Tech Lead — Conversational AI & Driver Automation

Engineering Lead – Conversational AI & Driver Automation

We are looking for an Engineering Lead to own and drive Snoonu's Conversational AI and Driver Automation platform — a portfolio spanning IVR systems, multi-channel chatbots, and a production-grade multi-agent Agentic AI framework built on AWS Bedrock. You will lead a focused team of AI Engineers and Python Backend Engineers, turning operational SOPs into autonomous, reliable workflows that serve drivers in real time across Qatar.

Responsibilities

Set technical direction, establish engineering standards, and own delivery end-to‑end — from architecture decisions and code reviews to observability and cost governance.
Lead, mentor, and grow a cross‑functional team of AI and Python backend engineers, driving technical quality, delivery velocity, and engineering culture.
Own sprint planning, technical scope definition, and delivery commitments for the conversational AI and driver automation domain.
Conduct design reviews, define coding standards, and maintain engineering quality across IVR, chatbot, and agentic system codebases.
Act as the primary technical interface between Engineering, Product, and Operations for all driver‑facing automation initiatives.
Partner with the R&D Director to shape the team's technical roadmap, evaluate emerging AI capabilities, and surface the next high‑leverage bets.
Own the architecture and continuous improvement of Snoonu's IVR system, ensuring reliability, low latency, and clean escalation paths for driver calls.
Drive design decisions for call flow logic, intent/slot management, DTMF routing, and voice‑to‑action fulfillment.
Define and monitor SLAs for IVR uptime, misroute rate, and escalation‑to‑human ratios.
Lead the design and delivery of Snoonu's multi‑agent AI chatbot service for driver support across real‑time chat channels.
Own the four‑agent architecture — Coordinator, Data Collector, Rules Agent, and Action Executor — running on AWS Bedrock Agents or similar architecture.
Ensure chatbot flows handle driver intents reliably, drive LLM evaluation cycles, prompt strategy, and Bedrock Guardrail design to ensure safe, consistent responses.
Integrate chatbot services with order management, CRM (Salesforce), Slack operations channels, and driver‑facing apps via secure, event‑driven patterns.
Lead the build‑out and operation of the SOPs‑as‑Code framework, encoding operational SOPs as machine‑readable policies executed by the multi‑agent system.
Own the Config File architecture and Config Reader Agent pipeline that converts PDF‑based SOPs into deployable agent configurations on AWS Bedrock.
Govern the structured rules engine, design and enforce human‑in‑the‑loop checkpoints, escalation triggers, confidence thresholds, and operator override capabilities.
Establish versioning, rollback, and safe deployment practices for SOP configuration changes in production.
Own the cloud backbone for AI services: Lambda, ECS/Fargate, SQS/SNS, DynamoDB, MongoDB, S3, CloudWatch, and AWS Bedrock.
Build and maintain CI/CD pipelines for prompt versioning, agent configuration rollout, and automated eval gates before production deployment.
Define observability standards — per‑agent‑turn latency SLAs, Bedrock cost tracking, drift detection, and failure alerting.
Lead capacity and cost planning as interaction volumes scale across driver and operations channels.
Evaluate frontier LLMs and orchestration frameworks against Snoonu's operational constraints, prototype next AI capability, and produce architecture decision records and technical documentation.

Qualifications

Education

Bachelor's or Master's degree in Computer Science, AI, Software Engineering, or a related field.

Experience

6–10 years of software engineering experience, with at least 3 years in a tech lead or engineering management role.
Track record of shipping production conversational AI, IVR, or agentic systems end‑to‑end, including architecture, monitoring, and full stack.
Hands‑on experience with multi‑agent orchestration on AWS Bedrock or equivalent frameworks (LangGraph, CrewAI, AutoGen).
Prior experience leading a team of 3–8 engineers, coaching‑first approach to technical growth.
Strong Python and backend development skills; ability to write, review, and uphold production‑grade code.
Experience with AWS services: Lambda, API Gateway, SQS/SNS, Step Functions, DynamoDB, S3, CloudWatch, IAM, VPC, Secrets Manager, CI/CD for AI services.
Background in IVR architecture, intent/slot management, DTMF handling, and SLA monitoring.
Deep familiarity with AWS Bedrock Agents, Guardrails, and LLM invocation; preference for Claude Sonnet/Opus.
Knowledge of RAG pipelines, embedding models, vector databases (OpenSearch, Pinecone, pgvector).
Experience with Docker, ECS/Fargate, Git, automated testing, CI/CD workflows, and Salesforce integrations.
Strong communication skills, ability to articulate trade‑offs and influence roadmap decisions.
Bias for action, structured under ambiguity, collaborative, and direct—raising risks early and pushing back as needed.

Core Technical Skills

Multi‑agent system design and orchestration patterns.
Prompt engineering: system prompts, structured outputs, tool‑use, multi‑turn reasoning, safety evaluation.
Structured rules engines: condition/operator/value schemas for deterministic decision logic.
Observability & cost governance across AI services.
Capacity and cost planning for scaling user interactions.
Ability to evaluate frontier LLMs and orchestration frameworks.

Bonus Skills

Knowledge of logistics, food delivery, or real‑time operations domains.
Experience building for Arabic‑speaking markets or Arabic language NLP.
Familiarity with open‑weight model inference, multi‑modal AI, or voice‑first interaction design.

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