Senior AI Lead – Document, Conversational & Data Intelligence
Company: EDMO
Location: Pune
Role Type: Full-time
Role Overview
EDMO is hiring a Senior AI Lead to own and scale our AI stack across document intelligence, conversational intelligence, and data intelligence. You will turn prototypes into robust, production-grade capabilities that integrate deeply with customer systems and meet strict standards for scale, reliability, and security.
You will work closely with product and engineering leadership, lead a small high-performing AI team, and directly influence how universities and institutions experience EDMO's platform.
Key Responsibilities
- AI Strategy & Architecture
- Define and own the end-to-end AI architecture across document, conversational, and data intelligence.
- Design multi-tenant, cloud-native AI services optimized for performance, cost, and reliability.
- Establish standards for prompt engineering, tool/agent orchestration, RAG, fine-tuning, evaluation, and monitoring.
- Translate product requirements into clear technical designs, milestones, and delivery plans.
- Document Intelligence
- Lead design and implementation of document understanding pipelines for transcripts, forms, financial docs, policies, and knowledge bases.
- Build capabilities for OCR, layout analysis, entity extraction, classification, validation, and summarization.
- Implement retrieval-augmented generation over large document corpora, including indexing, chunking, and relevance tuning.
- Define quality metrics and automated evaluation suites to continuously improve accuracy, robustness, and latency.
- Conversational Intelligence
- Own the architecture of production-grade chat and voice assistants across web, mobile, and telephony channels.
- Design agentic workflows combining LLMs, tools/APIs, memory, and business rules to support complex student and staff journeys.
- Implement guardrails, policies, and UX patterns to minimize hallucinations and ensure safe, compliant responses.
- Set up a rigorous evaluation framework for conversation quality, containment, user satisfaction, and escalation performance.
- Customer System Integrations
- Architect and oversee integrations with customer CRMs, SIS, telephony, and data platforms (e.g., Salesforce, contact centers, data warehouses).
- Define API and event-driven integration patterns for both real-time and batch scenarios.
- Ensure AI features respect tenant boundaries, roles/permissions, and customer-specific configurations.
- Partner with solutions/implementation teams to make deployment repeatable, configurable, and maintainable across institutions.
- Data Intelligence & Analytics
- Collaborate with data engineering to design data models and pipelines that power AI features and insights.
- Lead development of models and heuristics for scoring, routing, prioritization, and personalization based on behavioral and conversational signals.
- Define and maintain dashboards and KPIs for AI performance, adoption, and business impact.
- Drive an experimentation culture with A/B tests, staged rollouts, and data-driven iteration.
- Production, Scale & Security
- Ensure all AI services meet enterprise standards for uptime, resiliency, and observability.
- Define and enforce best practices for logging, tracing, alerting, and model/service health monitoring.
- Work with security and compliance teams to align with data privacy regulations, including encryption, access control, and data retention policies relevant to education.
- Implement robust processes for model and configuration versioning, canary deployments, and safe rollbacks.
- Leadership & Collaboration
- Lead and mentor AI/ML engineers, data scientists, and AI application engineers.
- Collaborate closely with product, platform engineering, implementation, and customer success to ensure AI capabilities deliver real outcomes.
- Participate in key customer meetings to understand requirements, shape solutions, and represent EDMO's AI strategy.
- Contribute to hiring, career development, and setting the technical bar for AI roles at EDMO.
Required Experience
- 6–10 years of experience in software engineering and/or applied ML, with at least 4–5 years focused on building AI products.
- Proven track record of taking AI solutions (LLM or traditional ML) from PoC to production in a SaaS or enterprise environment.
- Hands-on experience with:
- Large language models and orchestration (prompting, tools, agents, RAG).
- Document understanding (OCR, layout, extraction, classification, semantic search).
- Conversational AI (chatbots, voicebots, agent assist) with measurable outcomes.
- Strong programming skills in one or more of: Python, Node.js, Java/Scala (or similar).
- Experience designing and operating cloud-native services (containers, CI/CD, infrastructure as code).
- Experience integrating with enterprise systems such as CRMs, telephony platforms, and data platforms.
- Familiarity with security and compliance considerations in regulated industries (education, healthcare, finance, or similar).
