AI Engineer — Chatbot & Agentic AI

Responsibilities

• Technical Leadership & Architecture
  • Own the end‑to‑end architecture of Snoonu’s conversational AI and agentic automation platform — from LLM selection and prompt strategy to cloud infrastructure and observability.
  • Define engineering standards, design patterns, and best practices for AI system development; conduct design reviews and enforce quality bars.
  • Mentor and guide junior/mid‑level engineers; review code, provide technical feedback, and accelerate the team’s LLM engineering capabilities.
  • Partner directly with the R&D Director to evaluate emerging technologies, shape the team’s technical roadmap, and present recommendations with trade‑off analysis.
• Conversational AI & Chatbot Development
  • Lead the design and delivery of multi‑channel chatbots (web, Whats App, app) using AWS Lex, Bedrock, and API Gateway integrated with Claude or other LLMs.
  • Own complex dialogue system challenges: multi‑turn reasoning, context persistence, intent disambiguation, and graceful fallback strategies.
  • Integrate chatbots with Snoonu’s backend services (order management, CRM, logistics APIs) via secure, scalable RESTful/event‑driven patterns.
  • Drive LLM evaluation cycles — benchmark model versions, prompt strategies, and RAG configurations against production quality and cost targets.
• Agentic AI Pipelines
  • Architect Agentic AI systems that encode Snoonu SOPs as autonomous, multi‑step workflows for customer support, order verification, and logistics operations.
  • Select and govern the right orchestration approach (Lang Graph, CrewAI, Bedrock Agents, Step Functions) per use case with a clear rationale on reliability, debuggability, and scalability.
  • Design robust memory, context management, tool‑use, and guardrail layers to ensure agents behave predictably in adversarial or edge‑case conditions.
  • Establish human‑in‑the‑loop checkpoints, confidence thresholds, and escalation paths — ensuring agents augment rather than replace human judgment in critical decisions.
• AWS Infrastructure & MLOps
  • Design and own the cloud backbone for AI services:
    Lambda, ECS/Fargate, SQS/SNS, DynamoDB, S3, Cloud Watch, and Bedrock — with a focus on scalability, cost, and reliability.
  • Build CI/CD pipelines for prompt versioning, model rollout, A/B testing, and automated evals before production deployment.
  • Define and enforce monitoring standards for drift, latency, cost, and failure rates across all deployed AI systems.
• R&D & Innovation
  • Lead frontier model evaluation — benchmark Claude, GPT, LLaMA, Mistral, and emerging open‑weight models against Snoonu’s specific use cases and constraints.
  • Identify and prototype the next high‑leverage AI capability the team should build — bring experiments from idea to validated proof‑of‑concept with clear go/no‑go criteria.
  • Produce high‑quality technical documentation: architecture decision records, experimental results, and prompt engineering playbooks for team‑wide use.

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

• 5–8 years of hands‑on software engineering experience, with at least 3 years focused on LLM‑based systems, conversational AI, or agentic architectures.
• Demonstrated track record of owning and shipping production AI systems end‑to‑end — not just models, but the full stack from API to monitoring. Portfolio, Git Hub, or detailed case studies required.
• Prior experience in a senior IC or tech lead role: setting technical direction, conducting design reviews, and mentoring engineers.
• Strong Python and backend development skills (FastAPI / Flask preferred); ability to write clean, production‑grade, maintainable code.
• Research‑driven mindset — obsessed with what’s next in AI; able to translate frontier research into production value quickly.
• Extreme ownership: you define the problem, architect the solution, ship it, and hold yourself accountable for outcomes — without waiting to be told.
• Strong business context awareness — you think about ROI, operational impact, and user outcomes, not just technical elegance.
• Senior communicator: can explain complex agent design trade‑offs to non‑engineers, write compelling technical proposals, and influence direction…