AI Product Manager - Delivery & Operations

Role Summary

our company is hiring an AI Product Manager to own the operational line of the Technology team - task assignment, delivery cadence, performance tracking, documentation, and discipline - while keeping AI product delivery on track. This role runs the day-to-day engine of the team so engineers and data scientists can focus on building. The technical/architecture direction stays with the Technical Head; this role partners with that line but owns how work is planned, tracked, and shipped.

The role combines understanding stakeholder requirements and leading client technical meetings , a hands-on understanding of how AI systems are deployed and how AI products are managed , and a research mandate to advance D proprietary models and agentic layers . These capabilities matter far more than years on a CV, which is why we are opening this at a mid-level (2-5 years) for a sharp, client-facing operator who can grow with the company.

• Core Competencies
  
  

Candidates will be assessed against the following areas:

Stakeholder requirements & client technical meetings - leads client and stakeholder technical meetings with confidence, asks the right questions to surface real needs and constraints, captures and documents requirements clearly, translates them into specs and tasks for the team, and manages expectations and scope throughout delivery. Comfortable being the technical face of in front of clients

AI systems deployment - practical understanding of the AI/ML delivery lifecycle: moving models and AI features from prototype to production, MLOps basics (versioning, retraining, monitoring, rollback), data pipelines, evaluation, and the operational risks of running AI in production. Able to spot delivery blockers early and hold an engineering team accountable to a deployment plan

AI product management - translating business goals into a prioritised backlog, writing clear specs and acceptance criteria, running an analysis-before-development workflow, managing scope against client commitments, and owning roadmap, releases, and stakeholder communication for AI products

Applied AI research direction - able to read the landscape of models and agentic techniques, frame research objectives, and steer the team toward greater in-house capability - reducing dependence on third-party models and building proprietary models and agentic layers into our company AI systems

Key Responsibilities

Stakeholder requirements & client meetings

lead client and stakeholder technical meetings, elicit and document requirements, translate them into clear specs and tasks, manage scope and expectations, and act as the technical point of contact between the client, leadership, and the team

Team & operations management

assign and balance work across the Technology team, run the weekly delivery cadence, remove blockers, and own team discipline and ways of working

Delivery & product ownership

maintain the backlog and roadmap for the Paula and -Hub products, enforce the analysis-before-development workflow, and ensure no client commitments are made without proper scoping

Research & proprietary capability

set and drive research objectives to grow own models and agentic layers, run a pipeline of experiments alongside delivery, and progressively move the AI systems from off-the-shelf components toward proprietary, in-house capability

Performance & KPIs

track weekly KPIs, run progress reviews, evaluate performance, and surface risks to leadership early

Documentation & process

ensure all work is documented and stored in the shared knowledge base, keep process living and followed, and onboard new joiners into it

Meeting cadence

run the standing weekly structure (e.g. Career Hub, client, and planning sessions) and keep them focused and outcome-driven.

Tools & Ways of Working - AI-Assisted Operations

our company runs its team operations with AI assistants ( Cowork-style skills/agents ) connected directly to the tools the team lives in. The AI Product Manager is expected to use and improve this setup to run the team in the most efficient way, including:

• ClickUp (mandatory) - AI-assisted task creation, assignment, status sync, and KPI/progress reporting; ClickUp is the single source of truth for all work
• Documents (Google Drive / knowledge base) - AI-assisted drafting, organising, and retrieval of specs, processes, meeting notes, and deliverables; all documentation lives in the shared drive
• Code repository - connected read access to the repo to track delivery progress, link work items to commits/PRs, and keep product status grounded in what is actually shipping
  
  

Comfort with AI/agentic tooling to automate routine operations (status roll-ups, reminders, reporting, document generation) is a core part of the job, not a bonus.

Requirements

Required Qualifications

• 2 - 5 years in technical product management, AI/ML product or delivery management, technical operations, or team lead roles within a software / AI / data environment
• Experience leading client / stakeholder technical meetings and gathering and documenting requirements
• Demonstrated understanding of AI systems deployment and AI product management
• Awareness of current models, agentic frameworks, and the build-vs-buy trade-offs for AI capability
• Hands-on experience running a work-management tool (ClickUp, Jira, Linear, Asana, or similar) for a delivery team
• Strong organisation, written communication, and stakeholder-management skills; fluent English and Arabic
• Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent practical experience
• Nice to Have
• Exposure to MLOps tooling, cloud platforms, or data-pipeline orchestration
• Hands-on experience building or fine-tuning models, or designing agentic / multi-agent systems
• Experience building or customising AI/automation workflows (Cowork, agents, scripting, or no-code automation)
• Prior experience managing a client-facing product squad
• First 90 Days - Success Measures
• Trusted to run client technical meetings solo, with requirements captured and signed off clearly
• Fully owns the ClickUp board and weekly KPI reporting for both product squads
• A defined research roadmap for proprietary models and agentic layers, with first experiments under way
• Team operating process documented, followed, and kept current in the shared drive
• Predictable weekly delivery cadence with blockers surfaced and resolved early
• AI-assisted operations (reporting, reminders, documentation) running with minimal manual effort