Business Unit:

Cubic Transportation Systems

Company Details:

When you join Cubic, you become part of a company that creates and delivers technology solutions in transportation to make people's lives easier by simplifying their daily journeys, and defense capabilities to help promote mission success and safety for those who serve their nation. Led by our talented teams around the world, Cubic is committed to solving global issues through innovation and service to our customers and partners.

We have a top-tier portfolio of businesses, including Cubic Transportation Systems (CTS) and Cubic Defense (CD). Explore more on Cubic.com.

Job Details:

Strategy & solution design
Translates business problems into feasible AI/ML solutions evaluates build-vs-buy vs. fine-tune decisions selects appropriate models, frameworks, and platforms (LLMs, traditional ML, computer vision, etc.) based on use case, cost, and latency needs defines technical roadmaps for AI adoption across the organization.

Implementation & delivery management
Leads end-to-end delivery of AI projects from pilot to production manages scope, timelines, and resourcing across data science, engineering, and product teams runs agile/iterative delivery cycles suited to the experimental nature of AI work de-risks projects by sequencing quick wins ahead of harder bets.

Technical architecture oversight
Ensures solutions are designed for scalability, maintainability, and integration with existing systems oversees MLOps/LLMOps pipelines - data ingestion, model training, evaluation, deployment, and monitoring reviews architecture decisions around vector databases, RAG pipelines, model hosting (cloud vs. on-prem), and API integrations.

Data governance & quality
Ensures data pipelines feeding models are reliable, well-governed, and compliant partners with data engineering on data quality, lineage, and access controls addresses bias, fairness, and representativeness in training data.

Model evaluation & risk management
Establishes evaluation frameworks for accuracy, hallucination rates, and business KPIs manages AI-specific risks - model drift, bias, security (prompt injection, data leakage), and explainability ensures compliance with emerging AI regulations and internal responsible-AI policies sets up human-in-the-loop review where needed.

Vendor & tooling management
Evaluates and manages relationships with AI vendors and platform providers (OpenAI, Anthropic, AWS Bedrock, Azure AI, etc.) negotiates SLAs, cost structures, and data privacy terms benchmarks tools against internal needs.

Cross-functional stakeholder management
Acts as the bridge between technical teams, business stakeholders, and leadership translates technical constraints and capabilities into business language manages expectations around what AI can and cannot realistically do drives change management and user adoption.

Team leadership
Manages or coordinates data scientists, ML engineers, and AI engineers mentors team members on best practices fosters a culture of experimentation balanced with production discipline conducts performance reviews and skill development planning.

Monitoring & continuous improvement
Sets up post-deployment monitoring for model performance, cost, and drift runs feedback loops to retrain/improve models tracks ROI and business impact of deployed AI systems iterates based on user feedback and changing data patterns.

Security & compliance
Ensures AI systems meet data privacy regulations (GDPR, CCPA, or sector-specific rules) implements guardrails against misuse, prompt injection, and unauthorized data exposure particularly relevant given defense/public-sector context (Cubic), ensures alignment with frameworks like NIST AI RMF or DoD AI ethics principles.

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Worker Type:

Employee

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