Overview:

Inception42, a G42 company, is the region’s leading innovator of AI-powered domain-specific as well as industry-agnostic products, built on a rich heritage of research and development. Within the G42 ecosystem, Inception42 functions as the core intelligence layer – transforming data and compute infrastructure into real-world, applied AI solutions. Beyond its commercial endeavors, Inception42 is committed to creating positive societal impact. For more information, please visit www.inceptionai.ai

Overview

You will own day-to-day Agile delivery for AI product and engineering teams, creating enough structure for dependable execution without constraining the experimentation required to build strong AI systems. The role sits close to Product, Engineering, Applied Science, Data, and Delivery teams and turns changing technical inputs into visible, manageable work. This is not a ceremony-only Scrum Master position: you will need to understand how model exploration, data readiness, evaluation, platform work, and production engineering interact, and help the team make credible commitments as evidence changes.

Responsibilities:

• Run sprint planning, daily coordination, reviews, retrospectives, refinement, and release-readiness activities with clear outcomes and disciplined follow-through.
• Keep the backlog execution-ready by working with Product, Engineering, Applied Science, and Data owners to clarify intent, acceptance evidence, dependencies, and sequencing.
• Structure AI experimentation as visible delivery work, including hypotheses, datasets, evaluation criteria, timeboxes, decision points, and paths to productionization.
• Distinguish exploratory work from committed engineering delivery and help teams forecast both honestly without hiding uncertainty or abandoning accountability.
• Track dependencies across data access and quality, model development, evaluation, infrastructure, security, integration, and deployment.
• Remove impediments directly where possible and drive timely escalation when decisions, access, capacity, or external teams block progress.
• Partner with technical leads to manage changing requirements and research findings while protecting sprint goals and making trade-offs explicit.
• Use delivery metrics such as cycle time, throughput, blocked time, predictability, defect trends, and experiment outcomes to improve team performance.
• Maintain accurate delivery information in Jira and related tools, ensuring boards, milestones, risks, and status views reflect reality.
• Facilitate productive technical and product discussions, challenge unclear ownership, and keep decisions documented and actionable.
• Strengthen team autonomy, continuous improvement, and delivery discipline across the full path from experiment to deployed product capability.

Qualifications:

What We’re Looking For

• 6–10 years of experience in Scrum, Agile delivery, or technical program execution within software-product, AI, SaaS, platform, or engineering organizations.
• Hands-on experience managing sprint planning, backlog readiness, ceremonies, impediments, cross-team dependencies, releases, and delivery metrics.
• Working understanding of AI/ML product development, including iterative experimentation, data dependencies, model evaluation, and the transition from prototype to production.
• Ability to engage credibly with product managers, software engineers, applied scientists, data teams, and platform or infrastructure teams.
• Strong Jira and Agile tooling discipline, including dashboards, workflow hygiene, reporting, and practical use of delivery data.
• Sound judgment when requirements or technical evidence changes, with the ability to replan without normalizing missed commitments.
• Strong facilitation, stakeholder management, written communication, and escalation skills.
• A structured, data-driven ownership mindset and the ability to operate effectively in ambiguity.

Nice to Have

• Professional Scrum Master, Certified ScrumMaster, SAFe Scrum Master, or comparable certification.
• Experience delivering generative AI, machine learning, data products, developer platforms, or enterprise AI solutions.
• Familiarity with experiment tracking, model evaluation, MLOps, data pipelines, cloud infrastructure, or production observability.
• Experience in a high-growth technology company, AI organization, leading technology firm, or product-focused consulting environment.