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 current 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 clear technical requirements documents

Driving AI-first delivery practices across the team — using AI copilots/agents for estimation, sprint planning support, and faster iteration cycles, while instilling the discipline to validate AI-generated output before shipping.

Championing an AI-first mindset in the team — continuously evaluating where AI can compress the SDLC (design, dev, test, deploy, monitor) and driving adoption, not just personal use.

Leadership & Stakeholder Management Cross-functional stakeholder management — working with product, sales/pre-sales, and customer success to align technical roadmap with business priorities.

Pre-sales/proposal support — ability to contribute to RFP responses, solution architecture diagrams, and costing/effort estimation for new engagements.

Team building — mentoring, hiring input, and technical career development for the engineering org.

Executive communication — presenting technical strategy, risks, and trade-offs to leadership in business terms.

Vendor/tool evaluation — assessing third-party AI tools, frameworks, and platforms for build-vs-buy decisions.

Note: Please Note: This role is expected to evolve continuously in line with the company's strategic priorities, business challenges, and growth objectives.

📌 Director / Architect - Artificial Intelligence (India)
🏢 OpsMx
📍 India