Sr. AI Lead

ESP Engineered; Pune District; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

ESP Engineered is hiring a Senior AI Lead in Pune to own and scale AI solutions across document and conversational intelligence. The role involves leading a team, defining AI architecture, and ensuring AI features are robust and compliant across customer systems. Candidates should have 6-10 years of experience in software engineering and applied machine learning, with a focus on AI product development, and be proficient in relevant programming languages including Python and Node.js. This full-time position offers significant influence in how institutions engage with technology.

Qualifications

• 6–10 years of experience in software engineering and/or applied ML, at least 4–5 years in AI products.
• Proven track record of AI solutions from PoC to production.
• Hands-on experience with large language models, document understanding, and conversational AI.

Responsibilities

• Define and own the end-to-end AI architecture.
• Lead a small high-performing AI team.
• Implement retrieval-augmented generation over large document corpora.
• Collaborate with data engineering to design data models.
• Ensure AI services meet enterprise standards for uptime and resiliency.

Skills

Software engineering

Applied machine learning

Large language models

Document understanding

Conversational AI

Python

Node.js

Java/Scala

Cloud-native services

Job description

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

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.
• 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.
• 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.
• 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.
• 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.
• 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).