enterprise architect & data governance head

ai engineer

data architect

business analyst – data & analytics Jobs in Abu Dhabi

business analyst – data & analytics

enterprise architect & data governance head Jobs in Abu Dhabi

it engineer

enterprise data architect

Damen Middle East

<p><b>Data &amp; AI Transformation Architect</b></p><p>Abu Dhabi, UAE | Full-Time</p><p>Ready to help shape the future of Data &amp; AI in a global maritime organization?</p><p>Damen is looking for an experienced <b>Data &amp; AI Transformation Architect</b> to help accelerate our journey toward becoming a truly data-driven and AI-enabled enterprise. This is a hands-on, strategic role focused on Data Architecture, Data Governance, Enterprise Data Modelling, Master Data Management (MDM), and modern Data &amp; AI platforms.</p><p><b>What You'll Do</b></p><ul><li>Lead enterprise Data &amp; AI transformation initiatives</li><li>Develop and improve enterprise data architecture and governance frameworks</li><li>Design and evolve conceptual, logical, and physical data models</li><li>Drive adoption of Data Governance, MDM, and AI best practices</li><li>Collaborate with business and technology stakeholders globally</li><li>Help shape Damen's future Data &amp; AI platform and operating model</li></ul><p><b>What We're Looking For</b></p><ul><li>10+ years of experience in <b>Data Architecture, Data Governance, MDM, Data Management, or Data Transformation</b></li><li>Strong experience with <b>Enterprise Data Modelling</b></li><li>Hands-on experience with Microsoft Azure, Fabric, Databricks, Purview, or similar technologies</li><li>Experience in manufacturing, marine, shipbuilding, engineering, construction, energy, utilities, or other asset-intensive industries</li><li>Strong stakeholder management and consulting skills</li><li>Experience driving enterprise-wide transformation and data maturity programs.</li></ul><p><b>Why Join Damen?</b></p><p>Join a global organisation investing heavily in Data, AI, and digital transformation. You'll work with international stakeholders, influence strategic initiatives, and play a key role in building a world-class Data &amp; AI capability that supports the future of the maritime industry.</p><p>Pay: AED15,000.00 - AED20,000.00 per month</p><p>Application Question(s):</p><ul><li>Has your experience been primarily limited to reporting, dashboards, business analysis, or data science without responsibility for enterprise architecture or governance? </li></ul><p>Experience:</p><ul><li>Data Architecture transformation programme: 5 years (Required)</li><li>Enterprise Data: 5 years (Required)</li></ul><p>Work Location: In person</p>

Data & AI Transformation Architect

DICETEK LLC

<div><p><b>JOB PURPOSE:
</b></p><p>We are looking for an experienced AI Architect responsible for architecting enterprise-wide Generative AI and Agentic AI capabilities across banking systems. The role will define target architecture, integration patterns, standards, reference implementations and reusable building blocks for full-fledged AI chat assistants, autonomous agents and multi-agent workflows.</p><p>
The AI Architect will lead end-to-end architecture for agentic retail banking journeys such as payments, transfers, servicing, self-service fulfilment and customer assistance, ensuring secure integration with enterprise APIs, middleware, core banking platforms and customer-facing channels across web and mobile.</p><p>
This role requires deep hands-on AI engineering capability, strong banking domain understanding, practical delivery experience with multiple production-grade banking agents, and the ability to collaborate with product, engineering, infrastructure, cybersecurity, data, governance and enterprise architecture teams to present and align designs through ARB.</p><p></p><p><b><br>
QUALIFICATIONS &amp; EXPERIENCE:
</b></p><p><b>Minimum Qualification</b></p><ul><li>Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Software Engineering, Information Technology, Artificial Intelligence, Data Science or a related discipline.</li><li>Relevant certifications in cloud architecture, AI engineering, security architecture, enterprise architecture or machine learning are preferred.</li></ul><p></p><p><b><br>
Minimum Experience</b></p><ul><li>Senior technology professional with around 12+ years of experience across software engineering, architecture, cloud/platform engineering and enterprise solution delivery.</li><li>Minimum 4+ years of hands-on AI engineering or AI architecture experience, including Generative AI, LLM applications and Agentic AI solutions.</li><li>Proven experience architecting and delivering multiple banking agents or full-fledged banking chat assistant capabilities integrated with enterprise systems.</li><li>Strong understanding of banking systems, retail banking journeys, payments, transfers, servicing, customer self-service, operational controls and regulatory/security considerations.</li><li>Full AI Engineer capability including Python, API integration, microservices, event-driven design, RAG implementation, model/agent evaluation and cloud-native deployment practices.</li><li>Hands-on experience with agent frameworks and orchestration platforms such as Microsoft Semantic Kernel, AutoGen, LangChain, LangGraph and similar frameworks.</li><li>Experience architecting MCP servers, tool integration layers, agent-to-agent communication, UI integration patterns and agent interoperability protocols such as MCP, A2A and A2UI.</li><li>Strong experience with Azure AI services, Azure OpenAI Service, AWS AI services, Amazon Bedrock and related model deployment/management capabilities.</li><li>Experience designing secure AI systems with zero trust principles, identity and access controls, data protection, secure API design, PII redaction and privacy-by-design controls.</li><li>Experience presenting solution architecture, trade-off analysis, ADRs and architecture recommendations to ARB or equivalent architecture governance forums.</li><li>Experience recommending infrastructure architecture for AI platforms including compute, Kubernetes, serverless, vector databases, observability, monitoring, data pipelines and connectivity.</li><li>Strong capability to collaborate with engineering, product, cybersecurity, infrastructure, operations, data, compliance and enterprise architecture teams.</li></ul><p></p><p><b><br>
Key Technical Skills</b></p><ul><li>Enterprise Agentic AI architecture, multi-agent systems, autonomous workflows, human-in-the-loop design and full-fledged chat assistant architecture.</li><li>LLMs, prompt engineering, context engineering, memory design, tool/function calling, agent orchestration, model/agent evaluation and cost/latency optimization.</li><li>RAG architecture, semantic indexing, embeddings, vector databases, retrieval optimization, reranking, grounding, answer relevancy and explainability patterns.</li><li>MCP server architecture, tool registries, multi-tool integration, A2A, A2UI, agent interoperability protocols and AI ecosystem design.</li><li>Azure AI, Azure OpenAI, AWS AI services, Amazon Bedrock, Kubernetes, serverless, microservices, APIs, event-driven architecture and observability.</li><li>Security architecture for AI systems including zero trust, PII redaction, data masking, privacy controls, guardrails, secure logging and auditability.</li></ul><p></p><p><b><br>
Behavioural / Leadership Skills</b></p><ul><li>Strategic architecture thinking with the ability to define enterprise standards, influence platform direction and simplify complex technical decisions.</li><li>Strong stakeholder communication with the ability to present architecture options, risks, trade-offs and recommendations to senior leadership and ARB forums.</li><li>Collaborative leadership style with the ability to work across business, product, engineering, cybersecurity, data and infrastructure teams.</li><li>Hands-on problem-solving mindset, pragmatic decision making, ownership, mentoring capability and commitment to high-quality secure delivery.</li></ul><p></p><p><b><br>
Technical Competencies</b></p><ul><li>Enterprise Agentic AI architecture and banking-grade AI ecosystem design.</li><li>Retail banking agent architecture for payments, transfers, servicing and self-service workflows.</li><li>RAG, memory, context engineering, evaluation, tool orchestration and MCP server architecture.</li><li>AI security architecture, zero trust, PII redaction, guardrails, auditability and governance.</li><li>Azure and AWS AI services, cloud-native infrastructure, Kubernetes, serverless and observability for agent platforms.</li><li>Architecture documentation, ADR creation, ARB presentation and cross-team design governance.</li></ul><p></p><p><b><br>
Skills:</b> AI Architecture , LLMs , Azure AI</p></div>

