AI Engineering Lead

What matters most

• Production-Grade AI Lifecycle Management

  The role requires moving beyond experimentation to managing the full lifecycle of LLM-based and agentic systems. You must prioritize establishing robust guardrails, human-in-the-loop workflows, and automated evaluation frameworks to ensure reliability in a high-stakes airport environment.

• Enterprise AI Architecture and Standards

  You are expected to define the technical strategy for AI-native products, including build-versus-buy decisions. This involves architecting scalable solutions on AWS that integrate RAG, vector search, and agentic workflows while maintaining consistent engineering standards across the organization.

• Cross-Functional Team and Vendor Leadership

  Success depends on managing a multidisciplinary team of in-house and offshore resources. You must balance technical mentorship with commercial responsibilities, such as budget management, vendor performance oversight, and translating complex business challenges into actionable delivery roadmaps.

Likely challenges to think through

• Balancing Innovation with Operational Stability

  Airport operations are mission-critical, creating a tension between adopting cutting-edge generative AI and maintaining system reliability. Prepare to discuss how you implement observability, fallback mechanisms, and rigorous testing to mitigate the inherent unpredictability of LLMs in production.

• Managing Distributed and Offshore Delivery

  Leading a team that includes offshore resources requires strong communication and delivery discipline. You may face challenges in maintaining code quality and architectural consistency; prepare to explain your approach to standardizing CI/CD, infrastructure as code, and collaborative development practices across different time zones.

• Translating Technical Complexity for Stakeholders

  You must bridge the gap between technical AI capabilities and business outcomes for non-technical stakeholders. The challenge lies in justifying AI investments and managing expectations regarding performance, latency, and costs; prepare to demonstrate how you quantify business value and explain technical trade-offs to senior leadership.

Topics to prepare

• Enterprise AI Guardrails and Governance

  Focus on designing frameworks for production-grade AI, including human-in-the-loop workflows, red-teaming, and fallback mechanisms. Exercise: Draft a strategy document outlining how you would implement automated evaluation and safety guardrails for a customer-facing LLM application to prevent hallucinations and ensure compliance.

• AI Lifecycle and Observability

  Understand the end-to-end management of models, prompts, and retrieval pipelines. Exercise: Design an observability dashboard strategy for an agentic system, identifying key metrics for latency, cost, and model performance that you would track to ensure operational reliability.

• Build-versus-Buy Strategy

  Develop criteria for evaluating SaaS AI offerings against custom-built solutions. Exercise: Create a decision matrix comparing the trade-offs of using managed services like Amazon Bedrock versus deploying custom fine-tuned models for a specific airport operations use case.

Skills to revise

• Agentic Workflow Architecture

  Design systems where LLMs use tools or function calling to perform multi-step tasks. Exercise: Map out an architecture for an autonomous agent that integrates with internal data sources and external APIs to resolve a common operational query.

• AWS Cloud-Native AI Deployment

  Proficiency in deploying scalable AI solutions on AWS. Exercise: Outline a CI/CD pipeline using AWS CDK or Terraform that automates the deployment of a RAG-based application, including infrastructure provisioning and model endpoint management.

• Retrieval-Augmented Generation (RAG) Optimization

  Mastering vector search and data grounding. Exercise: Explain how you would optimize a retrieval pipeline for a large-scale knowledge base, focusing on chunking strategies, embedding selection, and re-ranking techniques to improve response accuracy.

• Prepare a case study on production-grade RAG and agentic systems

  Draft a technical walkthrough of a project where you deployed a generative AI solution. Focus on how you handled RAG, vector search, and agentic workflows in production, specifically addressing how you implemented guardrails, human-in-the-loop workflows, and evaluation frameworks to ensure reliability.

• Develop an AWS-centric AI architecture framework

  Create a high-level architectural diagram or document demonstrating how you would integrate Amazon Bedrock with cloud-native services like Lambda, ECS/EKS, and DynamoDB. Include your strategy for managing AI consumption costs, latency, and observability in a high-scale environment.

• Formulate a strategy for managing offshore and multidisciplinary teams

  Prepare talking points on your experience managing offshore resources and vendor performance. Detail your approach to maintaining engineering standards, CI/CD discipline, and productivity across distributed teams while fostering a culture of innovation.

• Draft a framework for evaluating AI business outcomes

  Prepare a template for defining success criteria for AI initiatives. Explain how you translate business challenges into measurable KPIs, manage budgets, and make build-versus-buy decisions for enterprise AI platforms.

• Prepare thoughtful questions for the leadership team

  Develop questions regarding the current maturity of CAG's AI infrastructure, the primary operational bottlenecks they aim to solve with agentic systems, and how the organization balances rapid experimentation with the rigorous safety requirements of an airport environment.