Job Description

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

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