Solution Architect

About The Role

As an AI Solution Architect at Leena AI, you will own the technical and functional blueprint for enterprise AI deployments - translating customer business problems into scalable AI agent solutions built on our low-code/no-code platform. You'll sit at the intersection of client-facing solutioning and hands-on solution design: leading discovery, architecting the solution, configuring AI assistants and agentic workflows, and guiding implementation teams through delivery. This is not an infrastructure or cloud-ops role - it's a design, configuration, and client-advisory role for someone who thinks in prompts, workflows, integrations, and business outcomes rather than servers and pipelines.

Key Responsibilities

Solution Design & Architecture

Lead discovery workshops and technical design sessions with enterprise stakeholders to understand business processes across HR, IT, Finance, and Enterprise Operations Translate business requirements into end-to-end AI agent solution designs — conversation flows, workflow automations, and integration architecture Architect and document solution blueprints, data flows, and configuration specs that implementation teams can build against Own the technical narrative in client conversations — presenting solution designs, defending architectural choices, and adjusting scope with stakeholders in real time

AI, LLM & Agent Configuration

Design and configure AI assistants and agentic workflows using Leena AI's low-code/no-code platform - prompt design, tool/agent orchestration, and guardrails Customize LLM-powered experiences for customer-specific use cases, balancing accuracy, latency, and cost Configure multi-step agent workflows (approvals, escalations, human-in-the-loop) using in-house no-code/low-code tooling rather than custom backend builds Continuously refine prompts, knowledge sources, and agent logic based on evaluation results and production feedback

Enterprise Integrations & Delivery Oversight

Design integration architecture with enterprise systems (Workday, ServiceNow, SAP, Oracle, UKG, SuccessFactors, Microsoft Entra ID/AD, Teams, Slack, Salesforce, Jira, Zendesk) using REST/GraphQL APIs, Webhooks, OAuth 2.0/OIDC, SAML 2.0, and SCIM Drive UAT, go-live readiness, and hypercare, resolving design-level issues that surface during rollout Identify solution risks (scope, integration, adoption) early and propose mitigations

Technical Leadership & Client Advisory

Act as the senior technical point of contact for strategic accounts and complex, multi-system implementations Support pre-sales solutioning - scoping feasibility, effort, and architecture for prospective deployments Mentor on solution design practices, agent architecture patterns, and platform best practices. Build reusable solution templates, agent design patterns, and implementation accelerators to speed up future rollouts Stay current on LLM, RAG, and agentic AI developments and bring relevant patterns into customer solutions. Drive team performance and development - establish priorities, resolve technical bottlenecks, and lead regular design and code reviews for Solution Consultants and FDEs. Guide complex engagements hands-on - partner with team members throughout discovery and configuration to sharpen their solutioning capabilities, maintaining ultimate accountability for execution quality.

Required Qualifications

B.Tech in Computer Science / Information Technology from a Tier 1 or Tier 2 Engineering college or Software Engineering background preferred. 6+ years of experience in solution consulting, implementation, or technical architecture within enterprise SaaS, with 2+ years designing AI/LLM-powered or conversational AI solutions Hands-on experience with LLM applications — prompt engineering, RAG, AI agents, tool calling, and evaluation/guardrails Proven experience designing and configuring solutions on workflow builders, conversation designers, automation tools. Strong client-facing track record: leading discovery workshops, presenting architecture to senior stakeholders, and managing scope conversations Working knowledge of enterprise integration patterns - REST/GraphQL APIs, Webhooks, OAuth 2.0/OIDC, SAML 2.0, SCIM and enterprise systems such as Workday, ServiceNow, SAP, or Oracle etc. Excellent communication, documentation, and stakeholder management skills Experience with AI frameworks/ecosystems such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar (as a consumer/configurer, not necessarily a framework-level builder) Familiarity with MCP (Model Context Protocol), multi-agent systems, and AI orchestration patterns Experience leading solutioning for large, multi-stakeholder enterprise implementations.

Skills: llm,solution architect,forward deployed engineering