Principal Software Engineer, Backend & AI Workflow Integration

Principal Software Engineer, Backend & AI Workflow Integration

Location: United States

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Description

• Principal Software Engineer, Backend & AI Workflow Integration

• Department: Engineering

• Reports To: Manager of Software Engineering

• Level: Principal Individual Contributor / Technical Lead

• Location: Hybrid / Remote

• Employment Type: Full-Time

About the Role

We are seeking a Principal Software Engineer with deep expertise in back-end .NET engineering, enterprise platform architecture, and the practical integration of AI-enabled workflows into complex software products. This role is ideal for a senior technical leader who can operate as both a hands-on engineer and an AI evangelist across the engineering organization.

You will lead the design and implementation of scalable, secure, and compliant back-end services while helping shape how our product organization uses AI to improve workflows, decision support, automation, developer productivity, and customer outcomes. Because we operate in a regulatory and compliance-driven industry, this role requires strong judgment around data governance, security, auditability, explainability, privacy, and responsible AI adoption.

The right candidate will not only know how to build enterprise-grade systems in C#, .NET, APIs, distributed services, databases, and cloud platforms, but will also be actively engaged with modern AI architectures, including LLM-based workflows, retrieval-augmented generation, agentic patterns, model orchestration, prompt engineering, evaluation frameworks, and AI governance practices.

This is a high-impact technical leadership role for someone who can set direction, mentor senior engineers, influence architecture, and raise the organization’s AI maturity.

Key Responsibilities

• Back-End Engineering & Architecture

• Lead the design, development, and evolution of enterprise-grade back-end systems using C#, .NET, ASP.NET Core, Web APIs, microservices, event-driven architecture, and cloud-native patterns.

• Architect scalable, resilient, secure, and observable services that support mission-critical product workflows in a regulated environment.

• Drive technical decisions around service boundaries, API design, asynchronous processing, data models, integration patterns, performance, reliability, and operational supportability.

• Partner with product managers, UX, data, security, compliance, and infrastructure teams to translate complex business and regulatory requirements into robust technical solutions.

• Establish and enforce engineering standards for code quality, testing, maintainability, secure development, CI/CD, observability, and production readiness.

• Participate directly in implementation, code reviews, design reviews, troubleshooting, and critical technical decisions.

• AI Workflow Integration

• Lead the technical strategy and implementation of AI-enabled workflows within the enterprise product platform.

• Identify opportunities where AI can improve product capabilities, automate repetitive workflows, enhance user decision-making, improve operational efficiency, or accelerate internal engineering productivity.

• Design and implement AI architectures that may include LLMs, retrieval-augmented generation, vector databases, semantic search, prompt orchestration, model APIs, agents, workflow engines, evaluation pipelines, MCP and human-in-the-loop review patterns.

• Ensure AI capabilities are integrated safely into existing enterprise systems with appropriate controls for security, compliance, privacy, auditability, explainability, and reliability.

• Collaborate with data engineering, machine learning, product, compliance, and security stakeholders to define AI use cases, success metrics, risk controls, and production readiness criteria.

• Evaluate AI vendors, frameworks, APIs, model hosting options, orchestration platforms, and emerging architectural patterns to determine what is appropriate for enterprise use.

• Develop reusable AI integration patterns, reference architectures, internal libraries, and best practices that can be adopted across multiple product teams.

• AI Evangelism & Technical Leadership

• Serve as an AI evangelist across the engineering organization, helping teams understand how to responsibly and effectively use AI in both product features and software delivery practices.

• Stay current on AI trends, tools, architectures, model capabilities, regulatory considerations, and industry best practices, then translate that knowledge into actionable guidance for the development organization.

• Create internal enablement materials, technical talks, proof-of-concepts, architecture guidance, coding standards, and adoption playbooks for AI-assisted development and AI-powered product capabilities.

• Mentor engineers on AI fundamentals, prompt design, secure AI usage, evaluation methods, model limitations, hallucination mitigation, data privacy risks, and responsible implementation patterns.

• Champion practical AI adoption without hype, ensuring that solutions are measurable, maintainable, compliant, and aligned to real business outcomes.

• Act as a trusted technical advisor to engineering leadership on AI strategy, platform modernization, architecture tradeoffs, and talent development.

• Compliance, Security & Governance

• Design software and AI workflows that meet the expectations of a regulatory/compliance-driven environment, including strong consideration for data protection, audit trails, access controls, traceability, retention, explainability, and change management.

• Work closely with security, legal, compliance, and risk stakeholders to ensure AI integrations follow appropriate governance standards.

• Help establish engineering practices for responsible AI usage, including model evaluation, prompt/version control, test harnesses, monitoring, fallback strategies, human review, and escalation workflows.

• Ensure sensitive data is protected when interacting with AI models, third-party APIs, vector stores, logs, telemetry systems, and developer productivity tools.

• Contribute to internal policies and standards around secure AI-assisted development, AI-generated code review, data handling, and production AI feature deployment.

• Required Qualifications

• 10+ years of professional software engineering experience, with significant depth in back-end development and enterprise software systems.

• Expert-level proficiency with C#, .NET, ASP.NET Core, RESTful APIs, service-oriented architecture, modern back-end design patterns and MS SQL Server.

• Proven experience designing and delivering complex, scalable, high-availability enterprise systems.

• Strong understanding of cloud-native development, preferably with AWS or similar platforms.

• Experience with relational and/or NoSQL databases such as PostgreSQL, MongoDB, or similar technologies.

• Strong knowledge of distributed systems concepts, including messaging, queues, event-driven architecture, resiliency patterns, caching, observability, and fault tolerance.

• Hands-on experience integrating AI, machine learning, LLMs, or automation capabilities into production or near-production software systems.

• Familiarity with modern AI concepts such as LLMs, prompt engineering, RAG, vector embeddings, MCP, semantic search, model orchestration, AI agents, evaluation frameworks, and responsible AI practices.

• Experience working in a regulated, compliance-heavy, or security-sensitive industry such as healthcare, financial services, insurance, legal tech, government, life sciences, energy, or enterprise SaaS.

• Strong understanding of secure software development practices, identity and access management, data privacy, encryption, logging, auditability, and compliance-aware architecture.

• Demonstrated ability to lead technical direction across teams without requiring formal people-management authority.

• Excellent communication skills with the ability to explain complex technical and AI concepts to engineers, product leaders, executives, security teams, and compliance stakeholders.

• Preferred Qualifications

• Experience with AWS Bedrock, OpenAI APIs, Microsoft Agent Framework, Microsoft Foundry, LangChain, LlamaIndex, SpecKit, vector databases, or comparable AI orchestration and retrieval frameworks.

• Experience building AI workflows that include human-in-the-loop review, audit trails, confidence scoring, explainability, model evaluation, or risk-based controls.

• Experience designing systems that comply with frameworks or standards such as SOC 2, HIPAA, FERPA, HITRUST, PCI, GDPR, ISO 27001, NIST, or similar regulatory expectations.

• Experience with domain-driven design, clean architecture, hexagonal architecture, event sourcing, CQRS, or other enterprise architecture patterns.

• Expe