AI GenAI And Lakehouse Applications Engineer

Job Description: AI / GenAI & Lakehouse Applications Engineer

Epergne Solutions is seeking a specialized AI / GenAI & Lakehouse Applications Engineer to lead the operationalization of cutting-edge data intelligence in Abu Dhabi. This role is designed for a technical expert who bridges the gap between raw data and production-grade artificial intelligence, specifically within the Databricks ecosystem.

As an engineer in this role, you will be responsible for moving AI beyond the “lab” and into scalable, secure, and governed enterprise environments. You will architect the infrastructure that powers Generative AI—managing everything from high-dimensional embeddings to real-time inference—while ensuring the underlying Lakehouse architecture remains the “single source of truth.” If you have a passion for MLOps , LLMOps , and building intelligent applications that scale, this is your opportunity to define the future of data intelligence in the UAE.

Key Responsibilities

• ML Lifecycle Management: Implement and manage end-to-end ML lifecycles using MLflow , including experiment tracking, model registry, and controlled production promotion.

• Feature Engineering: Design and govern Feature Store assets to ensure versioning, reusability, and semantic consistency across the organization.

• Generative AI Deployment: Operationalize GenAI workloads, focusing on prompt management, vector embeddings, and efficient inference patterns for Large Language Models (LLMs).

• Lakehouse Application Design: Build and deploy governed applications that consume Lakehouse data for AI-driven analytics and conversational interfaces.

• Monitoring & Observability: Implement rigorous monitoring for model performance, identifying and remediating feature drift and data drift in real-time.

• Security & Governance: Ensure all AI/GenAI workflows strictly comply with enterprise access controls, data privacy regulations, and governance policies.

• Operational Integration: Integrate AI workflows into robust CI/CD pipelines and collaborate with DataOps to optimize for cost, reliability, and scalability.

Required Skills & Experience

• Experience: Minimum 5+ years of hands-on experience in AI/ML engineering or Data Science operations.

• Tech Stack: Deep proficiency with Databricks, MLflow, and Feature Stores .

• GenAI Proficiency: Practical experience with LLMs, prompt engineering, and RAG (Retrieval-Augmented Generation) architectures.

• Operations: Strong background in MLOps (CI/CD for Machine Learning) and deploying scalable batch/real-time inference solutions.

• Architectural Knowledge: Deep understanding of data governance, secure AI deployment, and Lakehouse principles.

Job Data Summary