AI Architect – Enterprise Agentic AI Architecture

bankfab

<div><div>Job Description</div></div><div></div>
<br><div><div>Responsibilities</div><div><br>
 <p><br>
<b>JOB SUMMARY / INTRODUCTION
</b></p><ul><li>To lead the strategic design, architecture, and enterprise build-out of the bank's AI &amp; Agentic Platform capability, ensuring it serves as a trusted, scalable, and governed foundation for AI-driven decisioning, agentic automation, and enterprise knowledge across the Group.
</li><li>The role is pivotal in shaping how AI capability is built, integrated, and adopted across business units, partnering with senior business and technology stakeholders to translate the bank's AI ambition into a coherent platform and solution architecture aligned with FAB's digital, agentic, and AI roadmap, regulatory expectations, and Group strategy.
</li><li><p>The role combines deep technical authority on AI platform architecture with the strategic judgement and stakeholder reach required to direct enterprise AI capability across a tier-one bank.
</p><p><b>KEY RESPONSIBILITIES
</b></p></li><li>Define and own the target architecture for the AI &amp; Agentic Platform, covering the agentic runtime, action gateway, control plane, knowledge foundation, model gateway and abstraction, evaluation, and observability layers.
</li><li>Set and evolve the cloud architecture for AI workloads across Azure and AWS, including hybrid model serving, inference economics, network and identity design, data residency, and the build-versus-buy positioning across managed services and bank-built capabilities.
</li><li>Lead the integration architecture between the AI Platform and the bank's core estate, including core banking, risk and credit systems, treasury and finance platforms, the enterprise data platform (Azure and Cloudera), enterprise APIs, ESB, and identity services.
</li><li>Establish and govern the engineering, architectural, and AI engineering standards adopted across the AI Platform team, including reference patterns, agent and tool contracts, runtime governance hooks, evaluation discipline, and integration with model risk.
</li><li>Shape FAB's multi-year AI capability direction, including build-versus-buy positioning, foundation model strategy, proprietary capability investment, and platform evolution in response to changes in the agentic AI and regulatory landscape.
</li><li>Partner with the Senior, Platform Engineering and Architecture lead in a two-in-a-box operating model, with a clear separation between platform direction, target architecture, and enterprise positioning (this role) and platform engineering execution and run-state.
</li><li>Build enterprise AI capability across the Group through reference architectures, standards, communities of practice, internal forums, and direct mentorship of senior engineers and architects across DAAI and the wider technology function.
</li><li>Engage business heads and their CIOs across Wholesale Banking, Personal Banking, Private Banking, Treasury, Risk, and Group Finance to shape how AI is integrated into their operating models, beyond individual use cases.
</li><li>Represent the AI &amp; Agentic Platforms function in forums with Group Technology, Enterprise Architecture, Cyber, Model Risk, Internal Audit, CBUAE, and external regulators on matters of AI architecture, governance, and trusted agentic operations.
</li><li>Manage strategic technology partnerships with hyperscalers, foundation model providers, agentic framework vendors, and SI partners on terms that protect
</li><li>FAB's intellectual property, capability, and optionality.
</li><li><p>Represent FAB externally in industry forums, regulator working groups, and peer banks, contributing to the Group's thought leadership on enterprise agentic AI.
</p><p><b>REQUIREMENTS / QUALIFICATIONS
</b></p><p><b>Technical Expertise
</b></p></li><li>Deep, current architectural knowledge of agentic AI systems, including the layered protocol stack (MCP for tool access, A2A for inter-agent coordination, ACP and emerging agentic standards), orchestration frameworks (LangGraph, Google ADK, LlamaIndex, Autogen, CrewAI, or equivalent), planning and reasoning patterns, multi-agent systems, evaluation, observability, and runtime governance.
</li><li>Strong working knowledge of foundation model architecture and integration patterns, including managed model APIs (Azure AI Foundry, AWS Bedrock, Google Vertex AI), open-weight model serving, model gateway design, prompt and context management, RAG, fine-tuning, and the cost-quality-latency trade-offs across model selection.
</li><li>Cloud architecture fluency on Azure and AWS at enterprise scale, covering networking, identity, data, model serving, inference cost management, and hybrid deployment patterns.
