Principal AI-Native Full-Stack Architect
Spektra Systems
Télétravail
- Lieu
- Remote (United States)
- Horaires
- Full Time
- Publié
- 7 oct. 2026
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Description du poste
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This is a remote position.
Location: Remote across India; may transition to onsite in the futureExperience:10+ years in software, including 3+ as an architect or tech lead on multi-tenant SaaS, and atleast 6+ months shipping production software mainly through AI coding agents.
The role.You design the Spektra’s SaaS modular platforms and the AI-driven engineering system that builds it. You own the boundaries between the .NET/Angular core and the Next.js/React/Python modules, the contracts between them, and the specs, templates and guardrails our coding agents work from. You lead by writing specs and reviewing output, not by typing code: every implementation is generated by agents and verified by you and your team.
What you'll do
Platform architecture
Own the target architecture: core services (identity, tenancy, entitlements, lab engine, learner record) and independently deployed modules.
Define module contracts: manifests, APIs, events, shared header/shell, branding, and the per-module data boundary (own schema/database, no cross-module reads).
Make and record decisions as ADRs: edge routing (Azure Front Door), identity (B2C / Entra External ID, server-side sessions, per-module app registrations), data, and hosting.
Plan the safe migration off legacy V1 services onto vNext, one path or feature at a time, with rollback at every step.
The agent-driven engineering system
Design how work flows from idea to production with agents: spec → plan → task breakdown → agent implementation → automated checks → human review → deploy.
Own the standing context agents load every session:AGENTS.md/CLAUDE.md, architecture rules, coding standards, skills, MCP servers and hooks. Review changes to them like code.
Maintain the golden module template (our TaskBoard reference module) so a new module goes from scaffold to QA in days.
Set the automated guardrails agents must pass: typecheck, lint, dependency-boundary rules, contract tests, row-level-security tests, secret scanning, conformance checks.
Decide which agent, model and mode fits which task, and track cost, speed and defect rates per workflow.
Quality, security and operations
Review the highest-risk agent changes yourself: authentication, authorization, tenant isolation, payments/credits, infrastructure and data migrations.
Threat-model new modules and AI features (prompt injection, credential leakage, abuse of lab environments).
Define SLOs, observability and incident practice for the platform; make sure every environment can be rebuilt from infrastructure-as-code (Bicep/Terraform).
AI product architecture
Architect AI-powered product features such as the AI Trainer (realtime voice, screen understanding, lab validation tools) and Lab Studio (AI-generated labs, guides and checks).
Choose and integrate model providers (Azure OpenAI, Anthropic and others), with evaluation suites, guardrails and cost controls.
Leadership
Coach developers on spec writing, agent orchestration and critical review.
Work with product, the .NET core team and partners on roadmap and trade-offs; explain them clearly to non-engineers.
Requirements
Must have
Deep, current experience with both halves of our stack: ASP.NET Core/C#/SQL Server and Angular on one side; TypeScript, React/Next.js and Python on the other. You can read any of it fluently and spot what an agent got wrong.
Proven architecture of multi-tenant SaaS: tenant isolation, RBAC/entitlements, white-labelling, API versioning.
Strong identity and security fundamentals: OAuth 2.0/OIDC, Azure AD B2C or Entra, session design, OWASP Top 10, secrets management.
Azure in production: Container Apps or AKS, App Service, Front Door or App Gateway, Key Vault, managed identities, Bicep or Terraform.
Demonstrated daily use of AI coding agents (Claude Code, Codex, Cursor agent mode, Copilot agents or similar) to ship production systems, including multi-agent/parallel workflows.
Experience writing specs and acceptance criteria precise enough that an agent can build from them, and standing instructions that keep a codebase consistent across many agent sessions.
Clear written English: ADRs, specs and review comments are your main output.
Nice to have
Built MCP servers, agent skills or custom agent tooling; experience with the Claude Agent SDK, OpenAI Agents SDK or similar.
Built LLM products in production: RAG, tool use, realtime voice (WebRTC), evaluations.
Learning-platform or lab-platform domain: LMS, LTI 1.3, assessments, cloud sandboxes, Guacamole/RDP consoles.
Migrated a large legacy platform incrementally (strangler pattern).
Microsoft, AWS or GCP architect certification.
Originally posted on Himalayas