EDMO | Conversational Intelligence Platform for Higher Education Location: Pune| Level: Senior | Type: Full-Time

About EDMO EDMO is building the next generation of conversational intelligence for higher education. Our platform empowers universities and EdTech institutions with production-grade agentic AI — enabling intelligent student advisors, enrollment agents, academic support workflows, and enterprise automation. We work with forward-thinking institutions (including flagship US university partners) and are scaling fast. If you love designing AI systems that actually run in production, this role is for you.

Role Overview As the Agentic AI Architect at EDMO, you will own the end-to-end technical design and architecture of our agentic AI systems. You will be the bridge between research-grade AI ideas and production-hardened implementations — defining how LLM pipelines, multi-agent orchestration, memory systems, and tool-use come together into a coherent, scalable platform.

This is not a research or prototyping role. You will design systems that run at enterprise scale, handle real student interactions, integrate with university SIS/CRM platforms, and meet compliance standards (FERPA, SOC 2). Equally important, you will be able to communicate complex architecture decisions clearly to engineers, product stakeholders, and enterprise clients.

What You'll Own System Architecture & Design

• Define the end-to-end agentic AI architecture — LLM orchestration layers, agent coordination mechanisms, memory management, tool-calling patterns, and multi-agent workflows
• Design multi-agent orchestration patterns including hierarchical agents, supervisor-worker topologies, plan-and-execute strategies, and agent-to-agent (A2A) communication
• Architect RAG (Retrieval-Augmented Generation) pipelines, hybrid search, and knowledge management systems tailored to higher-education content (catalogs, policies, student data)
• Establish architectural standards for prompt engineering, context-window management, token budgeting, and evaluation frameworks
• Design for non-functional requirements: latency targets, throughput, reliability, observability, cost controls, and scaling guardrails

Production Engineering Standards

• Build production-grade systems with structured error handling, retry logic, circuit breakers, and fallback execution paths
• Define DevSecOps architecture and deployment patterns across multi-cloud environments.
• Implement human-in-the-loop (HITL) checkpoints for sensitive decision points within student journeys
• Establish monitoring, observability, and audit logging frameworks for compliance, traceability, and agent behavior analysis
• Design secure AI architectures — threat modeling, adversarial prompt defenses, and data privacy controls aligned with FERPA and SOC 2

Client & Team Communication

• Produce clear architecture documentation: reference diagrams, integration patterns, decision records, ADRs, and runbooks for production operations
• Communicate architectural trade-offs to both engineering teams and non-technical enterprise clients in a structured, accessible way
• Lead architecture and design reviews, mentoring engineers on agentic patterns and production best practices
• Serve as the technical point of contact for enterprise client integrations — translating institutional requirements into AI system design
• Research & Innovation

You'll Be a Great Fit If You Have Core Technical Requirements

• 5+ years of hands-on software engineering, with 3+ years specifically in production AI/ML systems
• Demonstrated experience designing and shipping multi-agent AI systems in production (not just prototypes or demos)
• Deep expertise with LLM orchestration frameworks: LangChain, LangGraph (preferred), and/or AutoGen, CrewAI, Google ADK
• Strong command of Python and backend engineering fundamentals — APIs, microservices, async patterns
• Hands-on experience with RAG architectures, vector databases, and semantic search
• Proficiency with cloud platforms (AWS, GCP, or Azure) and containerized deployments (Docker, Kubernetes)

Architecture & Systems Thinking

• Ability to design systems addressing latency, scalability, reliability, and cost simultaneously — not just "it works on my local machine"
• Experience with agent memory architectures: episodic, semantic, procedural — and state management in stateful multi-agent graphs
• Familiarity with LLM inference optimization — quantization, model routing, caching, context compression
• Understanding of API gateway design, service boundaries, versioning, and governance

Communication & Leadership

• Ability to produce clear architecture diagrams and technical documentation accessible to both engineers and non-technical stakeholders
• Experience presenting architecture to enterprise clients or senior leadership
• Track record of leading technical design reviews and influencing architecture decisions without direct authority

Nice to Have SkillWhy It Matters at EDMOExperience with voice AI / telephony EDMO has voice agent modules for student engagement Salesforce / CRM integration experienceUniversity CRM and SIS integrations are core to our enterprise deployments Higher education domain knowledgeUnderstanding of admissions, advising, and enrollment workflows accelerates delivery FERPA / SOC 2 compliance experienceRequired for US university enterprise clientsFamiliarity with toolsUsed in select automation workflows (but production AI agents are primary)Experience with for AI data pipelinesCore to EDMO's data layerGoogle experiencePart of our multi-LLM strategy

What You Won't Be Doing This role is explicitly not for:

• Research-only, notebook-bound, or demo-focused ML work
• Low-code/no-code automation tool configuration (Zapier, Make, etc.)
• "Prompt-only" AI work without systems thinking
• Architecture decks that never get built

How We Work

• Tech Stack: Python, LangGraph, LangChain, Google Gemini / Claude / OpenAI (multi-LLM), MongoDB, Databricks, Salesforce integrations,
• Deployment: Production-grade microservices, containerized on cloud with CI/CD pipelines
• Compliance Environment: FERPA, SOC 2, VPAT — security and privacy are first-class concerns
• Team: Small, high-caliber engineering team. Architects write architecture AND contribute to code
• Clients: US-based universities and EdTech platforms at enterprise scale

What Success Looks Like In 30 days:

• Full audit of existing agentic system architecture; documented gaps, risks, and improvement areas

In 60 Days

• Delivered a reference agentic architecture for at least one production agent workflow; established architecture review process with the engineering team

In 6 Months

• Led architecture for a major platform capability (e.g., multi-agent student advisor, autonomous enrollment workflow); communicated architecture to at least one enterprise client; established observability and evaluation framework for agents in production

Compensation & Growth

• Level: Senior Architect
• Opportunity: Direct path to VP of AI as EDMO scales
• Impact: Your architecture decisions shape the AI experience for hundreds of thousands of students at leading US universities
• This role sits at the intersection of applied AI research, enterprise product engineering, and client strategy — rare in the market