Senior Ai Engineer - Riyadh,KSA at DeepSource Technologies

Cairo, Cairo, Egypt -

Full Time

Start Date

Immediate

Expiry Date

04 Jul, 26

Salary

0.0

Posted On

05 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, SQL, MLOps, LLMs, RAG, Machine Learning, CI/CD, Data Engineering, Cloud-native Architecture, Agentic AI, Model Fine-tuning, Infrastructure as Code, ETL/ELT, Data Governance, Model Monitoring, Distributed Systems

Industry

IT Services and IT Consulting

Description

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. • 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.

Responsibilities

The Senior AI Engineer will lead the end-to-end design, development, and deployment of production-grade AI and machine learning solutions. Responsibilities include building LLM-powered applications, managing MLOps pipelines, and collaborating with cross-functional teams to ensure operational excellence.