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 contemporary 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)

📌 Technical Architect (India)
🏢 viamagus
📍 India