Top Tech Jobs & Startup Jobs

Lead end-to-end mergers and acquisitions processes, including target sourcing, strategic analysis, financial modeling, valuation, due diligence, negotiations, and transaction closing. Build relationships with executives, business stakeholders, bankers, advisors, and counterparties across international markets. Present recommendations to senior management and the Investment Committee while applying generative AI tools to improve M&A processes.

Defines and leads enterprise data, cloud, and AI strategies for clients. Advises executives on governance, cloud migration, AI adoption, and digital transformation; oversees complex transformation programs; shapes go-to-market offerings; supports business development and consulting sales; and contributes to thought leadership and practice growth.

Designs, analyzes, re-engineers, and develops UiPath-based RPA solutions that translate business requirements into technical specifications and production-ready automation. The role collaborates with stakeholders and Agile squads, supports process optimization and digital transformation, and requires strong functional analysis, low-level design, communication, and client-facing capabilities.

Leads international data and AI projects, coordinating technical teams, executives, vendors, procurement, and implementation partners. Responsibilities include project planning, governance, financial and vendor controls, capacity planning, steering committee management, executive communications, and external presentations. The role requires translating data science concepts for C-suite stakeholders and supporting digital transformation, AI initiatives, global rollouts, and target operating model design.

Provide primary support for clients’ ServiceNow instances by resolving incidents, fulfilling service requests, troubleshooting issues, guiding users, performing system health checks, maintaining configurations, documenting resolutions, and reporting service trends. Collaborate with development and support teams while meeting SLAs and delivering strong customer service.

Administer and maintain Ivanti UEM/MDM solutions for corporate iOS and Android devices. Manage enrollment, device lifecycles, security policies, configuration profiles, applications, integrations, and endpoint compliance. Troubleshoot complex incidents, analyze logs, support VIP users, maintain identity and security integrations, prepare for mobile OS updates, document processes, and support onboarding and offboarding workflows.

Designs, architects, and deploys machine learning and generative AI solutions for clients. Advises stakeholders on AI transformation opportunities, selects and fine-tunes ML, deep learning, and LLM models, and implements RAG techniques. Leads production AI delivery using DevOps and MLOps practices, including CI/CD, containerization, orchestration, monitoring, and performance tracking. Requires advanced expertise in Python and major AI frameworks, cloud platforms, data engineering, and multimodal AI technologies.

Build enterprise-grade LLM applications and reusable AI components using RAG, tool calling, structured generation, prompt engineering, and agentic workflows. Optimize inference, model serving, GPU utilization, scalability, latency, reliability, and cost. Develop evaluation frameworks and production deployment pipelines across cloud, on-premise, and hybrid environments. The role also involves troubleshooting AI systems, assessing foundation models, managing stakeholders, and providing client-facing consulting in Riyadh.

Elicit and document business requirements, create functional specifications and user stories, and collaborate with technical and business stakeholders on end-to-end solutions. Support testing, quality assurance, and user acceptance activities, identify process improvements, and provide ongoing functional support to align technology capabilities with business objectives.

Lead the identification, mapping, cataloging, and documentation of organizational data assets, relationships, source systems, data flows, business rules, and end-to-end lineage. Collaborate with data owners, stewards, business teams, and architects to establish governance practices, improve data quality, manage metadata, identify information gaps, and support analytical, operational, and AI initiatives. Apply data modeling principles to structure enterprise data architecture.