Technical Architect

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, HIPAA, where applicable
• Identify and mitigate delivery risks early; escalate with proposed mitigations

Nice to Have

• Contributions to open-source projects or AI tooling
• Experience with real-time sync (CRDTs, Realm, Ditto) or offline-first architectures
• Published technical content - blogs, talks, GitHub
• Google/AWS/Azure certifications (bonus, not substitute)