</li><li>Proven expertise in integrating AI and data platforms into the core estate of a bank or comparably regulated enterprise, including core banking systems, risk and credit platforms, enterprise data platforms (Azure, Databricks, Cloudera), APIs, ESB, and identity layers.
</li><li><p>Strong grounding in software and platform engineering, with ongoing hands-on credibility in architecture reviews, prototyping, and critical design decisions.
<br>
Working knowledge of AI governance, model risk management, and regulatory expectations applicable to AI in financial services, including CBUAE, EU AI Act, NIST AI RMF, and equivalent global frameworks.
</p><p><b>Strategic &amp; Leadership Skills
</b></p></li><li>Demonstrated ability to influence at executive level, including business heads, Group CTTO / CIO, board-level forums, and external regulators.
<br>
Strong stakeholder management across business, technology, risk, audit, and external partner ecosystems.
</li><li><p>Track record of building enterprise capability through standards, communities of practice, and the development of senior engineering and architecture talent.
<br>
Ability to navigate ambiguity, manage competing priorities across multiple business units, and translate strategic intent into deliverable platform architecture.
<br>
Excellent written and verbal communication, with the ability to engage equally credibly with engineers, business heads, and regulators.
</p><p><b>Minimum Experience
</b></p></li><li>12+ years in software, platform, or AI engineering and architecture, with significant time spent at principal, staff, or distinguished engineer / architect level.
</li><li>Demonstrated architectural ownership of at least one large-scale AI, data, or platform programme in production at enterprise scale.
</li><li>Substantive experience in financial services, banking, or another comparably regulated industry.
</li><li><p>Track record of partnering with executive-level stakeholders across business and technology functions.
</p><p><b>Minimum Qualifications
</b></p></li><li>Bachelor's degree in Computer Science, Software Engineering, Electrical Engineering, Mathematics, or a related quantitative discipline.
<br>
Preferred / Beneficial
</li><li>Master's degree (MSc / MTech / MEng) in Computer Science, Artificial Intelligence, Machine Learning, or a related discipline preferred.
<br>
Relevant professional certifications such as cloud architecture (Azure Solutions Architect Expert, AWS Solutions Architect Professional), TOGAF, or equivalent considered an advantage.
</li><li>Doctoral qualification (PhD) in a relevant discipline considered an advantage but not required.</li></ul></div></div>
<br><div><div>Qualifications</div></div><div></div>
<br><div><div>About Us</div>
<div><div><b>About Us:<br>
</b></div><div></div><br>
<div>First Abu Dhabi Bank (FAB) is the largest bank in the UAE and one of the world's largest and safest financial institutions. We offer a comprehensive range of personal and private banking services, including credit cards, Islamic banking, investments, loans, and mortgages. Our commitment to excellence and innovation drives us to provide top-tier financial solutions to our clients.<br>
</div><div></div><br>
<div><b>Life at FAB:<br>
</b></div><div></div><br>
<div>Working at FAB means being part of a team of talented and passionate individuals with a shared vision to support the ambitions of our stakeholders to &quot;Grow Stronger.&quot; We embrace the needs of our customers across the globe with a sense of responsibility and confidence driven by extensive expertise that can only be delivered by an ambitious world-class organization<br>
</div><div></div><br>
<div><b>Career Development:<br>
</b></div><div></div><br>
<div>FAB offers unique career choices, a chance to innovate, craft solutions for the future, and express yourself in a performance-based culture that will unleash the best in you. Whether you are an experienced professional or just starting your career, FAB provides a range of learning and development initiatives to support all employees through training and skill development.<br>
</div><div></div><br>
<div><b>Our Values:<br>
</b></div><div></div><br>
<div>At FAB, we place our customers at the core of our activities, live our values each day in every way, celebrate achievements, and empower each other to deliver leading solutions. We have structured plans for the recruitment and career progression of Emirati talent to enable them to make a mark in the financial and banking sector both regionally and globaly that rewards your hard work and dedication.<br>
</div><div></div><br>
<div>Join our team and be part of a journey to shape the future of banking.</div></div></div><div></div>

