Engineering Manager - AI, Data & Full-Stack

Open Innovation AI; Abu Dhabi; On-site; AED 300,000 - 520,000; Full time

Job summary

Open Innovation AI in Abu Dhabi seeks an experienced engineering leader to manage a 10-person AI/ML, data, and full-stack team. You’ll steer delivery, guide teams through ambiguity, and partner with product, architecture, QA, and data science to ship AI-powered products.

You’ll own quarterly planning, unblock blockers, ensure production readiness, and help grow the team through coaching, hiring, and a culture of accountability and continuous learning.

Qualifications

• 4+ years managing engineering teams
• Strong stakeholder management and clear communication
• Broad technical breadth across AI, data, and applications
• Ability to define tasks and break down work for research-driven and product delivery

Responsibilities

• Own delivery. Run quarterly and capacity planning, match engineers to projects, surface risks and dependencies.
• Unblock the team. Resolve context gaps and negotiate with Product, Solution Architecture, QA, and Data Science.
• Own operational health. Incident management, postmortems, observability, production readiness.
• Improve workflows. Address tooling, model evaluation, experiments, and release processes.
• Grow people. 1:1s, coaching, performance reviews, hiring, and a culture of collaboration and accountability.
• Shape the roadmap. Contribute to technical direction and address debt.

Skills

Team management

Stakeholder management

AI/ML breadth

Product delivery

Communication

Job description

Company Overview

Open Innovation AI is a global technology company that specializes in developing advanced

solutions for managing AI workloads. Its flagship product, the Open Innovation Cluster

Manager (OICM), orchestrates complex AI tasks efficiently across diverse infrastructures. The

platform is hardware agnostic, optimized for various GPUs and accelerators hardware, and

facilitates seamless integration and scalability for enterprise AI applications. Open Innovation

AI focuses on optimizing and simplifying AI workload management and making AI

technologies accessible to organizations of all sizes. With its innovative solutions, companies

can reduce operational costs, accelerate time to value, and maximize their return on investment,

ensuring that their AI strategies contribute directly to enhanced business outcomes.

Role Overview

Lead a 10 person team of AI/ML, data, and full stack engineers - the people who ship our AI powered products, the data infrastructure behind them, and the applications users touch every

day. This is a management-first role: your job is to keep a talented team shipping fast through

ambiguity, research-to-production cycles, and a constantly shifting AI landscape, in close

partnership with Product, Solution Architecture, and QA.

Role Responsibilities

• Own delivery. Run quarterly and capacity planning, match engineers to the right projects, and surface risks, dependencies, and trade offs to stakeholders early.
• Unblock the team. Chase down missing context, defuse conflict, and negotiate with Product, Solution Architecture, QA, and Data Science so decisions stay sustainable and scalable.
• Own operational health. Incident management, postmortems, observability, and production readiness for both applications and AI systems.
• Improve how the team works. Fix process, tooling, and developer experience pain points including model evaluation, experimentation, and release workflows and track engineering health metrics.
• Grow people. Effective 1:1s, career coaching, performance reviews, hiring, and a culture of collaboration, accountability, and continuous learning.
• Shape the roadmap. Contribute to the team’s technical direction and make sure technical and model debt gets tackled, not deferred.

Required experience & Qualification

• 4+ years managing engineering teams of roughly 10 people. Management ability is the fundamental requirement here - not a bonus on top of technical skill.
• Strong stakeholder management: you communicate AI, data, and application trade-offs clearly to non-technical audiences and handle conflict with confidence.
• Enough technical breadth to credibly evaluate trade-offs across AI, data, and application layers - without needing deep expertise in every discipline.
• Sharp task definition and breakdown skills, for experimental research-driven work as much as standard product delivery.
• A track record of building strong hiring pipelines and healthy, high-accountability engineering cultures.

Bonus Points

• Handson familiarity with LLM application patterns (RAG, agents, output evaluation); ML observability (model performance, drift, quality); data pipeline and platform design; modern full-stack development; and a good instinct for AI safety, data privacy, and responsible-AI practice.