Software Architect

Ncontracts

20d•$135,000 - $155,000

About The Position

We are seeking a Software Architect with a strong AI focus to work alongside engineering teams, helping implement architectural patterns and standards defined by our Enterprise Architect and Solution Architects. This role focuses on execution, translating architectural decisions, including those governing our AI and LLM-powered features, into working software while developing the skills and perspective needed to grow into broader architectural responsibilities. 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.

Requirements

4+ years of progressive experience in software engineering, with demonstrated technical leadership or senior developer experience
Strong hands-on coding skills, you're an experienced developer who writes production code regularly
Solid experience with at least one of our core languages/frameworks: .NET/C#, React, or Python
Hands-on experience building with large language models (LLMs) in production or near-production settings
Practical experience with prompt engineering, including designing, testing, and iterating on prompts for reliability and consistency
Experience with LLM observability or evaluation tooling such as Langfuse (or comparable platforms)
Familiarity with cloud platforms, particularly Azure or AWS
Understanding of modern application architectures: microservices, API design, event-driven patterns
Experience with code review practices and ability to provide constructive, actionable feedback
Ability to work across different teams and adapt to varying codebases and contexts
Strong communication skills, you can explain technical concepts clearly to other developers
Curiosity about the "big picture," you want to understand how systems fit together, not just your piece
Eagerness to learn and grow into broader architectural responsibilities
Bachelor's degree in Computer Science, Information Systems, or related field

Nice To Haves

Experience with our tech stack: .NET/C#, React, Python, Snowflake, dbt
Experience integrating LLM APIs (OpenAI, Anthropic, or similar) into application code
Familiarity with retrieval-augmented generation (RAG), vector databases, or agentic workflow patterns
Familiarity with our toolchain: TeamCity, Octopus Deploy, Azure DevOps, GitHub Actions
Exposure to integration patterns: REST APIs, message queues, CDC
Experience with database design and data modeling concepts
Background in DevOps practices and CI/CD pipelines
Experience working in multi-tenant SaaS environments
Prior experience in B2B SaaS or enterprise software development

Responsibilities

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
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
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
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
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
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

Benefits

Responsible PTO Plan that meets or exceeds state and local medical and family leave laws
11 paid holidays
Community and social events to keep you connected and engaged
Mental Health Benefits
Medical, Dental and Vision insurance
Company-paid Group Life Insurance, Short- and Long-Term Disability
Flexible Spending Account & Health Savings Account
Aflac Benefits – Critical Illness, Cancer Protection, & Hospital Choice
Pet Insurance
401 (k) with company match with eligibility on Day 1 of employment
2 Paid Volunteer Time Off Days