Principal Lead, AI Platform & Solutions

<div><div>Job Description</div>
<div>
 <p><b>JOB SUMMARY / INTRODUCTION
</b></p><ul><li>To lead the engineering, architecture, and run-state ownership of the bank's enterprise AI &amp; Agentic Platform, ensuring it operates as a reliable, secure, observable, and bank-grade production platform supporting agents and AI workloads across the Group.
</li><li>The role is the senior technical authority for platform engineering and architecture across the agentic runtime, model gateway, integration fabric, identity layer, and infrastructure backbone of the platform.
</li><li>It operates in a two-in-a-box model with the existing Platform Product Owner to provide concurrent technical ownership and organizational resilience for the platform, with shared accountability for platform availability, performance, cost, security posture, and architectural evolution.
</li>The role is deeply technical in nature, requiring hands-on engineering depth across distributed systems, agentic protocols, LLM infrastructure, identity and access management, and cloud-native platform engineering.<br>
<li>
 </li></ul><p></p><p><b>KEY RESPONSIBILITIES
 </b></p><p><b>Key Accountabilities &mdash; Platform Architecture<br>
</b><br>
</p><ul><li>Own the end-to-end technical architecture of the AI &amp; Agentic Platform's five-layer stack: Action Gateway, Agent Kernel, Control Plane, Knowledge Foundation, and the Users and Channels layer.
</li><li>Architect and evolve the agentic runtime to natively support the emerging multi-protocol stack: Model Context Protocol (MCP) for agent-to-tool access, Agent-to-Agent (A2A) for inter-agent coordination and task delegation, Agent Communication Protocol (ACP) and equivalent emerging standards, ensuring the platform is interoperable with hyperscaler agent fabrics (Azure AI Foundry agents, AWS Bedrock Agents, Google ADK).
</li><li>Design the platform's agent kernel, including agent lifecycle management, planning and reasoning loops, memory architecture (short-term, long-term, episodic), state management, session affinity, scratchpad persistence, and execution chains.
</li><li>Architect the platform's knowledge foundation, including vector store selection and topology, hybrid retrieval (BM25, dense, graph), embeddings strategy, knowledge graph integration (FIBO, OWL, SHACL, SPARQL), context engineering, and grounding patterns.
</li><li><p>Design the platform's control plane, including policy-gated execution, Know Your Agent (KYA) enforcement at runtime, agent registries, tool registries, capability discovery, evaluation pipelines, and tracing and lineage at agent and tool granularity.
</p><p><b>Key Accountabilities &mdash; Integrations &amp; Model Layer
</b></p></li><li>Architect and engineer the platform's LLM gateway and model abstraction layer, providing a unified interface across foundation model providers (Azure AI Foundry, AWS Bedrock, OpenAI, Anthropic, Google Vertex AI, Cohere) with intelligent routing, fallback, retries, prompt and response caching, semantic caching, rate limiting, token accounting, cost attribution, and tenant isolation.
</li><li>Design model serving patterns for managed APIs, dedicated capacity (PTUs / provisioned throughput), and self-hosted open-weight models on GPU infrastructure (vLLM, TGI, Triton, or equivalent), with appropriate trade-offs across cost, latency, sovereignty, and compliance.
</li><li>Lead the integration architecture between the AI Platform and the bank's core estate, including core banking, payments, treasury, credit and risk systems, the enterprise data platform (Azure, Cloudera, Databricks), enterprise APIs, ESB, event streaming (Kafka, Event Hubs), and the data product layer.
<br>
Design the action gateway as the bank's enforcement boundary for agentic action, including API mediation, contract enforcement, circuit breakers, idempotency guarantees, transactional safety, and audit-grade action logging.
</li><li><p>Engineer the platform's tool layer and MCP server estate, including tool packaging, versioning, capability advertisement, schema enforcement, and runtime tool discovery across Wholesale, Retail, and Group functions.
</p><p><b>Key Accountabilities &mdash; Identity, Security &amp; Governance
</b></p></li><li>Architect and own the agent identity and workload identity model for the platform, including non-human identity (NHI) management, agent identity lifecycle, blended user-plus-agent identity for delegated actions, and the identity propagation chain across multi-agent flows.
<br>
Design the platform's authentication and authorization architecture, including OAuth 2.1 and OIDC flows for agent-to-tool and agent-to-API interactions, just-in-time credential issuance, short-lived token exchange, mTLS for agent-to-agent communication, and integration with the bank's enterprise IAM (Microsoft Entra ID, PAM, secrets management).
</li><li>Implement zero-trust principles across the agentic stack, including least-privilege scoping per agent and per task, real-time policy evaluation, behavioral posture checks, and continuous authorization rather than static service-account-style access.
</li><li>Engineer the platform's runtime governance controls, including KYA enforcement, prompt and output guardrails (PII, PHI, MNPI, prompt injection defense), sensitive action approval flows, and human-in-the-loop escalation patterns.
</li><li>Harden the platform to meet CBUAE, internal model risk, and Group governance requirements, including auditability, lineage, data residency, model risk controls, third-party model governance, and OWASP LLM Top 10 alignment.
</li><li>Key Accountabilities &mdash; Infrastructure &amp; Run-State
</li><li>Lead the platform's cloud and infrastructure architecture across Azure (primary) and AWS, including infrastructure as code (Terraform), networking (private endpoints, peering, egress control), Kubernetes (AKS) and container orchestration, secrets management, and CI/CD pipelines.
</li><li>Own platform Site Reliability Engineering (SRE), including SLO design, error budget management, observability (OpenTelemetry, traces, metrics, logs at agent and tool granularity), incident response, post-mortems, capacity planning, and cost governance for a growing fleet of agents and AI workloads in production.
<br>
Operate in genuine two-in-a-box with the existing Platform Product Owner, including shared on-call, shared roadmap ownership, and shared accountability for major architectural decisions, regulator conversations, and critical incidents.
</li><li>Drive engineering excellence across the platform team, including testing discipline (unit, integration, evaluation, red-teaming), documentation, infrastructure as code maturity, operational readiness reviews, and technical mentorship of platform engineers.
</li><li>Represent the platform in senior technical forums with Enterprise Architecture, Cyber, Model Risk, Internal Audit, and the Group CTTO's office on architecture and engineering matters.
</li><li><p>Engage with hyperscale, model provider, and framework vendor technical teams on platform-level integration, performance, sovereignty, and cost optimization.
</p><p><b>REQUIREMENTS / QUALIFICATIONS
