Job Summary Grant Thornton is building an AI Factory to deliver enterprise‑grade, agentic AI solutions that are reliable, scalable, and trusted in real operating environments. As an AI Data / Platform Engineer, you will be responsible for the data and platform foundations that enable AI Pods to move fast without breaking trust. You will ensure agentic solutions have access to high‑quality, governed, and performant data, and that AI platforms are designed for production use, not experimentation. This role is critical when clients face fragmented data, legacy systems, or enterprise constraints that would otherwise limit AI effectiveness. Responsibilities 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 Skills and Experience Experience 6+ years of experience in data engineering, platform engineering, or cloud infrastructure roles Proven experience building production data platforms that support analytics, automation, or AI workloads Experience working in enterprise or regulated environments Data & Platform Skills Strong experience with:Data pipelines (batch and streaming) Structured and unstructured data processing API‑based data access patterns Hands‑on experience designing systems that support AI/ML or advanced analytics workloads Understanding of how data quality, latency, and availability affect AI behavior Technical Skills Proficiency in Python, SQL Experience with cloud‑native architectures (Azure preferred), including:Storage, compute, and data services Identity and access management Fabric experience is preferred Familiarity with DevOps and CI/CD practices for data and platform workloads Preferred Qualifications Experience supporting AI or agentic systems in production Hands‑on experience with:Vector databases and embedding pipelines Search, indexing, and retrieval systems Familiarity with:Data governance and cataloging concepts Model and prompt lifecycle management Consulting or client‑facing delivery experience #J-18808-Ljbffr