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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
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📌 Ai Data / Platform Engineer (Bogotá)
🏢 Auxis
📍 Bogotá