Job Description

Roles & Responsibilities

Requirements:

Bachelor s or master s degree in computer science, Software Engineering, Artificial Intelligence, or a related quantitative field.
3 5+ years of professional engineering experience, with a proven track record of taking ML models out of Jupyter notebooks and deploying them into production environments.
Deep, hands-on mastery of Google Cloud Platform (GCP) for ML workloads is essential.
Strong proficiency in Python (OOP, modular design, unit testing) and relevant AI/ML libraries (TensorFlow, PyTorch, scikit-learn, Pandas).
Experience with backend API development frameworks (FastAPI, Flask) for high-throughput model serving.
Strong DevOps fundamentals: Docker containerization, Git version control, CI/CD tools (Cloud Build, GitHub Actions), and Infrastructure as Code (Terraform).
Solid understanding of machine learning evaluation metrics (Precision, Recall, ROC-AUC) and the ability to evaluate algorithmic trade-offs for specific business problems.

Desired Candidate Profile

The Job Description

End-to-End ML model Development along MLOps Pipelines: Design, develop, and implement production-ready CI/CD pipelines on GCP, encompassing data ingestion, feature engineering, model training, evaluation, and scalable deployment.
GCP AI Architecture: Leverage and orchestrate the full GCP data stack to build AI infrastructure:
Data & Features: Build robust data pipelines and Feature Stores using BigQuery and Dataflow , Apache Beam.
Model Training & Registry: Train and version control models using Vertex AI Workbench and the Vertex Model Registry.
Endpoints and containerize lightweight deterministic rule engines using Cloud Run or Google Kubernetes Engine (GKE).
Orchestration: Schedule complex batch-scoring workflows using Cloud Composer (Apache Airflow).
Hybrid Cloud AI Integration: Experience designing architectures that securely bridge on-premises data centers (Oracle/SQL) with GCP AI services using Cloud Interconnect, Apigee API gateways, or secure REST endpoints.
Data Anonymization & Security: Proven ability to build on-premises data masking and tokenization pipelines (removing PHI/PII) before sending stateless inference requests to cloud-based LLMs.
GCP AI Architecture: Leverage and orchestrate the full GCP data stack to AI infrastructure:
Data & Features: Build robust data pipelines and Feature Stores using BigQuery and Dataflow ,Apache Beam.
Model Training & Registry: Train and version control models using Vertex AI Workbench and the Vertex Model Registry.
Deployment & Serving: Deploy low-latency real-time inference using Vertex AI Endpoints, and containerize lightweight deterministic rule engines using Cloud Run or Google Kubernetes Engine (GKE).
Orchestration: Schedule complex batch-scoring workflows using Cloud Composer (Apache Airflow).
API & System Integration: Wrap machine learning models in secure, high-performance RESTful APIs (e.g., FastAPI/Flask) to integrate seamlessly with claims processing engines (e.g., Care Connect) and API gateways.
Model Observability: Implement Vertex AI Model Monitoring to continuously track data drift, concept drift, and training-serving skew, ensuring models adapt to changing healthcare billing behaviors.
Data Security & KSA Compliance: Architect AI solutions that strictly adhere to Saudi Arabian data sovereignty and healthcare regulations (SAMA, CHI, NDMO, PDPL). Implement Cloud DLP (Data Loss Prevention) and VPC Service Controls to dynamically mask and secure Protected Health Information (PHI) and National IDs.
Cross-Functional Collaboration: Partner closely with Data Scientists, FWA Investigators, Medical SMEs, and Product Managers to translate clinical rules and business requirements into scalable technical solutions.

Sr. AI Engineer (GCP) Total-TECH Co

Sr. AI Engineer (GCP)
Total-TECH Co