Power BI Developer

Job Description: ML Engineer (with MLOps Expertise)

Role Overview

1. End-to-End ML Lifecycle Ownership

• Build and maintain pipelines covering data ingestion → feature engineering → model training → deployment → monitoring → retraining.
• Own production ML workflows, ensuring reliability, scalability, and observability.

2. Feature Engineering & Feature Store

• Design reusable, production-grade feature pipelines using PySpark.
• Manage feature stores (Databricks Feature Store preferred).
• Ensure point‑in‑time correctness, feature versioning, and online/offline feature parity.

3. Model Training & Experimentation

• Develop structured training pipelines (beyond ad‑hoc notebooks).
• Use MLflow for experiment tracking, reproducibility, and lineage.
• Implement hyperparameter tuning and model comparison frameworks.

4. Model Deployment

• Build and maintain batch scoring pipelines and real-time inference endpoints.
• Implement CI/CD for ML models using GitHub/Bitbucket.
• Manage model versioning, environment consistency, and deployment automation.

5. Model Monitoring & Observability

• Set up monitoring for data drift, concept drift, and model performance degradation.
• Build dashboards and alerting mechanisms for proactive issue detection.

6. Automated Retraining

• Design automated retraining pipelines with validation gates and approval workflows.
• Implement trigger-based retraining (data changes, drift thresholds, performance drops).

7. Integration with Data Platform

• Work deeply with Databricks (Unity Catalog, Delta Lake, Workflows).
• Understand data dependencies, schema evolution, and platform governance.

8. Governance & Compliance

• Ensure model lineage, auditability, and reproducibility.
• Implement access control and governance using Unity Catalog.

Must-Have Skills

• Python + PySpark (production-grade)
• Strong experience with Databricks (Jobs, Workflows, Delta, MLflow)
• CI/CD pipelines (GitHub Actions / Bitbucket Pipelines)
• Pipeline orchestration (Databricks Workflows / Airflow)
• Experience deploying and maintaining ML models in production

Good-To-Have Skills

• Real-time inference architecture (REST endpoints, streaming)
• Exposure to GenAI / LLMOps
• Experience with large-scale enterprise ML systems
• Knowledge of cloud platforms (Azure preferred)

Candidate Profile

• Demonstrated experience owning ML systems in production.
• Strong engineering mindset — versioning, testing, automation, observability.
• Ability to collaborate with data engineers, platform teams, and business stakeholders.
• Comfortable working in fast-paced, delivery-focused environments.

Why Join Us

• Work on real enterprise ML systems with modern tooling.
• Opportunity to build scalable ML platforms from the ground up.
• Exposure to automation, agentic workflows, and next-gen AI capabilities.
• Collaborative, engineering-driven culture.

Job Types: Full-time, Permanent

Application Question(S)

• Notice period
• Current Salary per month in AED
• Expected salary per month in AED

Experience

• Pipeline orchestration (Databricks Workflows / Airflow): 4 years (Required)
• deploying and maintaining ML models in production: 4 years (Required)
• Machine Learning: 5 years (Required)
• Python + PySpark (production-grade): 4 years (Required)
• Databricks (Jobs, Workflows, Delta, MLflow): 4 years (Required)
• CI/CD pipelines (GitHub Actions / Bitbucket Pipelines): 4 years (Required)