project engineer

planning engineer

ai engineer

architect

data architect

development architect

bankfab

<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

EPAM Systems

<div><p>We're looking for a <b>Python Engineering Lead</b> to join our team in Abu Dhabi, UAE in an onsite working mode. In this role, you will lead the engineering team responsible for delivering a secure Nuclear Material Accounting and Control platform. The position combines hands-on Python development with full-cycle engineering leadership to ensure technical excellence and timely delivery. You will work closely with the Solution Architect and coordinate cross-functional teams including backend, frontend, QA, DevOps, data and integration engineers. This is a strategic role for someone passionate about secure enterprise application development and AI-assisted engineering practices.</p><p><b>
Responsibilities</b></p><ul><li>
Lead and coordinate the engineering team across all technical workstreams
</li><li>Remain hands-on in developing critical Python backend components
</li><li>Translate architecture and requirements into actionable engineering plans and tasks
</li><li>Manage technical decisions, estimations, dependencies and resolve complex issues
</li><li>Define and enforce coding standards, code reviews, quality gates and automated testing
</li><li>Introduce and govern AI-assisted coding practices throughout the software development lifecycle
</li><li>Ensure AI-generated code meets security, compliance and maintainability standards
</li><li>Oversee system integrations, data integrity, auditability and production-readiness
</li><li>Mentor team members and maintain high delivery quality across disciplines
</li></ul><p><b>Requirements</b></p><ul><li>
Strong experience in Python backend engineering with practical coding expertise
</li><li>Demonstrable experience leading multidisciplinary engineering teams
</li><li>Knowledge of FastAPI, REST API development, PostgreSQL and SQLAlchemy
</li><li>Experience delivering secure, auditable enterprise applications
</li><li>Hands-on expertise with AI coding assistants and AI-driven development workflows
</li><li>Familiarity with automated testing, CI/CD pipelines and secure coding practices
</li><li>Understanding of cloud infrastructure and containerized environments such as Azure, Kubernetes and Docker
</li><li>Ability to work collaboratively with architects, analysts, and client security teams
</li><li>Fluency in English.
</li><li>Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Information Systems, Statistics or a related field.
</li></ul><p><b>Nice to have</b></p><ul><li>
Experience with SAP and enterprise identity system integrations
</li><li>Frontend delivery knowledge using React or Angular
</li><li>Background in nuclear, energy, government or regulated sector environments
</li><li>Exposure to orchestration and data processing tools like Prefect or Databricks</li></ul><p><b>
We offer</b></p><ul><li>
End of service gratuity</li><li>
Private healthcare and life insurance</li><li>
Employee assistance program</li><li>
Wellness program</li><li>
Annual air travel allowance for expatriates</li><li>
Regular performance feedback and salary reviews</li><li>
Global travel medical and accident insurance</li><li>
Referral bonuses</li><li>
Learning and development opportunities including in-house training and coaching, professional certifications, and courses</li></ul></div>

