Senior Architect - Conversational AI

Sr AI Architect – CX

Role Overview

We are looking to onboard an AI Architect for CX workloads to lead the design and implementation of enterprise-grade conversational, generative, and agentic AI solutions. This role is responsible for architecting scalable, secure, highly available chatbot, voicebot, IVR, and AI agent systems, integrating LLMs and enterprise platforms, and driving intelligent automation initiatives across Contact Center and Customer Experience (CX) environments.

The ideal candidate combines deep Conversational AI, Contact Center, Agentic AI, and enterprise architecture expertise with strong system design capabilities and a research-driven mindset to solve complex enterprise challenges. The candidate should have experience taking AI solutions from concept and prototype through production at enterprise scale, including modernization and migration of existing Contact Center ecosystems.

Key Responsibilities

Conversational AI & GenAI Architecture

● Design and architect enterprise conversational systems including chatbots, voicebots, IVR automation, AI agents, and agent-assist solutions.

● Define conversational AI architecture standards including intent and interaction modeling, entity strategy, context management, fallback handling, escalation, human handoff, and multi-channel experiences.

● Lead implementation of GenAI and Agentic AI solutions using LLMs, Retrieval-Augmented Generation (RAG), tool calling, prompt orchestration, memory, multi-agent workflows, and autonomous/semi-autonomous agent patterns.

● Design production-grade agent architectures with appropriate guardrails, permission controls, human-in-the-loop mechanisms, tool governance, and failure-handling strategies.

● Establish prompt engineering best practices, response evaluation frameworks, AI safety guardrails, and continuous evaluation strategies.

● Drive continuous improvement of conversational and agent performance through analysis of historical conversations, customer behavior, operational data, and AI evaluation results.

● Define performance benchmarks for response accuracy, task completion, latency, cost, reliability, and overall customer experience.

Contact Center Transformation & Modernization

● Lead architecture for large-scale Contact Center migrations, modernization, and transformation initiatives involving legacy IVR, telephony, CCaaS, conversational AI, agent-assist, and enterprise backend systems.

● Define migration strategies covering current-state assessment, target architecture, coexistence, phased migration, cutover, rollback, and decommissioning of legacy platforms.

● Architect integrations across CCaaS platforms, telephony/SIP environments, AI platforms, enterprise applications, APIs, and data platforms.

● Evaluate existing Contact Center capabilities and identify opportunities for AI-led automation, self-service, agent augmentation, and operational optimization.

● Ensure solutions support enterprise requirements around scalability, availability, security, compliance, observability, and business continuity.

Agentic AI & Production Engineering

● Architect and implement production-scale Agentic AI systems capable of reasoning, tool execution, workflow orchestration, decision-making, and controlled autonomous actions.

● Define agent architecture patterns including single-agent, multi-agent, orchestrated, event-driven, and human-in-the-loop workflows.

● Establish standards for agent tools, APIs, schemas, authentication, authorization, retries, timeouts, state management, memory, observability, and cost controls.

● Design mechanisms for agent evaluation, traceability, hallucination mitigation, prompt injection protection, policy enforcement, and safe tool execution.

● Drive transition of Agentic AI prototypes and POCs into production-ready enterprise solutions.

System & Solution Design

● Own end-to-end solution architecture (HLD/LLD) for distributed, real-time, conversational, GenAI, and Agentic AI applications.

● Design scalable, event-driven, and API-based integration frameworks with enterprise systems.

● Architect resilient, fault-tolerant systems with clear HA/DR strategies, failover mechanisms, and disaster recovery considerations.

● Design secure and scalable backend services using JavaScript/Node.js or Python, including APIs, microservices, webhooks, asynchronous processing, and integration services.

● Apply appropriate SQL and NoSQL database technologies for transactional, conversational, state, memory, analytics, and operational workloads.

● Evaluate architectural trade-offs and make data-driven technical decisions considering performance, scalability, security, maintainability, latency, and cost.

● Produce architecture documentation, HLD/LLD, design artifacts, technical standards, architecture decision records, and implementation guidelines.

Problem Solving & Research

● Analyze complex business and technical problems and translate them into scalable AI and CX solutions.

● Conduct research on emerging LLM, Agentic AI, AI orchestration, AI evaluation, AI safety, and Contact Center technologies and evaluate their enterprise applicability.

● Prototype and validate innovative approaches to improve conversational intelligence, automation, customer experience, and operational efficiency.

● Perform root cause analysis for production issues and drive long-term architectural improvements.

● Stay current with advancements in Agentic AI architectures, AI safety, evaluation methodologies, LLM platforms, multi-agent systems, and enterprise AI engineering.

Engineering Leadership

● Provide technical mentorship and architectural guidance to engineering teams.

● Promote best practices in coding standards, quality engineering, CI/CD, observability, security, and Agile methodologies.

● Collaborate with business, product, engineering, and cross-functional teams to align technical strategy with organizational goals.

● Support engineering teams in adopting modern GenAI and Agentic AI development practices and production engineering standards.

Required Qualifications

● 8+ years of experience in software engineering, solution architecture, or system architecture.

● Strong Conversational AI / Contact Center domain understanding, including chatbot, voicebot, IVR, CCaaS, agent-assist, and customer experience transformation.

● Proven experience in large-scale Contact Center migration, modernization, or transformation programs.

● Hands-on experience designing and implementing GenAI and Agentic AI solutions at production scale.

● Strong understanding of LLM application architecture including RAG, prompt engineering, tool calling, agent orchestration, memory, evaluation, and AI safety.

● Proven expertise in distributed systems and scalable architecture design.

● Strong backend development experience in JavaScript/Node.js or Python, including APIs, microservices, webhooks, and enterprise integrations.

● Experience working with SQL and NoSQL databases and selecting appropriate data storage patterns for enterprise applications.

● Hands-on Cloud experience in GCP, AWS, or Azure.

● Strong understanding of system design principles including scalability, reliability, availability, security, observability, performance, and cost optimization.

● Strong analytical thinking and structured problem-solving skills.

● Research-oriented mindset with the ability to evaluate emerging technologies and translate them into practical enterprise solutions.

● Excellent communication, stakeholder management, and technical leadership skills.

● Experience mentoring and leading small technical teams.

Good to Have

● Exposure to GTM activities, solutioning, pre-sales, client workshops, technical proposals, and estimation.

● Experience supporting L&D, mentoring, and technical upskilling programs for engineering teams.

● Experience building AI accelerators, reusable frameworks, POCs, reference architectures, and capability initiatives.

● Exposure to Analytics, Contact Center Insights, conversation analytics, operational analytics, or AI-driven business intelligence.

● Experience defining or implementing AI evaluation frameworks, quality metrics, observability, and continuous improvement mechanisms.

● Exposure to multi-agent architectures, AI orchestration frameworks, and enterprise AI platforms.

● Experience working with cross-functional teams across Product, Engineering, Data, Cloud, Security, and Business functions.