About Textlayer
TextLayer helps enterprises and funded startups deploy advanced AI systems without rewriting their infrastructure. We work with organizations across fintech, healthtech, and other sectors to bridge the gap between AI potential and practical implementation.
Our approach combines deep technical expertise with proven frameworks to accelerate development and ensure production-ready results. From bespoke AI workflows to agentic systems, we help clients adopt AI that actually works in their existing tech stacks.
We're on a mission to help address the implementation gap that over 85% of enterprise clients experience in adding AI to their operations and products. We're looking for sharp, curious people who want to meaningfully shape how we build, operate, and deliver.
If you're excited to work on foundational AI infrastructure, solve complex problems for diverse clients, and help define what agentic software looks like in practice, we'd love to meet you.
The Role
The AI Architect role sits at the centre of everything TextLayer does.
- Align: Architects work directly with both customers and Product leads to define the work to be done. That means evaluating the customer's current stack and capabilities, designing a plan, and getting everyone aligned on a path forward.
- Build: Once a plan is finalized, Architects lead the execution in a player-coach capacity. That often includes hands-on-keyboard work, but also enforcing standards, upskilling teams, and evaluating and communicating risks as the project evolves.
- Grow: Architects then make sure customers have the systems and internal capabilities to monitor and maintain the production system, and help them spot the next highest-value opportunity based on real usage data.
Key Responsibilities
- Engage with customers on the front line, understanding their needs and their existing capabilities to scope a custom solution that is both high-value and feasible.
- Architect and maintain Python-based services using FastAPI for internal and customer-facing AI use cases
- Build and scale secure, well-structured API endpoints that interface with LLMs, vector stores, and agentic tools
- Implement orchestration logic and tool chaining for advanced agent workflows
- Collaborate with frontend, AI, and devops teams to ensure system-wide reliability and observability
- Set up robust test coverage and CI pipelines for backend services
- Contribute to our modular architecture for agents
- Stay current with emerging trends in AI engineering, LLM integrations, and scalable backend systems
- Drive technical strategy and roadmap for AI infrastructure
- Mentor and guide senior and mid-level engineers on architecture and best practices.
What You Will Bring
- A bias for getting shit done: you wear multiple hats, spot gaps and fill them without being asked, and take accountability for things outside your lane. At the same time, you have the patience to navigate complex enterprise environments.
- You’ll need a deep full-stack development expertise, a strong understanding of modern architecture patterns, and a bias toward building modular, maintainable systems.
- The tact and presence to work directly with customer executives and engineering leads: you can run a discovery conversation, push back on scope with diplomacy, and translate technical trade-offs into business terms.
- A player-coach mindset: you're as comfortable shipping production code as you are reviewing someone else's, setting standards, and leveling up the engineers around you.
- Sound judgment under ambiguity: customer environments vary wildly, and you can assess an unfamiliar stack quickly, identify what matters, and commit to a pragmatic path forward.
- An ownership mentality that extends past delivery: you care whether the system is monitored, maintained, and actually used, not just whether it shipped.
Required Qualifications
- 8+ years of software engineering experience, including 3+ years designing and building scalable backend systems in Python
- Proven experience owning architecture for production systems end to end: design, build, deployment, and operation
- Strong knowledge of FastAPI (or similar frameworks) for building production-grade APIs
- Hands-on experience shipping LLM-powered features: orchestration frameworks (e.g., Pydantic AI), OpenAI/Anthropic API integration, and RAG patterns
- Experience working directly with customers or non-technical stakeholders to scope solutions and communicate trade-offs
- Track record of leading technical initiatives and influencing architecture across teams
- Comfortable with modern delivery tooling: GitHub, Docker, CI/CD (e.g., GitHub Actions), and automated testing
Bonus Points
- Prior consulting or agency experience delivering software into someone else's environment and codebase
- Startup or scale-up experience building for enterprise customers: you've moved fast on a small team while working within the realities of large organizations
- Experience designing multi-tenant or platform-level AI infrastructure
- You've built your own agentic system end to end, professionally or as a side project
- Deep experience with AWS, GCP, or Azure at scale, especially designing for cost and security
- Familiarity with LLM observability and evaluation tooling (e.g., Langfuse, LangSmith, Braintrust)
- Contributions to open-source AI/ML projects
Don't Meet These?
We hire for great developers - not just those with extensive backgrounds in AI. If you've held Staff or Principal roles within your organization and want to learn AI, we want to meet you!