Senior Data Engineer (AI Foundry)
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About the Role
Aventra Group is a dynamic and fast-growing company dedicated to transforming enterprises through innovative Data and Application Engineering services.
Key Skills for This Role
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Company Description
Aventra Group is a dynamic and fast-growing company dedicated to transforming enterprises through innovative Data and Application Engineering services.
We deliver value-focused solutions in Data and Analytics, Enterprise Applications, Cloud Engineering, and Platform Development while maintaining world-class security and quality standards with ISO27001 and SOC 2 certifications.
With operations across Malaysia, Singapore, Philippines, Vietnam, and India, we cater to clients in APAC, the Middle East, and Europe, specializing in industries such as Maritime, BFSI, Healthcare, Oil & Gas, and Logistics.
We partner with leading technology providers, including Microsoft, AWS, and Salesforce, and invest in proprietary innovations like AG Data Exchange and AG Ai Studio.
Join our rapidly expanding global team and make a tangible impact with Aventra.
Explore opportunities at Aventra.
Role Description
This is a full-time, on-site position for a Senior Data Engineer in Aventra Group’s AI Foundry Team, based in Dubai.
The Senior Data Engineer will design and implement scalable data engineering solutions, including data pipelines and ETL processes.
The role involves managing and optimizing data warehouse architecture, developing and maintaining data models, and performing in-depth data analysis to extract valuable insights.
Collaborating with cross-functional teams to support AI and analytics functions is a critical part of this role.
Use Case Acceleration & Delivery
- Rapidly design and implement AI use cases sourced from:
- The approved demand intake channel
- EA blueprint improvement opportunities
- Translate high-level business problems into deployable AI Foundry solutions (MVP → pilot → scale)
Microsoft AI Foundry & Fabric Enablement
- Build and deploy AI applications using Microsoft AI Foundry, including:
- Agents, copilots, and AI workflows
- Model catalog and Azure OpenAI models
- Develop Retrieval-Augmented Generation (RAG) patterns grounded in:
• OneLake / Microsoft Fabric
- SharePoint and enterprise data sources
Analytics & Model Development
- Implement fit-for-purpose ML and analytics models (forecasting, classification, anomaly detection) using Azure ML and/or Fabric
- Integrate analytical outputs into AI agents and business workflows
Architecture & Data Alignment
- Work closely with:
- Enterprise Architecture to ensure alignment with target-state architectures
- Data teams to reuse certified datasets, semantic models, and pipelines
- Follow EA guardrails while prioritizing speed and reuse over bespoke solutions
Operationalization & Governance
- Apply pragmatic MLOps and AI governance practices aligned with client standards
- Evaluate AI solutions using AI Foundry evaluation tools (groundedness, relevance, safety)
- Ensure documentation of:
- Use case objectives
- Architecture patterns
- Deployment approach and limitations
Collaboration & Enablement
- Work alongside BI analysts, data engineers, and product owners
- Enable internal teams by sharing reusable patterns, templates, and reference implementations
Required Qualifications
- Strong Python skills for data analysis and AI development
- Hands-on experience with Microsoft Azure AI services, preferably:
- Azure AI Foundry / Azure AI Studio
- Azure OpenAI
- Microsoft Fabric
- Experience building RAG-based applications
- Solid understanding of analytics, data modeling, and applied machine learning
- Ability to operate in ambiguous environments and deliver rapidly
Preferred Qualifications
- Prior experience accelerating AI or analytics use cases in large enterprises
- Familiarity with EA or target architecture models
- Experience with Dataiku DSS (nice‑to‑have, not mandatory)
- Exposure to agent frameworks (Semantic Kernel, LangChain)
- Experience in logistics, shipping, or asset-intensive industries
- Azure AI Engineer certification (preferred, not essential)
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