</b></p><p><b>Technical Expertise
</b></p></li><li>Deep, current expertise in agentic AI architecture and the modern multi-protocol stack: Model Context Protocol (MCP), Agent-to-Agent (A2A), Agent Communication Protocol (ACP), and the engineering patterns required to build interoperable agentic systems on top of them at platform scale.
<br>
Strong hands-on knowledge of agent orchestration frameworks (LangGraph, Google ADK, LlamaIndex, Autogen, CrewAI, OpenAI Agents SDK, or equivalent), planning and reasoning loops, multi-agent coordination patterns, agent memory systems, and evaluation harnesses (RAGAS, OPIK, LangSmith, Promptfoo, or equivalent).
</li><li>Deep technical knowledge of LLM serving and inference architecture, including managed model APIs (Azure AI Foundry, AWS Bedrock, OpenAI, Anthropic, Google Vertex AI), provisioned throughput / PTUs, self-hosted open-weight models, GPU scheduling, vLLM / TGI / Triton, KV-cache management, batching strategies, and the cost-latency-quality trade-offs that drive model selection.
</li><li>Strong working expertise in LLM gateway and AI gateway architecture, including model routing, fallback and retry, prompt and semantic caching, rate limiting, tenant isolation, token accounting, and policy enforcement at the gateway boundary.
</li><li>Deep experience in retrieval and knowledge architecture, including vector databases (pgvector, Azure AI Search, Pinecone, Weaviate, Qdrant, or equivalent), hybrid retrieval, reranking, embeddings model selection, knowledge graphs (FIBO, RDF, OWL, SHACL, Neo4j, Apache Jena), and context engineering at scale.
<br>
Strong grounding in agent and workload identity, including non-human identity (NHI) governance, agent identity lifecycle, OAuth 2.1, OIDC, SPIFFE / SPIRE-style workload identity, mTLS, just-in-time credentialing, secret-less architectures, and integration with enterprise IAM (Microsoft Entra ID, Azure AD, PAM platforms).
</li><li>Deep proficiency in cloud-native platform engineering on Azure (preferred) and AWS, including Terraform, Kubernetes (AKS / EKS), Helm, service mesh (Istio, Linkerd), API gateways (APIM, Kong, Envoy), private networking, and policy-as-code (OPA, Azure Policy).
<br>
Strong programming proficiency in at least one of Python, Go, or Java, with active engagement in code reviews, design reviews, and platform-level engineering decisions.
</li><li>Strong Site Reliability Engineering background, including production on-call, incident leadership, post-mortem discipline, SLO and error-budget design, capacity planning, and observability tooling (Open Telemetry, Prometheus, Grafana, Datadog, or equivalent) tuned for non-deterministic AI workloads.
<br>
Working knowledge of integration patterns relevant to banking platforms, including event-driven architectures (Kafka, Event Hubs), API gateways, ESB, ISO 20022, payment rails, and core banking integration patterns.
</li><li>Strong grounding in security engineering for regulated industries, including OWASP LLM Top 10, prompt injection defence, model supply-chain security, secrets management, network segmentation, data residency, and audit logging.
<br>
Engineering Leadership &amp; Collaboration
</li><li>Demonstrated ability to operate effectively in a two-in-a-box model with another senior technical owner, including shared accountability, shared on-call, and shared decision-making.
</li><li>Track record of raising engineering standards across a platform team through standards, design reviews, documentation, and direct technical mentorship.
<br>
Strong written and verbal communication, with the ability to engage credibly with engineers, architects, risk and audit functions, hyperscaler and vendor technical teams, and senior business stakeholders.
<br>
Minimum Experience
</li><li>10+ years in software and platform engineering, with significant time spent at senior, staff, or principal engineering / architect level.
<br>
Demonstrated production ownership of at least one platform serving multiple internal or external consumers at enterprise scale, ideally including agentic or LLM workloads.
</li><li><p>Substantive experience in financial services, banking, or another comparably regulated industry.
<br>
Demonstrated SRE and on-call experience on a production platform with meaningful availability and operational requirements.
<br>
Track record of building, integrating, or operating LLM-based or agentic systems in production.
</p><p><b>Minimum Qualifications
</b></p></li><li><p>Bachelor's degree in computer science, Software Engineering, Electrical Engineering, or a related technical discipline.
</p><p><b>Preferred / Beneficial
</b></p></li><li>Master's degree (MSc / MTech / MEng) in Computer Science, Software Engineering, Distributed Systems, or a related discipline preferred.
</li><li>Relevant professional certifications in cloud engineering (Azure Solutions Architect Expert, Azure DevOps Engineer Expert, AWS DevOps Professional, AWS
</li><li>Solutions Architect Professional, Certified Kubernetes Administrator), security (CISSP, CCSP), or SRE considered an advantage.</li></ul></div></div><p></p>
<br><div><div>Responsibilities</div></div><div></div>
<br><div><div>Qualifications</div></div><div></div>
<br><div><div>About Us</div>
<div><div><b>About Us:<br>
</b></div><div></div><br>
<div>First Abu Dhabi Bank (FAB) is the largest bank in the UAE and one of the world's largest and safest financial institutions. We offer a comprehensive range of personal and private banking services, including credit cards, Islamic banking, investments, loans, and mortgages. Our commitment to excellence and innovation drives us to provide top-tier financial solutions to our clients.<br>
</div><div></div><br>
<div><b>Life at FAB:<br>
</b></div><div></div><br>
<div>Working at FAB means being part of a team of talented and passionate individuals with a shared vision to support the ambitions of our stakeholders to &quot;Grow Stronger.&quot; We embrace the needs of our customers across the globe with a sense of responsibility and confidence driven by extensive expertise that can only be delivered by an ambitious world-class organization<br>
</div><div></div><br>
<div><b>Career Development:<br>
</b></div><div></div><br>
<div>FAB offers unique career choices, a chance to innovate, craft solutions for the future, and express yourself in a performance-based culture that will unleash the best in you. Whether you are an experienced professional or just starting your career, FAB provides a range of learning and development initiatives to support all employees through training and skill development.<br>
</div><div></div><br>
<div><b>Our Values:<br>
</b></div><div></div><br>
<div>At FAB, we place our customers at the core of our activities, live our values each day in every way, celebrate achievements, and empower each other to deliver leading solutions. We have structured plans for the recruitment and career progression of Emirati talent to enable them to make a mark in the financial and banking sector both regionally and globaly that rewards your hard work and dedication.<br>
</div><div></div><br>
<div>Join our team and be part of a journey to shape the future of banking.</div></div></div><div></div>

