Technical Architect

At Viamagus, every engineer works with AI every day - not as a side experiment, but as the core of how we build. We're looking for a Technical Architect who has shipped real systems, led real teams, and already treats AI tools as first-class instruments of engineering. You'll own architecture across all client engagements, mentor a team of 15–20 engineers, and shape how an AI-native consultancy delivers at scale.
Direct access to client CTOs - architecture decisions that actually ship, not slide decks
Work inside an org where Claude Code, Cursor, and LLM-native workflows are the default, not the exception
Lead technical direction on diverse engagements: cloud modernisation, AI product builds, enterprise integrations
Shape Viamagus's engineering standards, internal tooling, and AI practices from the ground up
What you'll do
Design scalable, secure architectures for client engagements and lead technical due diligence on proposals
Drive production readiness: incident management, observability, release processes
Mentor backend, mobile, and cloud engineers - through architecture reviews, code reviews, and retrospectives
Be the technical face to client CTOs: translate business objectives into architecture decisions and escalate risks with proposed mitigations
Enforce security-first design including threat modelling, data classification, and AI-specific risks (prompt injection, PII leakage)
Ensure compliance readiness for ISO 27001, SOC 2, and HIPAA where applicable
What you bring
Engineering foundation
8+ years of software development, 3+ years in an architect or lead role; degree in CS/Engineering (Master's preferred)
Built and owned systems from scratch to production - full lifecycle, not slices
Multiple integration experiences: third-party APIs, enterprise systems (SAP, Salesforce, ERP), messaging buses, legacy modernisation
Built reusable platforms, SDKs, and internal tooling adopted across teams
Technology-agnostic: strong in at least one modern backend stack, one frontend framework, and one cloud platform
AWS or Azure architecture: VPC design, IAM, container orchestration, cost optimisation
DevOps fluency: Docker, Jenkins/GitHub Actions, Terraform or CDK
Performance tuning, distributed tracing, APM tools (Datadog, New Relic, or equivalent)
AppSec fundamentals: OWASP Top 10, VAPT remediation, secrets management; ISO/SOC 2/HIPAA exposure a plus
AI-era fluency (working knowledge of most of these required)
Daily use of AI coding tools - Claude Code, Cursor, Copilot, or equivalent - and the ability to articulate where they help and where they fall short
LLM integration patterns: OpenAI, Anthropic, Gemini, or open-source models; streaming, function calling, structured outputs
RAG fundamentals: vector DBs (pgvector, Pinecone, Qdrant), embeddings, chunking, retrieval tradeoffs
Agentic systems: tool use, multi-step agents, LangGraph or CrewAI
Prompt engineering: versioning, structured outputs, guardrails, handling hallucinations
AI evaluation and cost awareness: measuring quality, latency, and cost of LLM-powered features
MCP (Model Context Protocol): awareness of what it is and where it fits
Nice to have
Open-source contributions or published AI tooling
Real-time sync experience: CRDTs, Realm, Ditto, or offline-first architectures
Technical writing - blogs, conference talks, or public GitHub work
Google, AWS, or Azure certifications (a bonus, not a substitute for depth)

Work arrangement
No