Assistant Manager – Customer Interaction Suite

Overview

As Technical Program Manager, you’ll drive the end-to-end delivery of customer onboarding, technical programs, and AI-powered solutions. You’ll bridge business needs with technical execution across product, engineering, and operations to deliver on time, on budget, and with measurable outcomes. This role blends traditional program management with hands-on AI knowledge to orchestrate complex deployments. You’ll shape AI initiatives, from discovery to deployment, and ensure operational excellence across multi-functional teams. You’ll work in a fast-moving, cloud-enabled, AI-driven environment.

Responsibilities

• Manage project initiation, planning, scheduling, execution, monitoring and closure using PM methodologies
• Develop and maintain project plans, milestones, dependencies, resources, and delivery schedules
• Track performance against scope, timeline, budget and quality metrics
• Provide regular status reporting to customers, stakeholders and leadership
• Manage risks, issues, assumptions and dependencies with mitigation plans
• Lead cross-functional meetings with customers, partners, vendors and internal teams
• Document and translate customer requirements into actionable implementation plans
• Drive continuous improvement of PM methodologies, tools, and processes
• Collaborate with Product, Engineering, Solution Architects, and Operations to assess feasibility
• Oversee delivery activities involving APIs, authentication, networking, cloud services, integrations, and workflow automation
• Troubleshoot, escalate, and manage issues during implementation and operations
• Ensure solutions meet customer expectations and business objectives
• Lead end-to-end delivery of Agentic AI and Voice AI initiatives from discovery to post-production optimization
• Define AI solution design activities including workflow, intents, multilingual experiences, and NLP training
• Coordinate development and deployment of AI solutions leveraging RAG, MCP, orchestration frameworks, LLMs, and integrations
• Define prompting strategies, testing frameworks, and evaluation criteria with AI engineers and data teams
• Drive prompt engineering lifecycle management (design, optimization, testing, versioning, performance)
• Establish AI QA practices covering functional testing, hallucination assessment, safety, and UAT
• Define success metrics for AI projects (accuracy, latency, adoption, efficiency, business impact)
• Monitor AI performance post-deployment and drive improvements
• Define AI acceptance criteria and quality metrics for Generative AI (accuracy, relevancy, hallucination rate, safety, user satisfaction)

Key requirements

• customer interaction
• stakeholder management
• communication
• AI program management
• Generative AI
• Agentic AI

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