Senior Engineer, Platform Engineering and Architecture

<div><p><b>Description:</b></p><p>
We are looking for an experienced AI Engineer responsible for designing, developing, and deploying Generative AI and Agentic AI solutions that support enterprise-scale business use cases. This role includes building intelligent systems that integrate complex backend services and client-facing applications across web, mobile, and enterprise platforms.</p><p>
</p><p>The primary responsibility will be to design and develop AI-powered applications, autonomous agents, and multi-agent workflows, while coordinating with cross-functional teams across architecture, engineering, product, and business functions. Thus, a commitment to collaborative problem solving, sophisticated design, and product quality is essential.</p><p><b>
KEY ACCOUNTABILITIES:</b></p><ul><li>
Designing and building Generative AI applications using Large Language Models (LLMs)</li><li>
Developing Agentic AI solutions, including autonomous agents, multi-agent orchestration, and workflow-driven decision systems</li><li>
Building solutions using frameworks such as LangChain, LangGraph or similar agent frameworks</li><li>
Implementing RAG architecture with vector databases</li><li>
Knowledge of embeddings, vector DBs, and model evaluation.</li><li>
Implement various Context Engineering strategies to reduce Token Utilization &amp; Latency.</li><li>
Implement RAG Strategies to reduce retrieval latency and answer relevancy.</li><li>
Prompt engineering, tool calling, memory management, and agent orchestration</li><li>
Integrating AI services with enterprise APIs, middle ware, and backend systems</li><li>
Evaluated &amp; Improvise AI Agent Performance using several metrics</li><li>
Deploying scalable AI solutions on Azure / AWS cloud platforms</li><li>
Working with model evaluation, guardrails, observability, and AI governance</li></ul><p><b>
QUALIFICATIONS &amp; EXPERIENCE:</b></p><ul><li>
Bachelor&rsquo;s degree in computer science, Engineering, or a related field.</li><li>
Senior professional with around 10 years of experience.</li><li>
3+ years of relevant experience in AI Engineering</li><li>
Working experience in an agile software development environment with a good understanding of the principles of agile architecture. Strong collaborative mindset for collective decentralized decision making.</li><li>
Demonstrate strong technical skills with a deep understanding of modern architectural styles and practices such as Microservices, Containers, Cloud (AWS, Azure), APIs, Continuous Delivery, Event-driven architecture, Evolutionary architecture, etc., with a passion for hands on coding.</li><li>
Strong foundation knowledge of software architecture concepts, patterns, principles, and quality attributes. Ability to consistently apply them in real-world scenarios with a pragmatic, system thinking, and problem-solving mindset by analyzing architecture trade-offs for delivering high-quality, sustainable solution architecture.</li><li>
Proven leadership skills with a proactive, positive, and growth mindset. Ability to foster and motivate programmers for delivering with craftsmanship. Good personal skills to continuously engage and communicate with an egoless empathetic mindset.</li><li>
Experience and expertise in delivering architecture for large software solutions meeting critical business purposes. Ability to proactively discover technical debts and continuous improvement opportunities of existing live systems. Work closely with the product owner and enterprise architects to influence and prioritize technical backlog items.</li><li>
Strong proficiency in Python (NumPy, pandas, FastAPI, PyTorch/TensorFlow).</li><li>
Experience with Agent framework LangChain, Lang Graph.</li><li>
Architect end-to-end data indexing pipelines optimized for semantic search and RAG.</li><li>
Design resilient data ingestion frameworks that ensure high availability and low latency for vectorized datasets.</li><li>
Experience deploying and managing models via Amazon Bedrock, Azure OpenAI Service, and Google Vertex AI.</li><li>
Integration of pre-built AI APIs for Speech-to-Text, Computer Vision, and Natural Language Processing (NLP).</li><li>
Leveraging serverless architectures (e.g., AWS Lambda, Azure Functions) to trigger AI inference and data pre-processing.</li><li>
Experience working with CI/CD tools like Jenkins/Gitlab in Cloud Native environments.</li><li>
Experience in setting up pipeline for cloud based and on-prem based application with static code analysis, requirement tagging in Jira.</li><li>
Experience working with one or more DevOps tools &amp; config management tools.</li><li>
Experience in managing and deploying AI workloads in Kubernetes.</li><li>
Experience operating monitoring tools for traditional and cloud environments.</li><li>
Strong analytical mind for problem solving.</li></ul></div><p></p>

