Senior Ai Engineer KSA

Responsibilities

• Design, develop, deploy, and maintain production‑grade AI and machine learning systems end to end.
• Build and optimize LLM‑powered applications, including RAG pipelines, prompt workflows, agent‑based systems, and multimodal AI use cases.
• Develop intelligent workflows using tool‑calling, orchestration frameworks, and contextual reasoning patterns.
• Fine‑tune, evaluate, and operationalize machine learning and foundation models for enterprise use cases.
• Build and manage MLOps pipelines covering training, evaluation, model registration, deployment, monitoring, and retraining.
• Implement CI/CD pipelines for ML and AI workflows to support automated testing, release management, and controlled deployments.
• Establish model monitoring frameworks for drift detection, feature attribution, inference quality, and performance tracking.
• Ensure reproducibility, reliability, and version control across AI/ML environments.
• Architect scalable AI/ML platforms using modern compute, storage, orchestration, monitoring, and search services.
• Build repeatable environments using Infrastructure as Code.
• Support secure, high‑availability, and cost‑efficient deployment models across development, staging, and production environments.
• Design scalable inference and serving patterns for variable workloads.
• Build and maintain automated data pipelines, ETL/ELT workflows, and data processing frameworks for AI/ML consumption.
• Ensure data quality, lineage, governance, and versioning to support dependable model training and inference.
• Work with structured and unstructured datasets across data lakes, data warehouses, and operational systems.
• Deliver analytics‑ready datasets to downstream systems and applications.
• Lead multiple AI initiatives in parallel, including planning, execution, and coordination with internal teams and stakeholders.
• Work closely with product, engineering, data, and business teams to deliver production‑ready AI capabilities.
• Contribute to architecture decisions, technical documentation, best practices, and engineering standards.
• Support knowledge sharing, technical leadership, and continuous improvement across the AI function.

• Bachelor’s degree in Computer Engineering, Computer Science, Artificial Intelligence, Data Science, or a related field.
• 5+ years of hands‑on experience in AI engineering, machine learning engineering, MLOps, or data/ML platform engineering.
• Proven experience deploying production AI/ML solutions in enterprise environments.
• Strong programming experience in Python and SQL.
• Strong experience with enterprise AI/ML architecture and delivery.