project engineer

devops engineer

planning engineer

ai engineer

quality engineer

ai platform engineer

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

Halian | Managed Services, Recruitment Agency & Contract Staffing

<div>
 We are seeking an experienced Senior AI Engineer to design, develop, and deploy enterprise-grade AI solutions across complex and large-scale business environments. The ideal candidate will have a strong software engineering background combined with deep expertise in modern AI technologies, Retrieval-Augmented Generation (RAG), LLM integrations, and enterprise system connectivity. This role requires someone who has successfully delivered AI-enabled products within complex organizations, integrating multiple business systems, cloud services, and data platforms while maintaining scalability, security, and performance.
 <br/>
 <br/>
 Key Responsibilities
 <br/>
 <br/>
 <ul>
  <li>
   Design and develop AI-powered applications using modern LLM frameworks and OpenAI technologies.
  </li>
  <li>
   Build and optimize Retrieval-Augmented Generation (RAG) solutions leveraging structured and unstructured enterprise data.
  </li>
  <li>
   Develop scalable backend services using Java Spring Boot and Python.
  </li>
  <li>
   Integrate OpenAI SDKs and AI services into enterprise applications.
  </li>
  <li>
   Implement observability, tracing, evaluation, and monitoring using Langfuse.
  </li>
  <li>
   Build and maintain APIs and microservices supporting AI workloads.
  </li>
  <li>
   Design and optimize data models within PostgreSQL (PGSQL) environments.
  </li>
  <li>
   Develop frontend and mobile experiences using React Native.
  </li>
  <li>
   Integrate Microsoft ecosystem services including Microsoft Graph API, WorkIQ, and other Microsoft platform capabilities.
  </li>
  <li>
   Deliver integrations with enterprise platforms including ERP systems such as SAP, Oracle, Microsoft Dynamics, or similar solutions.
  </li>
  <li>
   Collaborate with Product Owners, Architects, Security teams, and business stakeholders to define AI use cases and technical solutions.
  </li>
  <li>
   Ensure AI solutions meet enterprise requirements for scalability, security, governance, and reliability.
   <br/>
   <br/>
  </li>
 </ul>
 Required Skills &amp; Experience
 <br/>
 <br/>
 <ul>
  <li>
   Minimum 5 years of software engineering experience with proven delivery of enterprise-scale applications.
  </li>
  <li>
   Demonstrated experience working on large-scale, complex projects involving multiple integrations, stakeholders, and high-volume workloads.
  </li>
  <li>
   Strong hands-on experience with Python development.
  </li>
  <li>
   Strong experience with Java Spring Boot and microservices architecture.
  </li>
  <li>
   Practical experience with OpenAI APIs and SDKs.
  </li>
  <li>
   Experience designing and implementing RAG architectures.
  </li>
  <li>
   Hands-on experience with Langfuse or similar LLM observability and monitoring platforms.
  </li>
  <li>
   Strong knowledge of PostgreSQL (PGSQL).
  </li>
  <li>
   Experience building applications using React Native.
  </li>
  <li>
   Experience integrating with Microsoft Graph API and Microsoft productivity ecosystem services.
  </li>
  <li>
   Experience integrating enterprise systems and ERP platforms.
  </li>
  <li>
   Strong understanding of RESTful APIs, authentication, security, and application scalability.
  </li>
  <li>
   Experience working within Agile delivery environments.
  </li>
  <li>
   Excellent problem-solving, communication, and stakeholder management skills.
  </li>
  <li>
   Preferred Qualifications
  </li>
  <li>
   Experience with Azure AI Services, Azure OpenAI, or cloud-native AI architectures.
  </li>
  <li>
   Experience with vector databases and semantic search technologies.
  </li>
  <li>
   Understanding of AI governance, security, and responsible AI principles.
  </li>
  <li>
   Experience deploying AI workloads in production environments.
  </li>
  <li>
   Knowledge of containerization technologies such as Docker and Kubernetes.
  </li>
  <li>
   Experience designing multi-agent or agentic AI workflows.
   <br/>
   <br/>
  </li>
 </ul>
 AI Engineer in Abu Dhabi, United Arab Emirates
 <br/>
 <br/>
</div>


