RAG) architectures using vector databases (e.g., Azure AI Search, Pinecone, Weaviate) and embedding models. Building conversational AI features such as chatbots or copilot-style assistants powered by LLMs (e.g., Azure OpenAI, OpenAI API). Prompt engineering, context management, and implementing guardrails/safety layers for LLM outputs in enterprise and regulated environments. Familiarity with ML Ops practices: model versioning, A/B testing of models, monitoring model drift, and managing inference pipelines in production. Strong communication skills and the ability to collaborate across architecture, product, QA, and CloudOps. Experience using AI-assisted development tools (GitHub Copilot, Cursor, Windsurf) and familiarity with LLM-based tooling. Demonstrated ownership mindset: follows through, escalates blockers early, and raises the engineering bar. PowerPlan is an EOE Applicant Privacy Notice Please note that this is a hybrid role that involves a combination of onsite work from our corporate office as well as work from home. While we strive to accommodate flexible working arrangements when sensible, there will be times when onsite work is required. This could include scheduled office days, team meetings, client meetings, or special events.

Created: 2026-03-10