About MyOperatorMyOperator is a Business AI Operator and category leader that unifies WhatsApp, Calls, and AI-powered chatbots & voice bots into one intelligent business communication platform.Unlike fragmented communication tools, MyOperator combines automation, intelligence, and workflow integration to help businesses run WhatsApp campaigns, manage calls, deploy AI chatbots, and track performance — all from a single no-code platform.Trusted by 12,000+ brands including Amazon, Domino’s, Apollo, and Razor-pay, MyOperator enables faster responses, higher resolution rates, and scalable customer engagementRole SummaryWe’re hiring a Front Deployed Engineer (FDE)—a customer-facing, field-deployed engineer who owns the end-to-end delivery of AI bots/agents. This role is “frontline”: you’ll work directly with customers (often onsite), translate business reality into bot workflows, do prompt engineering + knowledge grounding, ship deployments, and iterate until it works reliably in production.Think: solutions engineer + implementation engineer + prompt engineer, with a strong bias for execution.Responsibilities1. Requirement Discovery & Stakeholder InteractionJoin customer calls alongside Sales and Revenue teams.Ask targeted questions to understand business objectives, user journeys, automation expectations, and edge cases.Identify data sources (CRM, APIs, Excel, SharePoint, etc.) required for the solution.Act as the AI subject-matter expert during client discussions.2. Use Case & Solution DocumentationConvert discussions into clear, structured use case documents, including:Problem statement & goals.Current vs. proposed conversational flows.Chatbot conversation logic, integrations, and dependencies.Assumptions, limitations, and success criteria.3. Customer Delivery OwnershipOwn deployment of AI bots for customer use-cases (lead qualification, support, booking, etc.). Run workshops to capture processes, FAQs, edge cases, and success metrics. Drive the go-live process: requirements through monitoring and improvement.4. Prompt Engineering & Conversation DesignCraft prompts, tool instructions, guardrails, fallbacks, and escalation policies for stable behavior. Build structured conversational flows: intents, entities, routing, handoff, and compliant responses. Create reusable prompt patterns and "prompt packs."5. Testing, Debugging & IterationAnalyze logs to find failure modes (misclassification, hallucination, poor handling). Create test sets ("golden conversations"), run regressions, and measure improvements. Coordinate with Product/Engineering for platform needs.6. Integrations & Technical CoordinationIntegrate bots with APIs/webhooks (CRM, ticketing, internal tools) to complete workflows. Troubleshoot production issues and coordinate fixes/root-cause analysis.What Success Looks LikeCustomer bots go live quickly and show high containment + high task completion with low escalation.You can diagnose failures from transcripts/logs and fix them with prompt/workflow/knowledge changes.Customers trust you as the “AI delivery owner”—clear communication, realistic timelines, crisp execution.Requirements (Must Have)1. 2–5 years in customer-facing delivery roles: implementation, solutions engineering, customer success engineering, or similar.2. Hands-on comfort with LLMs and prompt engineering (structured outputs, guardrails, tool use, iteration).3. Strong communication: workshops, requirement capture, crisp documentation, stakeholder management.4. Technical fluency: APIs/webhooks concepts, JSON, debugging logs, basic integration troubleshooting.5. Willingness to be front deployed (customer calls/visits as needed).Good to Have (Nice to Have)1. Experience with chatbots/voicebots, IVR, WhatsApp automation, conversational AI platforms with at least a couple of projects.2. Understanding of metrics like containment, resolution rate, response latency, CSAT drivers.3. Prior SaaS onboarding/delivery experience in mid-market or enterprises.Working Style & Traits We Value1. High agency: you don’t wait for perfect specs—you create clarity and ship.2. Customer empathy + engineering discipline.3. Strong bias for iteration: deploy → learn → improve.4. Calm under ambiguity (real customer environments are chaotic by default).