AI Engineer (m/f/d)

<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

DICETEK LLC

<div><p><b>Role Overview</b></p><p>
The DevOps Engineer owns the AWA platform's deployment, infrastructure-as-code, CI/CD, and operational reliability. You ensure that every AWA component &mdash; from AKS namespaces and KEDA autoscalers to ADLS Gen2 lifecycle policies and Azure Firewall allowlists &mdash; is deployed consistently, tested automatically, and operated reliably within THE BANK's Azure UAE environment. You are the guardian of the AWA infrastructure: nothing is deployed manually; everything is reproducible from code.</p><p><b>
Key Responsibilities</b></p><ul><li><b>Infrastructure as code: </b>Own and maintain all AWA Azure infrastructure in Terraform/Bicep: AKS 5-namespace cluster, ADLS Gen2 accounts with WORM policies, Azure AI Foundry APIM configurations, Azure Firewall allowlists, private endpoints, Key Vault, Container Registry, ExpressRoute peering.</li><li><b>CI/CD pipeline ownership: </b>Design and maintain Azure DevOps pipelines for all AWA components: container image build ACR push Notary v2 signing AKS deployment; Terraform plan policy check apply; Agent Regression Testing gate on every PR.</li><li><b>AKS operations: </b>Manage the 5-namespace AKS cluster &mdash; namespace isolation, Network Policies, Pod Disruption Budgets, KEDA autoscaling rules based on Kafka consumer lag, OPA Gatekeeper admission policies, Workload Identity bindings for all agent pods.</li><li><b>Container governance: </b>Manage ACR, enforce Notary v2 image signing, configure vulnerability scanning, maintain the approved-image ConfigMap used by OPA Gatekeeper admission control.</li><li><b>Secret and certificate management: </b>Own Azure Key Vault configuration &mdash; access policies per managed identity, automatic TLS certificate rotation, secret rotation schedules, audit of all secret access events.</li><li><b>Monitoring and alerting: </b>Configure Azure Monitor alert rules and Log Analytics KQL queries for SLA breach, error rate spike, pod crash loop, token budget breach, and Kafka consumer lag; maintain PagerDuty escalation paths.</li><li><b>Security posture: </b>Work with the AI Security Engineer to implement Azure Policy assignments, Defender for Cloud recommendations, Firewall rule reviews, and Private Endpoint configurations; maintain the AWA network topology diagram.</li><li><b>Disaster recovery: </b>Maintain and test DR procedures: failover to UAE South replica for ADLS Gen2, Azure AI Foundry PTU PAYG DR endpoint routing, Orkes cluster recovery, RTO/RPO validation.</li></ul><p></p><p><b><br>
Required Skills and Experience</b></p><h3 class="jobSectionHeader"><b>
Technical &mdash; Essential</b></h3><ul><li>5+ years DevOps/Platform Engineering; 2+ years on Azure</li><li>Terraform or Bicep &mdash; production IaC, not just templates</li><li>Kubernetes / AKS: namespaces, RBAC, Network Policies, Helm charts, Kustomize</li><li>Azure DevOps or GitHub Actions &mdash; multi-stage pipelines, approvals, environments</li><li>Azure Monitor, Log Analytics, KQL query language</li><li>Container ecosystem: Docker, ACR, image signing, admission control</li><li>Azure networking: VNet, private endpoints, NSGs, Azure Firewall, ExpressRoute concepts</li><li>KEDA (Kubernetes Event-Driven Autoscaling) or equivalent autoscaling experience</li></ul><p></p><h3 class="jobSectionHeader"><b><br>
Technical &mdash; Advantageous</b></h3><ul><li>Azure Key Vault, managed identity, Workload Identity for Kubernetes</li><li>OPA Gatekeeper or Kyverno for Kubernetes policy enforcement</li><li>Kafka / Azure Event Hubs &mdash; consumer group management, lag monitoring</li><li>ADLS Gen2, WORM immutable storage, lifecycle management policies</li><li>Azure AI Foundry / APIM &mdash; token quotas, rate limiting, backend routing configuration</li><li>Security tooling: Defender for Cloud, Sentinel, Notary v2</li></ul><p><b>
Skills:</b> Terraform , Kubernetes , Azure DevOps</p></div>

