Senior Manager AI Control Tower

Client of Salt; Abu Dhabi; On-site; AED 450,000 - 700,000; Full time

Job summary

Client of Salt is seeking an AI Platforms Lead to oversee the engineering and operation of the bank's AI estate. You will own the architecture, scaling, and integration of AI access across models, agents, APIs and enterprise services, driving standardisation, automation and secure adoption at scale.

You will lead capacity planning, telemetry, incident management, and governance, while mentoring teams to deliver resilient AI-enabled solutions for enterprise needs.

Qualifications

• 15+ years in platform engineering, enterprise integration or AI/ML ops.
• Experience leading production AI platforms and model/agent operations.
• Strong design, scaling and governance of shared technology services.

Responsibilities

• Define the technical strategy and roadmap for AI access, integration, and operations.
• Lead API/LLM gateway design, MCP services, and enterprise integrations.
• Set routing, authentication, throttling, versioning, and integration patterns.
• Establish onboarding, configuration, release and retirement processes for models and services.
• Drive capacity planning, telemetry, tracing and cost attribution across services.
• Mentor engineers and drive reliability, automation, and performance improvements.
• Coordinate incident resolution, post-mortems, and disaster-recovery readiness.

Skills

Platform engineering

Enterprise integration

Cloud architecture

DevSecOps

AI/ML operations

Leadership

Banking domain

APIs & gateways

Tools

Azure

AWS

Job description

AI Platforms Lead AI Control Tower Senior Manager

Start Date ASAP

Job Purpose

To lead the engineering and operational management of the ADCB AI estate by providing accountable service ownership consolidated performance visibility and capacity management Own the development and scaling of shared AI access connectivity and integration with models agents and enterprise services Drive standardisation intelligent automation and continuous optimisation across the AI estate enabling secure efficient and resilient adoption at enterprise scale

ACCOUNTABILITIES

Own and evolve the technical strategy service architecture and roadmap for AI access integration and operational capabilities Define reusable service patterns engineering standards and ownership boundaries across models agents APIs and shared enterprise services Lead the design and operation of API and LLM gateways MCP services and enterprise integrations Define routing authentication access policies throttling versioning and reusable integration patterns Establish repeatable onboarding configuration release and retirement processes for models agents and shared services Maintain dependency inventories compatibility checks evaluation evidence and tested rollback procedures Define telemetry and tracing standards across model calls agent execution and tool interactions Correlate availability latency quality and consumption signals to diagnose degradation and prioritise engineering improvements Set service objectives and drive capacity load and resilience testing Lead technical incident resolution root-cause analysis and recovery exercises automate recurring remediation within agreed operational controls Establish usage metering cost attribution and demand forecasts across services and consumers Optimise quotas committed capacity routing and caching against cost per successful task performance and quality targets Establish engineering standards and automated controls that support responsible AI adoption Shape the evolution of Control Tower capabilities to meet emerging model agent and enterprise service needs Lead major operational coordination incident management action tracking disaster-recovery readiness and business-continuity evidence while routing security events to the designated Cybersecurity function Manage operational and technical performance of cloud model platform and resource partners against agreed deliverables service levels evidence requirements and knowledge-transfer commitments Maintain first-line operational controls evidence and remediation tracking for change security data protection architecture and audit requirements escape risk acceptance and independent assurance decisions to authorised functions Provide technical leadership to AI platform operations and reliability engineers ensuring architectural alignment engineering quality and resilient delivery across shared AI services Mentor engineers in AI-assisted development lifecycle automation observability and performance optimization building capability to support advanced models and agent systems Drive technical reviews and continuous improvement introducing reusable patterns engineering accelerators and automated controls that strengthen platform reliability cost efficiency and developer experience Manage self and team in line with ADCB s people management policies procedures processes and practices to ensure adherence and to maximize own and employee contribution to business performance Organize and supervise the activities and work of the team to ensure that targets and objectives are achieved and the business plan is delivered in line with the required policies processes procedures and systems Implement approved departmental policies processes and procedures and ensure employee adherence so that work is carried out to the required standard while delivering the required standards of service to customers and stakeholders Manage and motivate the team to ensure they contribute to and participate in the identification and implementation of change initiatives programs and projects in line with the Bank s standards Demonstrate Our Promise and apply the ADCB Service Standards to deliver the Bank s required levels of service in all internal and external customer interactions

At least 15 years of relevant professional experience in platform engineering, enterprise integration, cloud services, reliability engineering, or AI/ML operations, including significant technical leadership in complex production environments.Demonstrated experience in designing, scaling and managing shared technology services, supported by practical experience in enterprise AI platforms, model and agent operations, or AI service integration. Experience within banking or another regulated industry is preferred.Shared platform services, distributed systems, cloud architecture, service design, technical standards, and platform evolution.Generative AI, agentic systems, LLMOps, AgentOps, model lifecycle management, evaluation and emerging AI capabilities API management, AI gateways, model routing, MCP services, enterprise integration, identity, and access controls Azure or AWS. AI observability, distributed tracing, service performance, SRE, service-level objectives, incident diagnosis, and recovery engineering.FinOps, consumption attribution, demand forecasting, capacity planning, performance-cost optimization. DevSecOps, infrastructure code, release automation, policy enforcement, lifecycle controls, and auditable evidence.Engineering reviews, team development, technical roadmap ownership, vendor assessment and collaboration across product, architecture, security, and operations. Executive reporting; vendor management; audit evidence; people leadership and cross-functional influence.