Senior AI Engineer

AppliedAI

<div>
 <p>
  <strong>
   AI Solutions Consultant
  </strong>
 </p>
 <p>
  On-site | Abu Dhabi
 </p>
 <p>
  <br/>
 </p>
 <p>
  AppliedAI is a pioneering AI technology company headquartered in Abu Dhabi, UAE. We are committed to innovation and excellence in artificial intelligence solutions across regulated industries such as healthcare, insurance, government, and financial services.
 </p>
 <p>
  <br/>
 </p>
 <p>
  AppliedAI is at the forefront of redefining the future of work through cutting-edge AI solutions. We empower organizations by automating complex, document-heavy processes, ensuring unparalleled efficiency and accuracy.
 </p>
 <p>
  <br/>
 </p>
 <p>
  Our commitment to synergizing human intelligence with artificial intelligence helps our clients excel in highly regulated industries, including healthcare, finance, and insurance.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Role Overview:
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  We are seeking a Solutions Consultant to join our growing post-sales Delivery Team, responsible for ensuring client success with our agentic AI workflow orchestration platform,
  <strong>
   Opus
  </strong>
  .
 </p>
 <p>
  <br/>
 </p>
 <p>
  In this role, you will work directly with enterprise clients after the sale to translate requirements into detailed technical designs and guide implementation.
 </p>
 <p>
  <br/>
 </p>
 <p>
  You&rsquo;ll demonstrate strong technical confidence and analytical skills, and will have the ability to balance solution design with hands-on technical oversight and stakeholder management.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Key Responsibilities:
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Client Engagement &amp; Discovery
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <ul>
  <li>
   Lead deep-dive discovery sessions to identify needs and drivers beyond surface-level requirements.
  </li>
  <li>
   Partner with client teams to define workflows and align on architecture decisions while building internal advocates and champions.
  </li>
  <li>
   Build trusted technical relationships with client stakeholders, acting as a go-to adviser for orchestration design.
  </li>
  <li>
   Navigate complex organizational dynamics to ensure alignment between IT and Operations.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Solution Design &amp; Documentation
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <ul>
  <li>
   Architect business cases that quantify ROI and P&amp;L impact to create urgency for change.
  </li>
  <li>
   Define integration points with client systems including APIs, data sources, and cloud infrastructure.
  </li>
  <li>
   Maintain design decision logs, system interface definitions, and solution playbooks for each engagement.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Technical Leadership
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <ul>
  <li>
   Provide deep technical guidance to both Provide clear and actionable instructions to client teams and internal implementation engineers.
  </li>
  <li>
   Identify and resolve technical blockers and system constraints during the delivery phase.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Outcome-Focused Delivery
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <ul>
  <li>
   Conduct demonstrations that focus on business outcomes rather than just feature tours.
  </li>
  <li>
   Serve as an escalation point for complex technical challenges during delivery.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Key Requirements:
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  Experience:
 </p>
 <p>
  <br/>
 </p>
 <ul>
  <li>
   5+ years in solutions consulting, technical consulting, or technical business analysis roles.
  </li>
  <li>
   Proven post-sales experience working directly with enterprise clients
  </li>
  <li>
   Strong background in workflow automation, distributed systems, or AI/ML-driven platforms.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  Technical Skills:
 </p>
 <p>
  <br/>
 </p>
 <ul>
  <li>
   Technical Confidence:Expert in discussing AI, APIs, and integrations (e.g., webhooks) without requiring regular engineering support.
  </li>
  <li>
   Requirements Engineering:Expert in translating vague use cases into robust technical solutions and sequence diagrams.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  Soft Skills:
 </p>
 <p>
  <br/>
 </p>
 <ul>
  <li>
   Effective Stakeholder Engagement: Ability to re-frame scepticism and stay calm when challenged by stakeholders.
  </li>
  <li>
   Executive Presence: Comfortable challenging C-suite thinking and shifting from features to strategic impact.
  </li>
  <li>
   Collaborative Mindset: Able to interface effectively with client engineers, architects, and executives.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   What We Offer:
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <ul>
  <li>
   Opportunity to shape the marketing analytics strategy at a leading AI company.
  </li>
  <li>
   Exposure to cutting-edge AI/ML applications in real-world business contexts.
  </li>
  <li>
   Collaborative, innovative, and growth-driven work environment.
  </li>
  <li>
   Competitive compensation, benefits, and career advancement opportunities.
  </li>
 </ul>
</div>


