Software Architect

We are looking for a Principal Architect — a deeply technical, hands‑on leader who sits at the intersection of functional requirements, system architecture, and pragmatic implementation. This is not a management role. It is a high‑impact individual contributor position for someone who loves writing code, designing systems, and staying ahead of the market curve. You will set the technical direction for our engineering practice, drive architecture decisions from first principles, and act as the team’s primary connection to fast‑moving developments.

You will own how we build apps — not just what we build.

Responsibilities

• Design end-to-end system architectures: APIs.
• Write production-quality code – Python, SQL, API services – across the full lifecycle.
• Define and own technical blueprints for new products and applications – from data ingestion and embedding pipelines through to response generation, evaluation, and production monitoring.
• Solve hard engineering problems, making well‑documented architecture decisions with clear trade‑off analysis (build vs buy, framework selection, deployment topology).
• Build and own reusable, framework‑quality components that accelerate team velocity.
• Prototype rapidly, validate with real data, iterate, and bring proofs‑of‑concept to production‑grade systems with full observability and evaluation harnesses.
• Own CI/CD pipelines, Docker‑based deployment, and production telemetry for AI services.
• Continuously track and evaluate the AI landscape – agentic frameworks (LangGraph, Google ADK, CrewAI, AutoGen), retrieval methods, fine‑tuning techniques, and emerging tooling.
• Establish and enforce engineering standards: code review practices, PR templates, testing frameworks, and reusable component libraries that reduce development‑to‑deployment cycle times.
• Serve as the senior technical reviewer for all designs, providing deep, opinionated feedback that raises the quality bar across the team.
• Pair with engineers on hard problems, conduct design sessions, and create internal reference architectures and runbooks that embed best practices across the team.
• Design and run internal AI upskilling to keep the team sharp on production‑grade development.
• Cross‑functional technical partnership: partner closely with Product, Data Science, and Platform Engineering to align architecture with product direction, data constraints, and infrastructure capabilities; communicate complex technical trade‑offs clearly to non‑technical stakeholders, translating architecture decisions into business impact narratives; work with compliance and legal teams in regulated environments (financial services, cybersecurity) and data‑handling requirements.

Must‑Have Experience

• 12+ years of hands‑on experience in full stack development with significant depth in production system delivery.
• Proven track record architecting and shipping enterprise‑grade applications.
• Expert‑level Python and SQL; fluency with GCP, AWS, Docker, FastAPI, Django, and CI/CD.

Hands‑On Implementation

• Write production‑quality code – Python, SQL, API services – across the full lifecycle.
• Build and own reusable, framework‑quality components that accelerate team velocity.
• Prototype rapidly, validate with real data, iterate, and bring proofs‑of‑concept to production‑grade systems with full observability and evaluation harnesses.
• Own CI/CD pipelines, Docker‑based deployment, and production telemetry for AI services.
• Continuously track and evaluate the AI landscape – agentic frameworks (LangGraph, Google ADK, CrewAI, AutoGen), retrieval methods, fine‑tuning techniques, and emerging tooling.
• Establish and enforce engineering standards: code review practices, PR templates, testing frameworks, and reusable component libraries that reduce development‑to‑deployment cycle times.
• Serve as the senior technical reviewer for all designs, providing deep, opinionated feedback that raises the quality bar across the team.
• Pair with engineers on hard problems, conduct design sessions, and create internal reference architectures and runbooks that embed best practices across the team.
• Design and run internal AI upskilling to keep the team sharp on production‑grade development.