Engineer, AI/ML

Carnival Corporation•Miami, FL

10h•Hybrid

About The Position

Description 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 maintainability. Uses strong debugging skills across model behavior, agent tool usage, infrastructure logs, and data pipelines to identify root causes efficiently. Impact: Delivers measurable business value by deploying reliable, scalable AI systems that improve efficiency, decision-making, and automation across corporate functions. Designs and deploys AI agents and automated workflows that materially reduce manual effort and cycle time in corporate processes. Over AI solutions from prototype to production, ensuring they are secure, performant, and aligned with enterprise standards. Improves AI solution reliability and business trust through proactive monitoring, observability, and continuous performance tuning. Drives adoption by building AI systems that integrate seamlessly into existing workflows and platforms. Quantifies impact using defined success metrics (accuracy, precision, throughput, cost savings, reliability, and user outcomes). Ensures AI solutions scale effectively with increased data volume, usage, and organizational demand. Actively incorporates production feedback and stakeholder input to improve agent behavior, model outputs, and automation outcomes. Contributes to the long-term AI capability of the organization by creating reusable patterns, templates, and deployment standards. Leadership: Acts as a technical leader and trusted partner, influencing outcomes through expertise, collaboration, and ownership—without formal people management responsibilities. Owns AI systems end-to-end, from initial design through production deployment, monitoring, and lifecycle maintenance. Partners effectively with Data Platform & Analytics Engineers to shape data contracts, feature definitions, and schema standards that enable high-quality AI solutions. Provides clear technical guidance and recommendations to stakeholders, helping translate business needs into realistic AI capabilities. Sets a high bar for engineering quality, reliability, documentation, and responsible AI practices. Advocates for responsible AI, security, and governance requirements and ensures they are embedded into solution design from the start. Leads incident response and postmortems for AI system failures, focusing on learning, prevention, and system improvement. Shares knowledge through documentation, runbooks, architecture diagrams, and best practices to raise the overall maturity of AI delivery. Demonstrates accountability, follows through on commitments, and proactively identifies risks before they impact production or business outcomes.

Requirements

Bachelor's degree in Computer Science, Data Science, or a related field.
5-6 years of experience in AI/ML engineering, data science, or a closely related software engineering discipline
Demonstrated experience building and deploying production AI or ML systems
Hands-on experience with cloud-based AI platforms
Proficiency with Snowflake AI tooling and Cortex capabilities, Amazon Bedrock, AWS SageMaker, and ECS for AI/ML workloads
Hands-on experience with agentic frameworks: Snowflake Intelligence, LangGraph, LangChain, AutoGen, or CrewAI
Strong Python skills including experience with ML libraries and API development
Experience with LLM orchestration, prompt engineering, and RAG patterns
Familiarity with CI/CD pipelines and MLOps best practices
Understanding of responsible AI principles, model governance, and data privacy standards
Strong communication skills with ability to explain complex AI concepts to non-technical stakeholders
Candidates must be located in (or willing to relocate to) the Miami/Ft. Lauderdale area.

Nice To Haves

Master’s degree in Machine Learning, AI, Computer Science, Statistics is a plus.
Experience in a corporate or enterprise environment preferred
AWS certifications (ML Specialty, Solutions Architect, or Developer)
Experience with multi-agent system design and orchestration at scale
Exposure to hospitality, travel, or cruise industry environments
Familiarity with Snowflake data sharing and S3-based data export patterns

Responsibilities

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

Benefits

Cost-effective medical, dental and vision plans
Employee Assistance Program and other mental health resources
Additional programs include company paid term life insurance and disability coverage
401(k) plan that includes a company match
Employee Stock Purchase plan
All full-time and part-time with benefits employees receive days off for 8 company-wide holidays, plus 2 additional floating holidays to be taken at the employee’s discretion.
All full-time employees at the manager and below level start with 14 days/year; director and above level start with 19 days/year. Part-time with benefits employees receive time off based on the number of hours they work, with a minimum of 84 hours/year. All employees gain additional vacation time with further tenure.
All full-time employees receive 80 hours of sick time each year. Part-time with benefits employees receive time off based on the number of hours they work, with a minimum of 60 hours each year.
Complementary stand-by cruises, employee discounts on confirmed cruises, plus special rates for family and friends
Personal and professional learning and development resources including tuition reimbursement
On-site Fitness center at our Miami campus