Technical Lead _ LivePerson _Conversational AI
Tech Mahindra
Job Description
The Technical Lead is responsible for end-to-end technical ownership and delivery of Conversational AI solutions on the LivePerson Conversational Cloud , with a strong focus on LLM-powered, GenAI-led customer experience transformation . The role ensures scalable, secure, and enterprise-grade implementations , aligned with Tech Mahindra delivery standards and client business outcomes. Key Responsibilities 1.
Technical Leadership Own technical architecture and design for LivePerson-based conversational solutions , including messaging, voice, and agent assist use cases. Lead implementation of LLM-driven conversational experiences , including intent-less flows, contextual conversations, and AI-assisted agent workflows. Translate business and CX requirements into robust, scalable technical designs aligned with platform best practices. 2.
LivePerson Platform Delivery Hands-on development and configuration on LivePerson Conversational Cloud , including bots, integrations, APIs, and orchestration logic. Implement omnichannel conversational journeys across Web, WhatsApp, Mobile, Voice, and Social channels . Drive platform optimization, performance tuning, and feature enablement. 3.
LLM / GenAI Enablement Design and implement LLM orchestration patterns including prompt engineering, grounding, contextual memory, and RAG-based integrations. Enable GenAI features such as agent co-pilot, summarization, intent discovery, and smart routing. Ensure responsible AI usage by applying hallucination controls, fallback strategies, and human-in-the-loop mechanisms. 4.
Enterprise Integrations Design and build integrations with CRM, ITSM, ERP, and backend systems (Salesforce, ServiceNow, SAP, databases, internal APIs). Use REST APIs, webhooks, and middleware to enable seamless data exchange and workflow automation. 5. Security, Compliance & Governance Ensure adherence to enterprise security standards , including PII masking, RBAC, encryption, logging, and audit trails.
Work closely with InfoSec, Architecture Review Board (ARB), and Compliance teams to secure approvals. Align implementations with GDPR, ISO, and client-specific compliance requirements . 6. Delivery & Team Management Lead and mentor onshore/offshore technical teams across design, build, test, and deployment phases.
Drive Agile delivery practices including sprint planning, code reviews, UAT support, and production releases. Provide technical inputs for estimations, RFP responses, solution demos, and client presentations . 7. Operations & Continuous Improvement Own post-production support strategy including hypercare, issue resolution, and performance monitoring.
Drive continuous improvement through platform enhancements, automation ideas, and LLM roadmap alignment. Key Skills & Competencies Mandatory Skills Strong hands-on experience with LivePerson Conversational AI platform Experience with LLMs / GenAI technologies (OpenAI / Azure OpenAI / enterprise LLM APIs) Conversational design, intent modeling, and dialog orchestration REST APIs, JSON, Webhooks Agile delivery and DevOps collaboration Technical Skills Programming/Scripting: JavaScript, Python, .NET (preferred) Cloud exposure: Azure (preferred) Tools: Git, Jira, ADO, CI/CD pipelines Behavioural & Leadership Skills Strong client-facing and stakeholder management skills Ability to mentor teams and drive technical excellence Problem-solving mindset with high ownership and accountability Strong communication and documentation skills Experience & Qualification 5–8 years of experience in Conversational AI / Chatbot development Minimum 3+ years in a Technical Lead / Solution Lead role Engineering degree (BE / BTech / MCA or equivalent) Good to Have Experience across multiple CAI platforms (Yellow.ai, Dialogflow, MS Bot Framework) Exposure to Agent Assist / Co-Pilot solutions Prior experience in enterprise CX transformation programs Key Deliverables LivePerson LLM-based bot solutions delivered on time and within scope Secure, compliant, and scalable conversational architectures High customer satisfaction and measurable CX/business outcomes Reusable accelerators, best practices, and documentation