DevOps Engineers (AIOps) - API and Kubernetes

<div><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. The role will build intelligent systems that integrate complex backend services and client-facing applications across web, mobile and enterprise platforms.</p><p>
The primary responsibility is 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. A commitment to collaborative problem solving, sophisticated design and product quality is essential.</p><p>
This role requires strong hands-on engineering experience, practical working knowledge of modern agent frameworks, and the ability to deliver secure, scalable, observable and governed AI solutions in cloud-native environments.</p><p></p><p><b><br>
QUALIFICATIONS &amp; EXPERIENCE:
</b></p><p><b>Minimum Qualification</b></p><ul><li>Bachelor&rsquo;s degree in Computer Science, Software Engineering, Information Technology, Data Science, Artificial Intelligence or a related discipline.</li><li>Relevant cloud, AI engineering, machine learning or architecture certifications are preferred.</li></ul><p></p><p><b><br>
Minimum Experience</b></p><ul><li>Senior professional with around 10 years of total software engineering, architecture, cloud or platform engineering experience.</li><li>Minimum 3+ years of relevant hands-on AI Engineering experience, including Generative AI and practical LLM-based application delivery.</li><li>Strong proficiency in Python, including NumPy, pandas, FastAPI and hands-on experience with PyTorch or TensorFlow.</li><li>Hands-on experience with LangChain and LangGraph; mandatory working experience with Microsoft Semantic Kernel and Microsoft AutoGen.</li><li>Experience implementing RAG using embeddings, vector databases, semantic search, retrieval optimization and model evaluation techniques.</li><li>Experience deploying and managing models using Amazon Bedrock, Azure OpenAI Service and Google Vertex AI.</li><li>Hands-on experience with microservices, containers, APIs, event-driven architecture, cloud-native services and evolutionary architecture practices.</li><li>Experience managing and deploying AI workloads on Kubernetes in cloud-native and/or hybrid environments.</li><li>Experience with CI/CD tools such as Jenkins or GitLab, DevOps toolchains, configuration management and cloud/on-prem deployment pipelines.</li><li>Experience setting up pipelines with static code analysis, requirement tagging in Jira, quality gates and release governance.</li><li>Experience operating monitoring tools for traditional infrastructure, cloud environments and AI-enabled business applications.</li><li>Strong hands-on problem-solving mindset with the ability to analyze trade-offs and deliver sustainable, secure and high-quality solutions.</li></ul><p></p><p><b><br>
Key Technical Skills</b></p><ul><li>Generative AI, Agentic AI, autonomous agents, multi-agent orchestration and workflow-based AI systems.</li><li>LLMs, embeddings, vector databases, RAG, semantic search, model evaluation, guardrails, observability and AI governance.</li><li>Semantic Kernel, AutoGen, LangChain, LangGraph and similar agent frameworks.</li><li>Python, FastAPI, PyTorch/TensorFlow, REST APIs, microservices, serverless functions and event-driven integration.</li><li>Azure, AWS, Kubernetes, containers, CI/CD, DevOps automation, monitoring and secure software delivery.</li></ul><p><b>
Behavioural / Leadership Skills</b></p><ul><li>Strong collaborative mindset for agile architecture and decentralized decision making.</li><li>Proactive, positive and growth-oriented leadership style with the ability to motivate engineers and foster craftsmanship.</li><li>Strong communication, stakeholder engagement and influencing skills across product, business, architecture and engineering teams.</li><li>Analytical, system-thinking and pragmatic problem-solving approach with commitment to product quality.</li></ul><p></p><p><b><br>
Skills:</b> Generative AI , Python , Kubernetes</p></div>

AI Engineer – Generative AI & Agentic AI

Brain Co.