Python Engineering Lead

<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

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

Greenfield Marine Technologies

<p>We are looking for a hands-on <b>GenAI Engineer</b> to design, build, and deploy production-grade AI applications powered by Large Language Models (LLMs). This role is focused on turning business use cases into reliable, scalable AI solutions such as copilots, knowledge assistants, document intelligence tools, and workflow automation systems.</p><p>The ideal candidate combines strong software engineering skills with practical experience in LLM application development, Retrieval-Augmented Generation (RAG), prompt engineering, model evaluation, and API integration. This person should be comfortable moving from prototype to production while optimizing for quality, latency, security, and cost. This builds on the original JD&rsquo;s emphasis on LLM apps, RAG, integrations, vector databases, and cloud deployment.</p><p><b>Key Responsibilities:</b></p><ul><li>Design, develop, and deploy GenAI applications using LLMs for real-world business workflows.</li><li>Build AI-powered solutions such as chatbots, copilots, knowledge assistants, search assistants, and workflow automation tools</li><li>Architect and optimize RAG pipelines using structured and unstructured enterprise data.</li><li>Develop robust prompt strategies, guardrails, and response orchestration for accuracy and safety.</li><li>Integrate AI capabilities into web applications, backend systems, APIs, and enterprise platforms.</li><li>Work with vector databases and retrieval systems to improve relevance and response quality.</li><li>Evaluate model performance using defined quality metrics, test cases, and user feedback loops.</li><li>Improve production performance across latency, cost, scalability, observability, and reliability.</li><li>Collaborate with product, engineering, and business teams to translate use cases into deployable AI features.</li><li>Stay updated with emerging GenAI tools, frameworks, and model capabilities, and recommend practical adoption where relevant.</li></ul><p><b>Must-Have Skills:</b><br/>Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.<br/>Strong hands-on programming experience in Python.<br/>Experience building GenAI applications using frameworks such as LangChain, LlamaIndex, or equivalent orchestration tools.<br/>Experience working with OpenAI APIs, Anthropic, Bedrock, Azure OpenAI, or similar LLM platforms.<br/>Strong understanding of prompt engineering, RAG architecture, and LLM application design.<br/>Experience integrating AI models with REST APIs, web applications, and backend services.<br/>Familiarity with vector databases such as FAISS, Pinecone, Weaviate, Chroma, or similar.<br/>Good understanding of machine learning and NLP fundamentals.</p><p>Experience deploying production-ready applications on <b>AWS, Azure, or GCP</b>.</p><p>Ability to evaluate and improve model quality, including hallucination reduction and retrieval performance.</p><p>Understanding of security, access control, and safe handling of enterprise data in AI applications.</p><p><b>Preferred Skills :</b><br/>Experience with Amazon Bedrock.<br/>Experience with multimodal AI applications involving text, images, PDFs, and document processing.<br/>Familiarity with fine-tuning, adapters, or domain adaptation techniques for LLMs.</p><p>Experience with Docker, Kubernetes, CI/CD pipelines, and MLOps workflows.</p><p>Exposure to observability and monitoring tools for AI systems.</p><p>Experience building domain-specific assistants for operations, compliance, technical support, or enterprise knowledge systems.</p><p><b>Experience Required:</b></p><p><b>2&ndash;3 years</b> of experience in software engineering, machine learning, or AI application development.</p><p>At least <b>1&ndash;2 years</b> of practical experience working on GenAI / LLM-based applications in production or advanced PoCs.</p><p>Job Types: Full-time, Permanent</p><p>Ability to commute/relocate:</p><ul><li>Abu Dhabi: Reliably commute or planning to relocate before starting work (Required)</li></ul><p>Application Question(s):</p><ul><li>What is your Visa Status?</li><li>What is your Current Salary?</li><li>What is your Expected Salary?</li><li>Visa Expiry Date?</li><li>Duration of Notice Period?</li><li>Mention your available dates for an online interview</li></ul><p>Experience:</p><ul><li>GenAI: 2 years (Required)</li></ul><p>Location:</p><ul><li>Abu Dhabi (Required)</li></ul><p>Work Location: In person</p>

GenAI Engineer

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

Jex Recruitment | Connecting top talent with leading companies

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  <strong>
   Staff AI Software Engineer | Abu Dhabi
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  We're hiring on behalf of a major Abu Dhabi-based enterprise that's scaling its applied AI capability, building AI/ML systems that plug directly into core operations, risk, and customer-facing products.
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   The Role
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  As a Staff AI Software Engineer, you'll be a senior technical leader, designing and shipping production-grade AI/ML systems at scale. This is a hands-on, high-influence role for someone who wants to shape how a major regional institution builds and deploys AI.
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   What you'll do
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   Architect and build scalable AI/ML systems, from prototype to production
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   Lead technical design decisions across the engineering team
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   Partner with data science, product, and engineering to translate business problems into deployable AI solutions
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   Set and champion engineering best practices, code quality, MLOps, model monitoring, and responsible AI
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   Mentor engineers and act as a technical reference point across the function
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   What we're looking for
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   Strong software engineering background (Python and/or relevant production languages)
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   Proven experience building and deploying ML/AI systems at scale (LLMs, NLP, or applied ML)
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   Solid grasp of MLOps, cloud infrastructure, and system design for production AI
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   Staff/Principal-level experience, someone who's operated as a senior IC or technical lead
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   Comfortable working in a regulated, enterprise environment
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   Why this role
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   Abu Dhabi, one of the region's fastest-growing hubs for applied AI in financial services
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   Ground-floor opportunity to shape AI strategy and engineering culture within a well-resourced innovation function
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   High-impact, high-visibility mandate within a major regional institution
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Staff Software Engineer

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