The Data Engineer III, AI Platform builds and operates reusable data pipelines and AI-ready platform services for Hawaii Mobility. Reporting to the Manager, Data Engineering, within the Data Visualization, Analytics & Automation function, this role builds, tests, releases, and observes production workloads on Databricks and Azure.
This senior individual contributor engineers governed feature, context, and evaluation pipelines for predictive and generative AI, and implements the data and platform components of MLOps and LLMOps patterns designed with the Data & AI Architect under the Director, Enterprise Architecture. The role owns reliability for the assets it ships and mentors engineers through review and pairing; the function is led by its Director and reports to the VP, Data & AI.
KEY OUTCOMES:
Production Pipelines and Frameworks
- Build and own reusable ingestion, transformation, orchestration, and serving patterns across batch and streaming workloads on Databricks. Develop connectors, recovery paths, schema evolution, and certified data sets for reporting, applications, and AI.
- Implement automated reconciliation, data quality, freshness, lineage, and access controls so production pipelines and AI context remain reliable and governed.
AI Enablement Engineering
- Engineer feature, context, and retrieval pipelines - document ingestion, metadata, embeddings, and indexes - to solution designs agreed with the Data & AI Architect.
- Build versioned evaluation data, model and agent monitoring feeds, feedback capture, and identity-aware feature tables, APIs, and event streams. Implement the data and platform components of MLOps and LLMOps delivery.
- Maintain and develop the semantic/ontology layer that informs Servco's LLMs and extends our data understanding to LLM platforms such as OpenAI and Claude.
- Tune Servco's LLMs for accuracy and review feedback via the Genie admin console.
- Assist with the implementation and maintenance of AI agents within our enterprise platforms to automate repetitive work and remove bottlenecks in the solution development lifecycle
Infrastructure and Reliability
- Implement reproducible environments, infrastructure as code, CI/CD, logging, monitoring, alerting, and runbooks. Tune workload performance and cloud cost within approved architecture and security standards.
- Own reliability, incident response, and recovery for the assets you ship. Improve testing and release practices from production learning.
Partnership and Mentorship
- Work with the Manager, Data Engineering, Analytics Engineers, architecture, and operators to translate customer and operating needs into dependable data products; support agreed shared capabilities.
- Mentor newer engineers through code review and pairing, document reusable patterns, and retain critical know-how in Servco-controlled systems.
- Training and supporting forward deployed team members in implementing AI solutions in line with architecture, implementation and platform standards
QUALIFICATIONS:
Education:
- Relevant degree or equivalent demonstrated experience.
Work Experience:
- 6-10 years building production data pipelines and platforms, with strong Python, SQL, Spark, and Databricks engineering on Azure or a comparable cloud.
- Experience building reusable frameworks, automated tests, CI/CD, infrastructure as code, and observable production services.
- Hands-on AI-ready pipelines for features, retrieval corpora, or evaluation data, with practical MLOps or LLMOps deployment and monitoring experience.
- Record of operating reliable data products, resolving incidents, tuning cost and performance, and mentoring engineers.
Skills:
- Curiosity about unfamiliar systems and an ability to anticipate how changes in business workflows affect data and platform design.
- MLflow or comparable model lifecycle tools, Unity Catalog, vector search, event integration, and automotive or retail operations data. (Preferred)
Interested?
Visit to apply online or email us at careers@servco.com.
