Senior Ai Engineer - Riyadh,KSA
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We are looking for a highly capable Senior AI Engineer / MLOps Engineer to join our team and lead the design, development, deployment, and optimization of scalable, production-grade AI and machine learning solutions. The ideal candidate will have strong hands-on experience across AI engineering, machine learning, MLOps, cloud-native architecture, and data engineering, with the ability to transform experimentation into reliable, business-ready systems.
This role requires deep expertise in LLMs, RAG, agentic AI workflows, CI/CD automation, production ML lifecycle management, and modern data platforms. The selected candidate will be expected to lead end-to-end AI initiatives, work across multiple projects, collaborate with technical and business stakeholders, and ensure operational excellence across AI platforms.
Key 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.
Requirements
⢠Bachelorās 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.
Requirements
- ā¢Hands-on experience across AI engineering, machine learning, MLOps
- ā¢Expertise in LLMs, RAG, agentic AI workflows
- ā¢Experience with CI/CD automation and production ML lifecycle management
- ā¢Knowledge of modern data platforms and cloud-native architecture
- ā¢Experience building and managing MLOps pipelines
- ā¢Experience with model monitoring frameworks
- ā¢Experience with Infrastructure as Code
- ā¢Experience building automated data pipelines
Nice to Have
- ā¢Experience with structured and unstructured datasets
- ā¢Experience with data lakes, data warehouses
- ā¢Experience with analytics-ready datasets
Responsibilities
- ā¢Design, develop, deploy, and maintain production-grade AI and ML systems
- ā¢Build and optimize LLM-powered applications
- ā¢Develop intelligent workflows
- ā¢Fine-tune, evaluate, and operationalize machine learning and foundation models
- ā¢Implement CI/CD pipelines for ML and AI workflows
- ā¢Establish model monitoring frameworks
- ā¢Architect scalable AI/ML platforms
- ā¢Build and maintain automated data pipelines
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DeepSource provides an AI-powered platform for automated code review, helping development teams improve code quality and reduce bugs. It serves software engineering teams of all sizes.
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