Technical Hands-on experience architecting and designing agentic AI solutions (multi-agent orchestration, tool-use/MCP-based integrations, LangGraph/CrewAI/AutoGen or equivalent frameworks) — minimum 6–12 months recent, production-grade experience. Architecting scalable, secure, multi-tenant solutions for enterprise customers, with working knowledge of at least one major cloud platform (AWS/GCP/Azure). Ability to perform in-depth code reviews and mentor architects, senior engineers, and engineers across the stack. RAG and knowledge-system architecture (vector DBs, multi-corpus retrieval, embeddings pipelines) — a plus, given increasing customer demand. Ownership of deliverable quality — from design review through production readiness (testing, monitoring, observability). Working knowledge of LLM evaluation/benchmarking (accuracy, latency, cost, quality metrics) to guide model/tool selection decisions. AI-first SDLC approach: using AI/agentic tools (Claude Code, Copilot, etc.) natively across the development lifecycle—code generation, code review, test generation, security scanning,



and remediation—not as an add-on but as the default way of working. Ability to define and enforce AI-assisted engineering guardrails (checklists, review gates, prompt/agent standards) so AI-first development doesn't compromise code quality or security posture. Functional / Delivery Delivery ownership — timely, high-quality delivery with zero compromise on either axis. Deep product knowledge, with the ability to conceive, prioritize, and ship new features that differentiate the product. Agility in converting ad-hoc/one-off customer requirements into reusable, scalable product features rather than one-off patches. Capability to deliver production-grade systems with proper monitoring, alerting, and incident response built in — not just "feature complete." Requirements gathering and translation — running feedback sessions with teams/customers and converting findings into explicit technical requi

📌 Sr. Architect (India)
🏢 OpsMx
📍 India