AI Solutions Consultant

Accenture

<div><div><h2 class="jobSectionHeader"><b>We Are</b></h2><p>
Accenture helps organizations harness the power of data, analytics, and artificial intelligence to reinvent how they operate, compete, and create value. Our Enterprise AI practice combines strategy, technology, and industry expertise to help clients accelerate AI adoption, scale innovation, and unlock measurable business outcomes responsibly and sustainably.</p><p>
As part of the Enterprise AI Value Strategy team, you will help clients define their AI vision, prioritize high-value use cases, accelerate adoption, and establish the operating models, governance frameworks, and product strategies required to realize enterprise-wide value from AI investments.</p></div><p></p><div><h2 class="jobSectionHeader"><b>
The Work</b></h2><p>
As an Enterprise AI Value Strategy Manager, you will lead the design and execution of AI-driven business transformation initiatives, helping organizations translate emerging AI capabilities into tangible business value. You will collaborate with business leaders, data scientists, engineers, and technology stakeholders to develop AI products, define strategic roadmaps, and scale AI adoption across the enterprise.</p><p>
Key responsibilities include:</p><ul><li>
Lead enterprise AI strategy and value realization engagements from ideation through implementation.</li><li>
Define AI product vision, business cases, and adoption strategies aligned with organizational goals.</li><li>
Identify, prioritize, and develop AI-driven opportunities that create measurable business value and competitive advantage.</li><li>
Partner with data scientists, engineers, and business stakeholders to design and deliver innovative AI solutions.</li><li>
Establish governance frameworks and responsible AI practices to ensure ethical, secure, and compliant AI deployment.</li><li>
Drive AI adoption and organizational change by developing scaling strategies and operating models.</li><li>
Monitor AI product performance and continuously refine solutions based on user feedback and business outcomes.</li><li>
Facilitate executive workshops and stakeholder alignment sessions to build consensus around AI priorities and investment decisions.</li><li>
Evaluate emerging AI technologies, market trends, and industry developments to inform strategic recommendations.</li><li>
Develop enterprise AI roadmaps that balance innovation, risk management, scalability, and value realization.</li><li>
Serve as a trusted advisor to senior client executives on AI transformation, investment priorities, and business impact.</li><li>
Support business development activities, proposal creation, thought leadership, and practice growth initiatives.</li></ul></div></div>
<br>
<div><div><h2 class="jobSectionHeader"><b>Here's What You Need</b></h2><ul><li>
Minimum 7 years of experience in AI strategy, data and AI consulting, digital transformation, product management, analytics, or technology strategy.</li><li>
Experience leading AI adoption, AI transformation, or enterprise-scale digital transformation initiatives.</li><li>
Proven experience developing AI business cases, product strategies, or innovation roadmaps.</li><li>
Strong understanding of Artificial Intelligence, Generative AI, Machine Learning, and emerging AI technologies.</li><li>
Experience working with executive stakeholders to define strategic priorities and drive enterprise change.</li><li>
Bachelor's degree in Computer Science, Engineering, Information Systems, Business, Data Science, or a related discipline.</li></ul><h2 class="jobSectionHeader"><b>
Bonus Points If You Have</b></h2><ul><li>
Advanced proficiency in Artificial Intelligence (AI).</li><li>
Advanced proficiency in Product Innovation Strategy.</li><li>
Experience with AI Architecture and AI Workflow Integration.</li><li>
Experience leading AI product management or AI platform initiatives.</li><li>
Knowledge of Responsible AI, AI governance, and risk management frameworks.</li><li>
Experience within financial services, banking, or highly regulated industries.</li><li>
Strong consulting, stakeholder management, and executive communication skills.</li><li>
MBA or Master's degree in a relevant discipline.</li><li>
Experience with cloud AI platforms, data ecosystems, and enterprise technology architectures.</li></ul><h2 class="jobSectionHeader"><b>
Professional Skills</b></h2><ul><li>
Enterprise AI Strategy</li><li>
AI Value Realization</li><li>
AI Product Management</li><li>
AI Adoption &amp; Change Management</li><li>
Product Innovation Strategy</li><li>
Responsible AI &amp; Governance</li><li>
Executive Stakeholder Management</li><li>
Data &amp; AI Transformation</li><li>
Workshop Facilitation</li><li>
Program &amp; Project Leadership</li><li>
Business Case Development</li><li>
Cross-Functional Team Leadership</li></ul><h2 class="jobSectionHeader"><b>
What Success Looks Like</b></h2><ul><li>
Accelerate AI adoption and business value realization for clients.</li><li>
Define and execute scalable AI strategies aligned with organizational objectives.</li><li>
Deliver measurable outcomes through responsible and sustainable AI transformation.</li><li>
Build trusted relationships with client executives and key stakeholders.</li><li>
Foster innovation while balancing governance, risk, and compliance requirements.</li><li>
Drive thought leadership in Enterprise AI and emerging technology trends.</li></ul></div></div><p></p>

AI Product Manager

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