Forward Deployed Engineer

Put AI agents into production with customers: discover the workflow, design the bot, wire the APIs, tune it on live calls.

About the Role

My. Operator is hiring a Forward Deployed Engineer (FDE) / Technical Business Analyst to bridge the gap between business requirements and production-grade Conversational AI deployments. In this customer-facing, delivery-focused role, you will work directly with clients to translate real-world business workflows into structured AI bot architectures.

You will lead requirement discovery workshops, author detailed solution/BRD documentation, execute system prompt engineering, configure webhooks/APIs, run regression testing, and iterate on live conversational logs until containment and resolution targets are met. This role is ideal for Business Analysts with a Computer Science/IT/Engineering background or Solutions/Implementation/Product Engineers who possess both strong stakeholder management capabilities and the technical depth required to debug payloads, write structured LLM prompts, and trace API integration logs.

About My. Operator My. Operator is a Business AI Operator, a category-leader that unifies WhatsApp, Calls, and AI-powered chat & voice bots into one intelligent business communication platform. Unlike fragmented communication tools, My. Operator 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 Razorpay, My. Operator enables faster responses, higher resolution rates, and scalable customer engagement — without fragmented tools or increased headcount Key Responsibilities & Observable Work Outputs Requirement Discovery & Business Process Mapping Lead discovery workshops with client business and technical teams; translate ambiguous business processes into structured conversational user flows, intent trees, fallback policies, and Functional Requirement Documents (FRDs/BRDs).

AI Solution Specification & Architecture Convert client use cases into technical specifications, detailing system integration points (CRM, ERP, ticketing), API payload requirements, database parameters, and data security constraints. Prompt Engineering & Knowledge Grounding Write, test, and refine system prompts, context instruction sets, guardrails, and tool/function call definitions to enforce deterministic bot behaviour and eliminate hallucinations.

API Integration & Technical Debugging Configure REST API webhooks between My. Operator's platform and third-party systems (e.g., Salesforce, Zoho, Lead. Squared, internal databases); inspect and debug JSON request/response logs to resolve integration breakages. Testing, Log Analysis & Containment Iteration Conduct transcript reviews of live bot interactions; build "golden conversation" test sets, run regression cycles, and optimize bot containment and task completion rates post-go-live.

Cross-Functional Technical Alignment Act as the technical voice of the customer to internal Product and Core Engineering teams—documenting platform feature gaps, API limitations, and core bugs with reproducible logs. Candidate Profile & Screening Criteria

1. Must-Have Requirements Education & Technical Foundation: Bachelor’s Degree in Engineering, Computer Science, Information Technology, or a related technical/STEM discipline (B.E. / B.Tech / BCA / MCA / B.Sc IT). Total Experience: 2 to 5 years of experience operating as a Technical Business Analyst, Forward Deployed Engineer, Solutions Engineer, Implementation Consultant, or Technical Product Analyst in a B2B SaaS, software, or IT services firm. Technical Documentation: Demonstrated experience writing technical use-case documents, Business Requirement Documents (BRDs), Process Flow Diagrams (Visio/Lucidchart/Miro), or API specification documents. API & Data Literacy: Hands-on ability to read and understand REST APIs, webhooks, JSON structures, and HTTP status codes (must be able to construct and inspect API calls via tools like Postman or cURL). LLM & Conversational AI Concepts: Practical, non-casual understanding of LLM parameters (system prompts, context windows, guardrails, function calling, tool use, temperature) or rule-based/NLU bot flow builders. Customer-Facing Delivery Ownership: Direct experience in client-facing discovery meetings, solution scoping, or delivery management with external business stakeholders.
2. Good-to-Have / Preferred Signals (Ranking Factors) Prior hands-on experience configuring or deploying Conversational AI platforms (e.g., Dialogflow, Yellow.ai, Gupshup, Enterprise OpenAI assistants, Rasa) or WhatsApp Business API. Scripting or automation skills in Python, JavaScript, or no-code i. PaaS tools (Make, Zapier, n8n, Zoho Flow). Familiarity with conversational metrics: Containment Rate, Task Completion Rate, Fallback Rate, First Response Time, and CSAT drivers. Exposure to CRM platforms (Zoho, Salesforce, Lead. Squared) and enterprise knowledge repositories (SharePoint, Notion, SQL databases).
3. Explicit Anti-Fit Criteria (Immediate AI Rejection Filters) Non-Technical / Pure Business Backgrounds: BA candidates without a technical/computer science degree or without demonstrable experience handling technical API/integration specifications. Pure Coding / Internal Engineering Only: Software engineers or developers seeking pure back-end/full-stack coding roles who have no interest or experience in client-facing workshops or requirement gathering. Pure BPO / Non-Technical Support: Candidates coming strictly from voice-process customer service, call centres, or transactional helpdesk without platform implementation experience. Casual AI Users: Candidates who describe standard ChatGPT text generation or copywriting as "Prompt Engineering" without experience building structured system prompts, tool interfaces, or guardrails for software applications. Hands-Off Project Management: Project managers or coordinators who delegate technical configuration and documentation entirely to developers without understanding the underlying technical workflows themselves. Target Metrics & Operational KPIs Implementation Turnaround Time (TAT): % of assigned bot projects taken live within agreed timeline SOWs. First-Time-Right (FTR) Quality: Zero critical post-launch rework or broken workflow redesigns within 30 days of go-live. Bot Performance Metrics: Containment rate and task completion rate on deployed bots Documentation Quality: Assigned projects delivered with fully populated solution architecture diagrams, prompt packs, and API mapping documents.