AI‑Ready Data Engineering
- Design and implement data pipelines that support AI and agentic workloads, including:
- Structured and unstructured data ingestion
- Data transformation and normalization
- Feature and context availability for AI use cases
- Ensure data is accurate, timely, explainable, and fit for AI consumption
- Define data contracts and quality expectations between source systems and AI components
Retrieval, Context & Knowledge Systems
- Design and maintain retrieval systems that power AI agents, including:
- Vector databases and embedding pipelines
- Metadata enrichment and indexing strategies
- Hybrid retrieval (structured + unstructured)
- Optimize context delivery for:
- Accuracy
- Latency
- Cost efficiency
- Partner with AI Engineers to improve relevance and reduce hallucinations caused by poor context
AI Platform & Infrastructure Enablement
- Build and operate AI‑adjacent platform components, including:
- Data access layers and APIs
- Secure storage for prompts, embeddings, and artifacts
- Model and prompt lifecycle support (versioning, rollback, traceability)
- Support CI/CD and environment promotion for AI workloads (dev → test → prod)
- Implement platform standards that enable reuse across AI Pods
Governance, Security & Enterprise Readiness
- Enforce enterprise‑grade controls across data and AI platforms:
- Access controls and identity integration
- Data privacy, masking, and classification
- Audit logging and traceability
- Partner with Platform & Trust teams to align with:
- Responsible AI requirements
- Model risk management
- Regulatory or audit expectations
- Design systems that balance speed, safety, and scalability
Observability, Performance & Cost Management
- Instrument data and AI platforms for:
- Data freshness and quality monitoring
- Retrieval performance and relevance
- Usage and cost‑to‑serve tracking
- Identify and remediate bottlenecks that affect AI accuracy or latency
- Support ongoing optimization and operational stability
Collaboration in the AI Pod
- Work closely with:
- Lead AI Architects to align data and platform design with agentic architectures
- AI Engineers to ensure reliable and performant data access
- AI Product Leads to understand data constraints that affect use‑case feasibility
- Contribute reusable data patterns, templates, and reference architectures to the AI Factory
