Technical

1. 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.

2. Architecting scalable, secure, multi-tenant solutions for enterprise customers, with working knowledge of at least one major cloud platform (AWS/GCP/Azure).

3. Ability to perform in-depth code reviews and mentor architects, senior engineers, and engineers across the stack.

4. RAG and knowledge-system architecture (vector DBs, multi-corpus retrieval, embeddings pipelines) — a plus, given increasing customer demand.

5. Ownership of deliverable quality — from design review through production readiness (testing, monitoring, observability).

6. Working knowledge of LLM evaluation/benchmarking (accuracy, latency, cost, quality metrics) to guide model/tool selection decisions.

7. 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.

8. 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

1. Delivery ownership — timely, high-quality delivery with zero compromise on either axis.

2. Deep product knowledge, with the ability to conceive, prioritize, and ship recent features that differentiate the product.

3.



Agility in converting ad-hoc/one-off customer requirements into reusable, scalable product features rather than one-off patches.

4. Capability to deliver production-grade systems with proper monitoring, alerting, and incident response built in — not just "feature complete."

5. Requirements gathering and translation — running feedback sessions with teams/customers and converting findings into clear technical requirements documents

6. 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.

7. 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

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

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

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

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

5. 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