Senior Machine Learning Engineer

AI in this role

pytorchtensorflowxgboost

ml-ops

Job Purpose

Responsible for designing, building, and operating a unified machine learning infrastructure that standardizes the lifecycle of models from research to production. ridges the gap between batch-oriented data in Snowflake and real-time inference on Kubernetes (K8s), ensuring high-quality software engineering standards and reducing implementation redundancy across all data science squads.

Key Result Responsibilities

Designs and maintains the core ML Platform architecture, integrating Snowflake/Snowpark with Kubernetes for hybrid workload support.
Designs and delivers domain specific end to end data science products, including flight revenue forecasting, customer retention, ground operations, and others.
Automates end-to-end ML pipelines including data ingestion, model training, and continuous deployment using Apache Airflow and GitLab CI/GitHub Actions.
Builds and manages a centralized Feature Store and Model Registry within Snowflake to ensure consistency between training and serving.

Key Result Responsibilities-Continued

Implements comprehensive observability systems for monitoring model performance, data drift, and system health using Prometheus, Grafana, and Evidently AI.
Develops reusable FastAPI or gRPC service wrappers for real-time model serving.
Establishes "paved road" workflows (CI/CD, unit testing, modular code) for the broader data science team.

Qualifications (Academic, training, languages)

Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.
Fluent in English Language.
Deep expertise in machine learning, statistics, and applied modeling
Mastery of Kubernetes, Docker, and Infrastructure as Code (Terraform).
Expert-level Python (OOP, modular design) and SQL.
Proficiency in serving models built with LightGBM, XGBoost, TensorFlow, and PyTorch.
Strong understanding of production ML systems and trade-offs
Ability to influence architectural decisions related to data, modeling, and deployment in collaboration with specialized teams
Strong leadership and communication skills to influence engineering culture without direct authority.
Deep understanding of aviation systems including PNRs, e-tickets, and revenue management logic (Yield and Inventory control).
Proficient in MS Office.

Work Experience

With a minimum of 6-8 years of experience in Data Sciernce, MLOps, Platform Engineering, or DevOps specifically for machine learning.
Out of which a minimum of 1-2 years of experience in the aviation domain – airline, vendor.
Strong experience designing scalable and impactful solutions
Hands-on experience with Snowflake/Snowpark and Airflow.

How we rate this

Senior Machine Learning Engineer at Air Arabia rates 97 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

ML OpsPyTorchTensorFlowXGBoost

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
Walk me through how you've used PyTorch in your day-to-day work.
What are the limits of TensorFlow that you've run into, and how did you work around them?
What's a project where you used XGBoost hands-on?
How would you decide a model or AI system is ready to ship?

Adapt your resume

List these exact terms on your resume: ML Ops, PyTorch, TensorFlow, and XGBoost. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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