About the Role
Viamagus is a fully AI-driven engineering organization. We're hiring a Technical Architect who has built real systems, deployed them to production, and meaningfully integrated AI into their engineering practice. You'll lead architecture across all client engagements.
Must-Have: Engineering Foundation
- Bachelor's in CS/Engineering or related field (Master's preferred)
- 8+ years of software development, 3+ years in an architect/lead role
- Built systems from scratch and taken them to production - owned the full lifecycle, not just slices
- Multiple integration experiences - third-party APIs, enterprise systems (SAP, Salesforce, ERP), messaging/event buses, legacy modernization
- Built frameworks for scalability - reusable platforms, SDKs, shared libraries, and internal developer tooling adopted across teams
- Technology-agnostic strength - strong across at least one modern backend stack, one frontend framework, and one cloud platform; able to pick the right tool for the job rather than defaulting to favourite
- AWS or Azure cloud architecture - VPC design, IAM, container orchestration, cost optimization
- DevOps fluency: Docker, Jenkins/GitHub Actions, IaC (Terraform/CDK)
- Performance tuning, distributed tracing, structured logging, APM tools (Datadog, New Relic, or equivalent)
- AppSec collaboration - OWASP Top 10, VAPT remediation, secrets management, compliance (ISO/SOC 2/HIPAA exposure a plus)
Must-Have: AI-Era Awareness
You will be expected to architect systems that use AI effectively and lead engineers who do the same. Working knowledge of several of these is required:
- AI-assisted development - daily driver of Claude Code, Cursor, Copilot, or equivalent; can articulate where they help, where they fail, and how to get better outcomes from them
- LLM integration patterns - understanding of when to use OpenAI, Anthropic, Gemini, or open-source models; familiarity with API usage, streaming, function calling, structured outputs
- RAG basics — vector DBs (pgvector, Pinecone, Qdrant), embeddings, chunking, retrieval tradeoffs — enough to review and guide RAG implementations
- Agentic systems awareness — conceptual understanding of tool use, multi-step agents, and frameworks like LangGraph or CrewAI
- MCP (Model Context Protocol) — awareness of what it is and where it fits
- Prompt engineering fundamentals — versioning prompts, structured outputs, guardrails, handling hallucinations
- AI evaluation and cost awareness — how to measure quality, latency, and cost of LLM-powered features
- Curiosity and experimentation mindset — has tried things beyond ChatGPT in a browser tab
Responsibilities
Architecture & Delivery
- Design scalable, secure architectures for client engagements
- Lead technical due diligence on proposals - feasibility, effort estimation, risk flagging
- Drive production readiness: incident management, observability, release processes
- Review and approve high-impact design decisions across projects
Team & Stakeholders
- Mentor 15–20 engineers across backend, mobile, and cloud teams
- Conduct architecture reviews, code reviews, and technical retrospectives
- Engage directly with client CTOs/architects on solution design and technical escalations
- Translate business objectives into architectural decisions and vice versa
Quality, Risk, Compliance
- Enforce security-first design - threat modelling, data classification, AI-specific risks (prompt injection, PII leakage, model supply chain)
- Ensure compliance readiness for ISO 27001, SOC 2