0000001423.AI ENGINEER II.INFO TECH - DATA AND AI

Designs and maintains robust AI agents and data pipelines. Performs data orchestrations and supports enterprise AI and Data efforts. Works across departments to build scalable AI solutions that ensure reliable, secure, and high-quality data is available to business users, analysts, upstream and downstream applications. Responsible for the full lifecycle of AI Development – from selecting foundation models (FM’s) to deploying scalable orchestration layers on hybrid cloud environments. Contributes to data labelling, MLOps integration, AI Observability, Language Models testing, and documentation in collaboration with AI analysts, AI Architects, data scientists, developers, and system owners. Designs and maintains robust AI agents and data pipelines. Performs data orchestrations and supports enterprise AI and Data efforts. Works across departments to build scalable AI solutions that ensure reliable, secure, and high-quality data is available to business users, analysts, upstream and downstream applications. Responsible for the full lifecycle of AI Development – from selecting foundation models (FM’s) to deploying scalable orchestration layers on hybrid cloud environments. Contributes to data labelling, MLOps integration, AI Observability, Language Models testing, and documentation in collaboration with AI analysts, AI Architects, data scientists, developers, and system owners. ### Responsibilities Designs, develops, and maintains scalable AI Agents and Orchestration workflows across structured and semi-structured data sources. Ensures consistent design and delivery of data and AI platforms supporting Data Engineering, Cloud, and AI centers of excellence. Integrates internal and external data sources with enterprise data platforms, lakes, or warehouses. Designs and develops multi-agent systems using frameworks like LangGraph, CrewAI, or Amazon Bedrock to automate complex enterprise reviews and workflows. Performs data profiling, cleansing, and standardization to improve data quality. Monitors data pipeline health and troubleshoots failures or anomalies. Documents AI architecture, APIs, AI Business rules, and data logic for internal users. Collaborates with DevOps or infrastructure teams to implement automated AI processing workflows. Collaborates with Enterprise Architecture teams to ensure AI solutions align with internal policies, vendor questionnaires, and ethical AI guidelines. Maintains data access controls, validation rules, and retention policies. Translates business and AI requirements into technical specifications and AI pipeline designs. Participates in Agile planning, backlog grooming, and technical design sessions. Develops data and AI flow diagrams, Machine learning models, and transformation logic. Supports dataset design and delivery for dashboards, reports, or self-service analytics. Collaborates with application owners to understand source system structures and data changes. Contributes to solution architecture decisions related to Language model performance, security, storage, and data delivery. Assists in scoping and estimating new data initiatives and enhancement requests. Identifies reuse opportunities for data components, tools, or models. Builds in validation and error-handling logic into data and AI pipelines to support reliability. Performs root cause analysis for data inconsistencies and recommends preventive actions. Contributes to and follows testing procedures for data validation, performance, and integrity. Implements version control, data lineage, and reproducibility practices. Identifies performance bottlenecks and refactor inefficient data processes. Recommends improvements to schema design, data granularity, and source-system integration. Maintains awareness of industry standards for data governance, security, and accessibility. Supports automation of routine data workflows and manual reporting processes. Works closely with analysts, data scientists, application developers, and stakeholders to del…