Top Tech Jobs & Startup Jobs

Design, implement, and operate enterprise-grade ML infrastructure and MLOps pipelines. Build CI/CD for ML workflows, manage model lifecycle (registry, tracking, monitoring), automate orchestration and scaling, and ensure governance, security, and reliability. Collaborate with data science, engineering, and infrastructure teams and support production-ready AI/GenAI deployments across the enterprise.

Lead enterprise AI/ML strategy and GenAI adoption for BFSI, architect agentic automation and LLM-based solutions, productionize scalable ML/GenAI systems, ensure responsible AI governance, mentor senior teams, and drive measurable business outcomes through applied AI and innovation.

Design, build, and scale AI/ML solutions for enterprise sales acceleration in BFSI. Develop risk, fraud, personalization, and predictive models; productize AI research into production systems; define data strategy with stakeholders; evangelize AI through prototypes and workshops; and mentor junior data scientists while leading cross-functional innovation sprints.

Lead and mentor a DevOps/SRE team to design, implement, and optimize CI/CD, multi-cloud infrastructure, observability, and reliability frameworks. Drive automation, incident response, cost optimization, and cross-functional collaboration to ensure secure, scalable, and highly available AI-driven platforms.

Design, develop, and deploy scalable ML and Generative AI solutions (classification, regression, time series, NLP, LLMs). Lead end-to-end model development, productize AI, implement MLOps (CI/CD, monitoring, retraining), collaborate with stakeholders, and drive measurable business impact. Support model governance, optimization, and knowledge sharing; travel across Saudi Arabia for client engagements.

Design, build, and scale cloud-native data platforms and pipelines for AI and analytics. Lead data engineering projects, implement big data processing (Spark/Hadoop/Hive/HBase), ensure data quality/governance, and collaborate with cross-functional teams to deliver production-ready datasets and data-driven product features.

Design, build, and scale AI-driven platforms and production-ready applications. Collaborate with data scientists and product teams to deploy cloud-native, reliable, and high-performance AI systems using distributed architectures, microservices, containers, CI/CD, and data pipelines.

Design, build, and maintain scalable ETL/ELT data pipelines and integrations from multiple sources. Optimize performance and reliability, implement cloud-based data architectures (AWS/Azure/GCP), collaborate with data scientists and product teams, and enforce best practices for testing, automation, security, and documentation.

Lead and optimize enterprise IT infrastructure, cloud environments (AWS/Azure), end-user support, security and access management, networking, asset/licensing governance, and next‑generation AI/HPC hardware while driving process improvement and ITSM best practices.