Position: Senior AI Lead – Document, Conversational & Data Intelligence
Location: Baner, Pune
Working Days: 5 days
Experience Required: 7+ Years
Role Overview
Our Client 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 Our Client’s platform.
Key Responsibilities
1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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 Our Client’s AI strategy.
Contribute to hiring, career development, and setting the technical bar for AI roles at Our Client.
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).