<div>
 Our Mission
 <br/>
 <br/>
 Rebuild how the world works, to make institutions work better for the people they serve.
 <br/>
 <br/>
 <strong>
  About Brain Co.
  <br/>
  <br/>
 </strong>
 Brain Co. builds AI-native operating systems for large, regulated institutions. Each system is built for a specific industry, powered by agents that push real workflows forward. Underneath it all is Atlas, our proprietary platform that keeps customers in control, secure by design, and never locked into one model.
 <br/>
 <br/>
 Why Now
 <br/>
 <br/>
 Brain Co. is entering its next phase of production deployments on a national scale with an elite team built from Palantir, Google, Meta, and Nvidia, and a growing footprint across government, insurance, health, and financial services.
 <br/>
 <br/>
 Joining now means shaping both the company and a new category of applied AI. Every project here ships to production and is expected to create measurable customer value and impact.
 <br/>
 <br/>
 You'll work alongside exceptional peers on some of the hardest problems in applied AI. It&rsquo;s the kind of work you'll still be proud of in ten years from now.
 <br/>
 <br/>
 <strong>
  Machine Learning Engineer, Applied AI
  <br/>
  <br/>
 </strong>
 <strong>
  About The Role
  <br/>
  <br/>
 </strong>
 So much of the work society depends on is still slower and harder than it should be. Permits take months. Claims sit unresolved. And AI hasn't changed that &mdash; because the bottleneck isn't the models. It's the institutional context AI needs to do the work: rules, history, relationships, and judgment scattered across people, documents, and legacy systems.
 <br/>
 <br/>
 BrainCo exists to fix that. We build agent-native operating systems for the institutions society depends on, and our products are the first of their kind in the world &mdash; we were the first, anywhere, to fully automate construction permitting, and we're now doing the same across insurance and other industries. There is no playbook here, because no one has built this before.
 <br/>
 <br/>
 As a Machine Learning Engineer on Applied AI, your work begins where the demo ends: getting a model to look impressive is the easy part; making it a production decision system an institution stakes its process on is the job. The problems come in every shape &mdash; custom vision model pipelines that check blueprints against building codes at 95%+ accuracy, agents that untangle policy stacks to reveal coverage gaps, systems that predict from clinical records whether a patient is on their care path &mdash; and you'll own them end-to-end, from ambiguous customer problem to the eval that catches a whole class of errors.
 <br/>
 <br/>
 This is frontier ML applied where it's hardest and matters most. The problems are underspecified, the documents are brutal, the accuracy bar is institutional-grade &mdash; and the feedback loops are real, because our systems move real workflows forward every day.
 <br/>
 <br/>
 <strong>
  Who We're Looking For
  <br/>
  <br/>
 </strong>
 You understand how machine learning actually works &mdash; not just the tooling, but the philosophy underneath: what a loss function really optimizes, how generalization breaks under distribution shift, why evaluation is where systems quietly go wrong. And you live at the bleeding edge of modern AI, with hard-won instincts for squeezing the most out of LLMs and agentic systems &mdash; prompting, fine-tuning, tool use, and reasoning. That combination is the job: you know when a fine-tuned segmentation model beats a VLM, when a rule engine beats both, and how to compose all three into a system more accurate than any single model. You treat frontier models as components to be measured, pushed, and engineered &mdash; never as magic.
 <br/>
 <br/>
 Most of all, you're energized by building things that have never existed, and comfortable when the problem, the data, and the definition of success all have to be invented at once.
 <br/>
 <br/>
 <strong>
  The Problems You'll Work On
  <br/>
  <br/>
 </strong>
 <strong>
  Composite AI systems and credit assignment.
 </strong>
 Our most demanding systems chain vision transformers, segmentation models, VLM reasoning, and rule engines. When the pipeline is wrong, which component failed? One of the most interesting open problems in applied ML.
 <br/>
 <br/>
 <strong>
  Document understanding beyond the frontier.
 </strong>
 Blueprints, site plans, policy stacks, contracts, clinical records &mdash; dense, multimodal documents that break off-the-shelf models. You'll build models that actually read them.
 <br/>
 <br/>
 <strong>
  Agents that learn from real work.
 </strong>
 Our deployments generate verified, ground-truth outcomes on every decision &mdash; reward signals most labs can only simulate. You'll help design the data, evals, and training loops to build and fine-tune agents on them.
 <br/>
 <br/>
 <strong>
  Evaluation as a product discipline.
 </strong>
 When a regulator has to trust your system, evals are the product. You'll build eval suites and failure-mode taxonomies rigorous enough to earn institutional sign-off.
 <br/>
 <br/>
 <strong>
  Institutional Intelligence that compounds.
 </strong>
 Every verified correction improves the system twice: the corrected fact percolates to every application, and the system that builds the intelligence learns to build it better. You'll work on both loops.
 <br/>
 <br/>
 <strong>
  In This Role, You Will
  <br/>
  <br/>
 </strong>
 <strong>
  Turn ambiguity into shipped systems
 </strong>
 &mdash; from no problem statement, no labeled data, and no agreed definition of success, to well-posed ML problems and production deployments.
 <br/>
 <br/>
 <strong>
  Own AI systems end-to-end.
 </strong>
 There is no handoff: the person who trains the model owns its behavior in production.
 <br/>
 <br/>
 <strong>
  Work at the research frontier with production stakes
 </strong>
 , applying LLMs, RL fine-tuning, and agentic systems where the output is a decision an institution acts on.
 <br/>
 <br/>
 <strong>
  Work directly with the institutions we serve
 </strong>
 &mdash; permit reviewers, underwriters, compliance officers &mdash; to understand how decisions actually get made and ensure your systems change how the work gets done.
 <br/>
 <br/>
 <strong>
  Engineer for production reality
 </strong>
 , navigating accuracy, latency, cost, and reliability in environments far messier than any benchmark.
 <br/>
 <br/>
 <strong>
  Raise the bar across the company
 </strong>
 through design reviews, our internal paper club, and the shared playbook for AI systems institutions can trust.
</div>


