Corporation's corporate functions. Working in close partnership with the Data Platform & Analytics Engineer, this role takes curated, AI-ready data and transforms it into production-grade intelligent systems. The ideal candidate brings hands-on experience in large language model orchestration, agentic workflow design, and the full lifecycle of deploying and maintaining AI solutions in cloud environments. This is a highly technical individual contributor role at the intersection of applied AI and enterprise automation.
Essential Functions:
AI Agent Design & Development
- Design and build AI agents and multi-agent systems using Snowflake Cortex AI, Snowflake ML, Amazon Bedrock, LangGraph, LangChain, AutoGen, and CrewAI
- Develop agentic workflows that automate business processes across corporate functions
- Translate business requirements into robust, maintainable agent architectures
- Implement prompt engineering strategies, tool use, and memory patterns for production-grade agents
Model Development, Fine-Tuning & Evaluation
- Develop, fine-tune, and evaluate ML models using Snowflake ML, AWS SageMaker and Bedrock foundation models
- Design evaluation frameworks to measure model accuracy, reliability, and alignment with business objectives
- Select appropriate foundation models and orchestration strategies based on use case requirements
- Manage model versioning, experimentation tracking, and performance benchmarking
Production Deployment & Infrastructure
- Deploy AI and agent workloads to AWS infrastructure including ECS and S3
- Build CI/CD pipelines for model and agent deployment, ensuring reliable and repeatable release processes
- Manage containerized AI workloads and ensure high availability and scalability in production
- Leverage Snowflake AI tooling and Cortex capabilities to power data-driven AI features
Monitoring, Maintenance & Reliability
- Monitor production AI systems for drift, degradation, and anomalous behavior
- Own incident response and root cause analysis for AI and agent failures in production
- Implement logging, observability, and alerting frameworks across all deployed AI solutions
- Continuously improve agent performance based on production feedback and stakeholder input
Data Collaboration & AI Readiness
- Partner closely with a Data Platform & Analytics Engineer to ensure curated data layers meet AI consumption requirements
- Define feature requirements, data contracts, and schema standards needed for agent and model development
- Provide inputs on data architecture decisions that impact AI workload performance
Governance, Security & Responsible AI
- Ensure all AI solutions adhere to Carnival Corporation’s AI governance standards and responsible AI principles
- Apply data privacy controls and access management within AI pipelines
- Document agent architectures, model cards, and deployment runbooks to support audit and compliance requirements
Knowledge, Skills & Abilities:
- Scope: The AI / ML Engineer operates with a high degree of independence in the design and execution of AI and agentic solutions. This role holds decision-making authority over agent architecture, model selection, prompt engineering strategy, and the deployment patterns used to bring AI capabilities into production. Day-to-day responsibilities span the full AI development lifecycle: from requirements analysis and prototype development through to production deployment, monitoring, and ongoing maintenance. The role works closely with the AI Product Owner to translate business use cases into engineering deliverables, and partners with the Data Platform & Analytics Engineer to ensure data readiness for AI workloads. Common challenges include managing agents in production, designing reliable multi-agent systems, and ensuring AI outputs meet governance and compliance standards. The ideal candidate is equally comfortable working on cutting-edge agentic frameworks and maintaining the reliability of systems already in production.
- Problem solving: Demonstrates the ability to solve complex, ambiguous problems at the intersection of data, AI, and business operations. Translates loosely defined business problems into well-structured AI agent architectures, model pipelines, and production-ready solutions. Selects appropriate foundation models, orchestration frameworks, and agent patterns (single-agent vs multi-agent) based on accuracy, latency, cost, and governance requirements. Breaks down end-to-end AI system challenges (data readiness, orchestration, deployment, monitoring) into manageable components and sequences work logically. Diagnoses issues in production AI systems, including model drift, prompt degradation, orchestration failures, and infrastructure bottlenecks. Applies systematic experimentation, evaluation frameworks, and benchmarking to compare alternative modeling and orchestration approaches. Balances trade-offs between speed to value, solution robustness, scalability, and long-term mai