Tech 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.

In this role you will set the technical direction, establish engineering standards, and own delivery end-to-end — from architecture decisions and code reviews to observability and cost governance. You will work directly with the R&D Director and Product to shape what gets built next, then lead your team to ship it with excellence.

🛠 What You’ll Get Your Hands On:

Team Leadership & Delivery:

• Lead, mentor, and grow a cross-functional team of AI Engineers 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.

IVR & Voice Automation:

• 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.

Conversational AI & Chatbots:

• 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: order removal, vehicle mismatch, ETA extensions, merchant disputes, and escalations.

• Drive LLM evaluation cycles, prompt strategy, and Bedrock Guardrail design to ensure responses are consistent, safe, and operationally correct.

Integrate chatbot services with order management, CRM (Salesforce), Slack operations channels, and driver-facing apps via secure, event-driven patterns.

Agentic AI & SOP Automation:

• Lead the buildout 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 PDFbased SOPs into deployable agent configurations on AWS Bedrock.

• Govern the structured rules engine (condition/operator/value schema) to ensure deterministic, auditable decisions with no LLM interpretation ambiguity.

• 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.

AWS Infrastructure & MLOps:

• 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.

R&D & Innovation:

• Evaluate frontier LLMs (Claude Sonnet/Opus, open-weight models) and orchestration frameworks (Bedrock Agents, LangGraph, CrewAI) against Snoonu's operational constraints.

• Identify and prototype the next AI capability Snoonu should dominate — from experiment to validated proof-of-concept with clear go/no-go criteria.

• Produce architecture decision records, prompt engineering playbooks, and technical documentation for team-wide use.

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.

• Demonstrated track record of shipping production conversational AI, IVR, or agentic systems end-to-end — not just models, but the full stack from architecture to monitoring. Portfolio, GitHub, or detailed case studies required.

• Hands-on experience with multi-agent orchestration on AWS Bedrock or equivalent agentic frameworks (LangGraph, CrewAI, AutoGen).

• Prior experience leading a team of 3–8 engineers, with a coaching-first approach to technical growth.

• Strong Python and backend development skills; able to write, review, and hold the bar on production-grade code.

• Engineering multiplier: makes every engineer on the team faster and better through design guidance, code reviews, and clear technical direction.

• Ownership without ego: defines problems, architects solutions, ships results — accountable for outcomes across the full platform, not just assigned tickets.

• Operational intelligence: understands the business context of driver support, logistics operations, and the real cost of failure in real-time systems.

• Senior communicator: articulates trade-offs (agent reliability vs. automation rate, cost vs. latency) clearly to non-engineers and influences roadmap decisions through clarity of thought.

• Structured under ambiguity: brings process and rigor to fast-moving R&D environments where requirements evolve alongside the build.

• Bias for action: prototypes fast, validates early, and ships iteratively — while maintaining the quality bars that prevent production incidents.

• Collaborative and direct: raises architectural risks early, pushes back on under-specified requirements, and surfaces trade-offs before they become delivery blockers.

Conversational AI & LLMs (Core)

• Deep experience with AWS Bedrock — Agents, Knowledge Bases, Guardrails, and model invocation; strong preference for Claude Sonnet/Opus via Bedrock.

• Multi-agent system design: orchestration patterns, agents-as-tools, inter-agent handoffs, context propagation, and failure isolation.

• Prompt engineering: system prompt design, structured output, tool-use, multi-turn reasoning, eval-driven iteration, and red-teaming for safety.

• Structured rules engines: condition/operator/value schemas for deterministic, noninterpretive decision logic — mandatory in production agentic systems.

• RAG pipelines: embedding models, vector databases (OpenSearch, Pinecone, pgvector), hybrid retrieval, and knowledge base tuning.

IVR & Voice

• IVR architecture: call flow design, intent/slot management, DTMF handling, escalationto-human routing, and SLA monitoring.

• Experience with STT/TTS pipelines or voice bot platforms is a strong plus.

AWS Services  (Core)

• Lambda, API Gateway, SQS/SNS, Step Functions, DynamoDB, S3, CloudWatch — event-driven and serverless architectures.

• AWS Bedrock Agents: agent creation, alias management, tool action group configuration, and Guardrail policy management.

• IAM, VPC, Secrets Manager — security and environment best practices for AI service deployments.

• CI/CD for AI services: prompt versioning, agent config deployment pipelines, automated evals, and rollback gates.

Backend & Engineering

• Python — async, OOP, clean code; REST API design with FastAPI or Flask.

• MongoDB and DynamoDB — schema design, indexing, querying, and operational monitoring.

• Docker, ECS/Fargate; Git, automated testing, and CI/CD workflows.

• Salesforce integration (APIs, events, data sync) is a strong plus.

Bonus Points if You Have:

• Knowledge of logistics, food delivery, or real-time operations domains.

• Arabic language NLP or experience building for Arabic-speaking markets.

• Familiarity with open-weight model inference (Ollama, vLLM, HuggingFace Transformers).

• Exposure to multi-modal AI or voice-first interaction design.