ncontracts - Software Architect

• This is a highly hands-on role. You'll spend significant time in the codebase, implementing patterns, reviewing code, pairing with developers, and ensuring that day-to-day technical decisions, including AI feature design, align with our architectural standards. You're not just advising; you're building. • You'll float across teams with focused assignments. Rather than spanning the entire organization like our Solution Architects, you'll work with teams on specific initiatives or problem areas, helping implement a new integration pattern, supporting a modernization effort, standing up an LLM-powered capability, or ensuring consistency across a set of related services. Your scope is narrower, but your impact is direct and tangible. • This is a growth role. You'll work closely with our Enterprise Architect and Solution Architects, learning how architectural decisions are made at the strategic level, including decisions around AI adoption, model selection, and responsible AI practices, while building the hands-on experience that makes those decisions credible. • Architectural Implementation • Implement architectural patterns and standards defined by the Enterprise Architect and Solution Architects, including patterns for integrating LLMs and other AI components into existing services • Work within engineering teams to ensure solutions, including AI-powered features, align with established architecture decisions • Translate high-level architectural guidance into concrete implementation approaches • Identify implementation challenges early and escalate to Solution Architects when decisions need to be revisited • Validate that completed implementations meet architectural requirements through hands-on review • AI & LLM Implementation • Design and implement patterns for integrating LLMs into product features, including prompt design, context management, and retrieval-augmented approaches • Apply prompt engineering techniques to build reliable, maintainable AI-driven features, and iterate on prompts based on observed model behavior • Use LLM observability tooling such as Langfuse to trace, evaluate, and debug AI workflows in development and production • Help define and apply evaluation approaches for AI features, covering accuracy, latency, cost, and safety considerations • Partner with Solution Architects and Enterprise Architect on standards for responsible and secure use of AI across the platform • Hands-On Technical Work • Write code regularly, this role requires significant time in the codebase implementing patterns and working alongside developers • Conduct detailed code reviews focused on architectural alignment, code quality, and maintainability • Pair with developers to work through complex implementation challenges, including those involving LLM integrations • Build and maintain reference implementations, including AI feature reference implementations, that teams can use as starting points • Prototype approaches to validate architectural decisions before broader rollout • Team Collaboration • Embed with engineering teams on focused assignments based on architectural priorities • Build strong working relationships with developers and engineering leads • Participate in technical discussions, bringing architectural perspective to implementation decisions • Help teams understand the "why" behind architectural standards, not just the "what" • Surface patterns and challenges observed in teams back to Solution Architects and Enterprise Architect • Learning & Development • Work closely with the Enterprise Architect and Solution Architects to understand how strategic architectural decisions are made • Seek feedback on architectural thinking and implementation approaches • Study our existing architecture, understanding the rationale behind current patterns and the history of key decisions • Develop broader perspective across the platform by working with multiple teams over time • Grow technical breadth across domains, including integration, data, cloud infrastructure, and applied AI, through hands-on experience • Documentation & Communication • Document implementation patterns and approaches that teams can follow, including AI implementation patterns • Contribute to architecture decision records (ADRs) by capturing implementation learnings • Communicate clearly with developers about architectural expectations and rationale • Raise questions and surface ambiguities in architectural guidance to improve clarity for all teams • Bachelor's degree in Computer Science, Information Systems, or related field