Vice President of Software Engineering

Company Description MyOperator is a Business AI Operator and a category leader that unifies WhatsApp, Calls, and AI-powered chat & voice bots into one intelligent business communication

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

by 12,000+ brands including Amazon, Domino's, Apollo, and Razorpay, MyOperator enables faster responses, higher resolution rates, and scalable customer engagement - without fragmented tools or increased headcount.

Role Summary MyOperator runs a high-scale CPaaS platform (calls + messaging) and is now building voice and WhatsApp first AI agents for business customer facing workflows (sales & support).

This is a VP of Engineering role with a clear path to CTO. You will be the senior-most engineering leader from Day 1, reporting to the CEO, and you'll own engineering execution, architecture choices, hiring, and delivery.

12-month mission: build a repeatable platform and operating model to launch, measure, and scale AI agents fast-while keeping the existing CPaaS platform stable. Expect targeted improvements, not disruptive rewrites.

Current State (High-Level) Scale: Millions of calls/messages per day (production). Stage: AI agents are in POCs - productization; focus is turning this into a repeatable rollout engine. Platform: Existing CPaaS is mission-critical; stability is non-negotiable. Team: Lead ~40-50 people engineering team.

What You'll Build: The \"Agent Factory\" The system to be created is focused on making agent deployment fast, safe, and measurable, comprising four main layers:

Agent Build & Orchestration Layer for creating reusable workflows, tool integration, and safe handling of fallbacks and human escalation. Knowledge & Data Workflow for managing ingestion, curation, retrieval, and versioning of agent knowledge. Quality System for ensuring quality through evaluation harnesses, golden conversations, regression testing, policy checks, and release gates. Deployment System for managing versioning, staged rollouts, monitoring, rollback, and incident response.

All supported by a Learning Loop for continuous improvement through feedback capture, drift detection, and disciplined iteration.

Responsibilities

Engineering leadership Hire and lead teams, build managers/tech leads, set execution cadence, and create a high-ownership culture. Build strong technical leadership depth across teams.

Architecture & delivery Make pragmatic architecture decisions, keep velocity high, and ensure production-grade reliability. Drive core architecture decisions for the Agent Factory and CPaaS evolution. Review and approve key design documents and technical standards.

Deep technical leadership (hands-on where it matters) This is a deeply technical leadership role - not a pure people manager position. Stay close to architecture and critical systems while leading the engineering organization. Debug high-impact production issues when needed. Guide implementation on complex or ambiguous problems. Shape build-vs-buy decisions with clear technical and cost trade-offs.

Quality & operational excellence Set the quality bar across engineering: testing, code reviews, release gates, and observability. Ensure strong operational maturity including monitoring, on-call practices, and incident response.

AI-native delivery practices Adopt AI-assisted development to increase throughput and quality, with strong guardrails (reviews, tests, release checks).

CPaaS stewardship Maintain stability and performance; make improvements only where needed to support agent scale and reliability.

How You'll Operate You should be comfortable operating at multiple levels: Strategy - org design, roadmap trade-offs, long-term technical direction. Architecture - distributed systems, reliability engineering, LLM/agent systems. Execution - guiding implementation, reviewing designs, solving hard technical problems.

We're looking for a leader with strong technical credibility - someone senior engineers respect and want to build with.

Decision Rights & Boundaries You own: engineering org design, hiring plan, agent platform architecture, build-vs-buy, tooling, delivery process, reliability practices.

Year-1 boundary: no \"big-bang\" platform re-architecture. Expect targeted reliability/cost improvements and incremental modernization when it supports the agent roadmap.

What Success Looks Like (12 Months) A production-grade agent platform that enables repeatable, fast agent launches (measurably reduced deployment time). A clear quality bar and system: evals + monitoring + regression that prevents quality regressions. Strong operational maturity: observability, on-call, incident response, and predictable release cadence. CPaaS stability maintained while agent capabilities and adoption scale.

Minimum Qualifications 10+ years building and leading engineering teams (including hiring and managing managers/tech leads). Proven track record building and operating high-scale, high-throughput, multi-tenant systems in production. Strong cloud experience (AWS preferred) and hands-on depth in distributed systems, reliability, and cost-performance trade-offs. Strong understanding of the full SDLC (design - build - test - deploy - operate) and modern delivery practices (CI/CD, observability). Hands-on enough to go deep into architecture and critical paths - comfortable debugging complex production systems.

This role is not for you if... Your last 8–10 years are mostly

large enterprises

with limited

startup/hypergrowth (0→1 / 1→10) ownership . You’re primarily a

program/delivery/practice

leader (PMO, governance, coordination) rather than an

architecture-first builder . Your background is mostly

IT services/consulting

without clear

product/platform ownership

(multi-tenant, APIs, reliability). Your “AI/GenAI” work is mostly

POCs/keywords

(no production evals, monitoring, guardrails, rollback, escalation/tooling). You’re not comfortable with

ambiguity + speed + tight resources

and being directly accountable for outcomes.

Preferred Experience Built or scaled AI agents / LLM products in production (evaluation, monitoring, iteration loops). If not direct: demonstrated ability to ramp quickly, with evidence from adjacent domains (ML platforms, search/retrieval, high-scale automation, etc.). Founder or has worked closely with founders in fast-moving product environments. Experience with modern backend + frontend stacks (flexible on exact languages/frameworks; depth matters more than labels).

Growth Path to CTO (Milestones) This is a defined progression, not an open-ended promise: 0-90 days: org + platform plan; ship a v1 agent platform baseline; establish evaluation + observability standards. 90-180 days: repeatable agent launch motion; multiple agents live with rollout/rollback and regression testing. 180-360 days: measurable reduction in deployment time; quality metrics trending up; stable on-call; hiring plan executed.

As these milestones are met and the platform scales, the role expands into full CTO scope and title.