Machine Learning Engineer, Applied AI - Deployed

Sundus

<div>
 <strong>
  Job Title
 </strong>
 : AI Engineer (ML, LLM, RAG &amp; Agentic AI)
 <br/>
 <br/>
 <strong>
  Job Code
 </strong>
 : JPC 6310
 <br/>
 <br/>
 <strong>
  Experience:
 </strong>
 2+ years of experience in AI/ML development.
 <br/>
 <br/>
 <strong>
  Education:
 </strong>
 Bachelor s degree in Computer Science, AI, Data Science, or related field.
 <br/>
 <br/>
 <strong>
  Language Skills
 </strong>
 : English
 <br/>
 <br/>
 <strong>
  Role Purpose
  <br/>
  <br/>
 </strong>
 The AI Engineer is responsible for developing, testing, and deploying AI/ML and
 <br/>
 <br/>
 Generative AI solutions, including LLM-based applications, RAG pipelines, and
 <br/>
 <br/>
 Agentic workflows. This role also includes the ability to deploy and run LLMs on
 <br/>
 <br/>
 local/on-premise servers, supporting secure and efficient AI solution delivery in
 <br/>
 <br/>
 enterprise environments.
 <br/>
 <br/>
 <strong>
  Required Skills &amp; Qualifications
  <br/>
  <br/>
 </strong>
 Bachelor s degree in Computer Science, AI, Data Science, or related field.
 <br/>
 <br/>
 2+ years of experience in AI/ML development.
 <br/>
 <br/>
 Strong programming skills in Python.
 <br/>
 <br/>
 <strong>
  Hands-on Experience With
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Machine learning frameworks (Scikit-learn, PyTorch, or TensorFlow)
  </li>
  <li>
   Basic NLP concepts
   <br/>
   <br/>
  </li>
 </ul>
 Understanding of ML model lifecycle (training, evaluation, deployment).
 <br/>
 <br/>
 Familiarity with REST APIs and integration concepts.
 <br/>
 <br/>
 Experience with Git/version control.
</div>


AI Engineer

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