Manager, Software Engineering

NetDocuments•Lehi, UT

1d•$170,000 - $190,000

About The Position

NetDocuments is seeking an Engineering Manager to lead a team building and delivering shared AI capabilities across its platform. The role drives engineering execution and technical direction while leading the development of scalable, secure, reliable AI-powered solutions for legal professionals. The manager partners with Product, Architecture, and senior engineering leaders to turn customer needs and emerging AI technologies into meaningful product capabilities, with a focus on delivery, engineering talent development, quality, performance, and enterprise readiness. The role guides AI architecture and implementation, including model selection, prompt engineering, retrieval-augmented generation (RAG), evaluation, testing, and monitoring. It also ensures AI capabilities meet enterprise requirements for security, privacy, compliance, and reliability. The manager leads, coaches, and develops software engineers; establishes priorities and engineering practices; and collaborates with Product, Design, Architecture, and other AI engineering teams to align technical priorities and shared platform capabilities.

Requirements

At least 6 years of professional software engineering experience, including at least 2 years managing engineering teams.
Experience delivering production-grade AI or LLM-powered features in an enterprise SaaS environment.
Strong understanding of LLMs, including prompt engineering, RAG, context limitations, latency, cost considerations, and common failure modes.
Familiarity with AI evaluation methodologies, retrieval systems, and methods for measuring model accuracy, reliability, and performance.
Understanding of AI agent architectures, tool-calling patterns, and emerging integration standards such as Model Context Protocol (MCP).
Strong foundation in modern software engineering, including distributed systems, microservices, and cloud-native architectures.
Experience with AWS or Azure and modern engineering practices such as CI/CD, containerization, and observability.
Working knowledge of C#/.NET or similar backend development technologies, with the ability to evaluate technical designs and guide engineering decisions.
Demonstrated ability to lead engineering teams, manage competing priorities, and deliver complex technical initiatives.
Strong communication, collaboration, and stakeholder management skills, with the ability to influence technical and business decisions.
Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Nice To Haves

Experience developing AI-powered solutions in legal technology, document management, or another regulated industry.
Experience building and scaling document AI capabilities, including intelligent document processing and retrieval systems.
Experience evaluating and managing AI model providers, including performance, cost optimization, and vendor selection.
Familiarity with model customization techniques such as fine-tuning and the ability to evaluate their business and technical value.
Experience leading teams responsible for shared AI services or platform capabilities used across multiple products.
Proven success building high-performing engineering teams that consistently deliver impactful software.
Advanced degree in Computer Science, Engineering, or a related field.

Responsibilities

Lead planning and execution of engineering initiatives to deliver high-quality AI capabilities predictably.
Partner with Product and Architecture to translate customer needs and emerging AI technologies into scalable, secure solutions.
Balance feature development with system reliability, performance, cost efficiency, and operational excellence.
Identify technical risks, dependencies, and tradeoffs, and communicate recommendations to leadership and cross-functional stakeholders.
Establish clear priorities, accountability, and engineering practices that support consistent delivery.
Partner with Principal and Staff Engineers to guide architecture, technical direction, and implementation strategies for AI-powered solutions.
Evaluate AI model selection and technical tradeoffs, including response quality, latency, scalability, and cost.
Guide prompt engineering and RAG approaches to deliver accurate, contextually relevant AI experiences.
Set expectations for AI evaluation, testing, and monitoring, including regression testing for model and prompt changes.
Ensure AI capabilities meet enterprise standards for security, privacy, compliance, and reliability.
Use technical knowledge of C#/.NET, microservices, containerization, and AWS or Azure to evaluate designs, remove blockers, and support engineering decisions.
Lead, coach, and develop software engineers, supporting performance, career growth, and technical development.
Foster a culture of ownership, accountability, experimentation, and continuous improvement.
Drive hiring and talent development to build a high-performing engineering team.
Encourage modern engineering practices and AI-assisted development tools to improve productivity and software quality.
Promote effective collaboration, constructive challenge, and continuous improvement of technical solutions.
Partner with Product, Design, Architecture, and other AI engineering teams to align technical priorities and shared platform capabilities.
Communicate engineering progress, technical decisions, risks, and tradeoffs clearly to stakeholders and senior leadership.
Build cross-functional relationships that support efficient decision-making and successful product delivery.
Translate complex technical concepts into clear business outcomes and recommendations.