G42
Director, Software Engineering
- Permanent
- Abu Dhabi, United Arab Emirates
- Experience 10 - 15 yrs
Job expiry date: 01/12/2026
Job overview
Date posted
17/10/2026
Location
Abu Dhabi, United Arab Emirates
Salary
AED 50,000 - 60,000 per month
Compensation
Comprehensive package + relocation
Experience
10 - 15 yrs
Seniority
Director
Qualification
Bachelors degree
Expiration date
01/12/2026
Job description
Software platforms, data infrastructure, machine learning pipelines and model deployment systems all fall under the engineering teams this Director leads at Inception42 in Abu Dhabi. Engineering managers and senior technical leaders report to you, and together you set direction and execution priorities in line with product, technology and business goals. Complex initiatives call for clear trade-offs across scope, time, resourcing, cost and quality. Standards for data management, MLOps, testing, release quality, observability and operational readiness are yours to establish, with security and compliance built into the lifecycle through practical DevSecOps controls. Focused R&D work also gets your sponsorship, and credible experiments are moved into production. Close collaboration with Product, Data, AI/ML, Security and Enterprise Architecture keeps engineering tied to customer outcomes. Executive stakeholders expect clear updates on strategy, progress and trade-offs. Candidates need strong engineering fundamentals, a bachelor's degree in engineering or computer science or equivalent experience, and a record of shipping and operating production software, data or ML systems at scale. A master's or PhD and experience with regulated, multi-tenant or national-scale platforms are a plus.
Required skills
Key responsibilities
- Set engineering direction and execution priorities in line with Inception42's product, technology and business objectives
- Lead and develop engineering managers and senior technical leaders across multidisciplinary teams
- Build an engineering culture grounded in ownership, technical rigour, collaboration and continuous improvement
- Drive planning and delivery of complex initiatives with clear trade-offs across scope, time, resourcing, cost and quality
- Guide architecture and technical decisions across software platforms, data infrastructure, ML pipelines and model deployment systems
- Improve the scalability, reliability, performance and operability of production data and ML systems
- Establish engineering standards for data management, MLOps, testing, release quality, observability and operational readiness
- Embed security and compliance into the software development lifecycle through DevSecOps controls and automation
- Sponsor focused R&D initiatives and move credible experiments into production capabilities
- Collaborate with Product, Data, AI/ML, Security and Enterprise Architecture to align execution with customer and business outcomes
- Create operating mechanisms for delivery health, technical risk, engineering quality and team effectiveness
- Communicate engineering strategy, progress, trade-offs and outcomes to technical and executive stakeholders
Experience & skills
- Bring strong engineering fundamentals and a track record of leading diverse teams through complex delivery
- Show experience shipping and operating production software, data or machine learning systems at meaningful scale
- Understand data infrastructure, MLOps, quality engineering, DevSecOps and the software development lifecycle in depth
- Translate company objectives into technical strategy, operating plans, architecture priorities and measurable outcomes
- Apply sound systems thinking across reliability, security, performance, cost and delivery speed
- Align technical teams and explain decisions clearly to senior stakeholders
- Operate comfortably in ambiguity and take accountability for execution
- Hold a bachelor's degree in Engineering, Computer Science or equivalent practical experience
- Add a master's degree or PhD in a related technical field (nice to have)
- Have experience with regulated, multi-tenant, high-availability or national-scale platforms (nice to have)
- Offer exposure to applied AI products, research collaborations or production model-serving environments (nice to have)
- Demonstrate experience leading platform modernisation or engineering operating-model change (nice to have)