Urwah Admin

Salt

<div>
 
 
 Job Title &ndash; Ai Engineer
 
 
 
 
 Salary &ndash; AED 25,000 - 28,500 Per Month
 
 
 
 
 Contract Length &ndash; 12 Months Contract
 
 
 
 
 Location &ndash; Dubai
 
 
 
 
 Start Date - ASAP
 
 
 
 
 
 
 My client is a global data, AI, digital transformation, and consulting company that helps organisations use data and technology to improve business performance, customer experience, and decision-making.
 
 
 
 
 
 
 Role Overview
 
 : The AI Engineer is the primary builder within the AWA AICoE. You will design, build, and test the LangGraph agent graphs, LLM extraction pipelines, prompt management workflows, and MCP tool integrations that power AWA's use cases &mdash; starting with Intelligent Document Processing (IDP) for cheque clearing and expanding across the bank's operations. You will work directly with the dual-layer orchestration stack (Orkes Conductor + LangGraph) and Azure AI Foundry to deliver production-grade agentic systems.
 
 
 
 
 
 
 Key Responsibilities
 
 
 <ul>
 <li>
 Agent graph development: Design and implement LangGraph agent graphs for Supervisor-Worker orchestration, self-reflection, actor-critic, and HITL interrupt patterns using TypedDict state schemas and conditional edge routing.
 </li>
 <li>
 LLM extraction pipeline: Build and maintain LLM-based entity extraction pipelines using Azure AI Foundry, implementing structured output enforcement, per-field confidence scoring, and Pydantic output validation against Use Case Manifest schemas.
 </li>
 <li>
 Prompt engineering and governance: Author, version, and govern prompts through the AWA Prompt Management System (PMS) using semantic versioning; run prompt sensitivity and correctness testing using the AWA IDP Test Strategy.
 </li>
 <li>
 MCP tool integration: Develop and maintain the five core MCP tool servers (query_structured, retrieve_precedents, query_documents, checkpoint_state, observe) and extend them for new use case requirements.
 </li>
 <li>
 Model evaluation and testing: Execute Band 1 and Band 2 testing (Model Testing, Prompt Testing, Agent Testing, AI Security Testing, Adversarial Testing) per the AWA IDP Test Strategy; maintain the Golden Dataset and Regression Dataset.
 </li>
 <li>
 HITL framework: Implement HITL interrupt() calls at mandatory and dynamic review points in LangGraph graphs; configure Orkes Wait tasks; validate HITL state persistence and checkpoint-based resumption.
 </li>
 <li>
 AI guardrail integration: Integrate Azure AI Content Safety, PII detection and tokenisation, prompt injection shielding, and output sanitisation into agent pipelines at the AWA AI Gateway layer.
 </li>
 <li>
 Cross-field validation rules: Implement business validation rules (amount-words-vs-figures, date validity, MICR format, referential integrity) within LangGraph validation agent nodes.
 </li>
 </ul>
 
 
 
 
 
 
 
 
 Required Skills and Experience
 
 
 
 
 Technical &mdash; Essential
 
 
 <ul>
 <li>
 5+ years Python development &mdash; production-grade, not just scripting
 </li>
 <li>
 Hands-on experience with LangGraph, LangChain, or equivalent agentic AI frameworks
 </li>
 <li>
 Practical LLM prompt engineering: structured outputs, few-shot design, output schema enforcement
 </li>
 <li>
 Familiarity with Azure AI Foundry / OpenAI API or equivalent LLM API (Anthropic, Cohere)
 </li>
 <li>
 Understanding of agent design patterns: ReAct, self-reflection, tool use, HITL
 </li>
 <li>
 REST API development and consumption; JSON schema design
 </li>
 <li>
 Git version control; CI/CD pipeline awareness
 </li>
 </ul>
 
 
 
 
 
 Technical &mdash; Advantageous
 
 
 <ul>
 <li>
 Experience with Orkes Conductor, Apache Airflow, or Temporal for workflow orchestration
 </li>
 <li>
 Azure AI Document Intelligence or equivalent OCR platform experience
 </li>
 <li>
 Computer vision model integration (object detection APIs)
 </li>
 <li>
 Knowledge of RAG architectures: chunking, vector indexing, Azure AI Search
 </li>
 <li>
 Azure platform familiarity: AKS, ADLS Gen2, Azure ML, Azure Monitor
 </li>
 <li>
 Pydantic, FastAPI, TypeScript/Node.js
 </li>
 </ul>
 
 
 
 
 
 Domain and Soft Skills
 
 
 <ul>
 <li>
 Genuine intellectual curiosity about AI systems and their failure modes
 </li>
 <li>
 Ability to explain AI behaviour to non-technical Ops and business stakeholders
 </li>
 <li>
 Structured problem-solving and debugging approach for non-deterministic AI systems
 </li>
 <li>
 Banking domain interest &mdash; willingness to deeply understand the business processes being automated
 </li>
 </ul>
 
 
 
 
 Thanks,
 
 
 Ollie
 
 
 
 
</div>

Ai Engineer

<div>
 
 🤖
 
 Site Reliability Engineer (SRE) &ndash; AI Platforms
 
 
 
 
 📍 Abu Dhabi
 
 
 
 
 ⏳ 6-month initial contract - with extensions
 
 
 
 
 
 
 
 Help Build AI Platforms That Never Sleep
 
 
 
 We're partnering with one of the UAE's leading financial institutions to hire
 
 2 Site Reliability Engineers (SREs)
 
 to support the delivery of a next-generation
 
 Agentic AI platform
 
 powering the future of digital banking. This is far more than a traditional SRE role. You'll sit at the intersection of
 
 Site Reliability Engineering, Test Automation, Cloud Infrastructure and AI Quality Assurance
 
 , ensuring enterprise AI services are resilient, secure, scalable and production-ready. You'll build the automated frameworks that validate AI outputs while maintaining the reliability and performance of cloud-native applications running across Azure and AWS.
 
 
 
 
 
 As a
 
 Site Reliability Engineer
 
 , you'll take ownership of
 
 Production Readiness
 
 across enterprise AI applications and cloud infrastructure. You'll develop automated testing frameworks, define reliability standards, implement observability, and ensure AI-powered services consistently meet the performance, security and compliance standards expected within a highly regulated banking environment. Working alongside AI Engineers, DevOps Engineers, Software Developers and Cloud Architects, you'll play a critical role in ensuring the platform performs reliably at scale.
 
 
 
 
 
 
 What You'll Be Doing
 
 
 
 Site Reliability Engineering
 
 <ul>
 <li>
 Design and implement resilient, highly available cloud platforms.
 </li>
 <li>
 Build automated recovery and self-healing capabilities.
 </li>
 <li>
 Conduct chaos engineering exercises to validate system resilience.
 </li>
 <li>
 Perform load and stress testing to ensure applications can support banking-scale workloads.
 </li>
 </ul>
 
 Automated Testing &amp; Quality Engineering
 
 <ul>
 <li>
 Develop automated regression testing frameworks using Python.
 </li>
 <li>
 Integrate automated testing into CI/CD pipelines.
 </li>
 <li>
 Validate both application functionality and infrastructure deployments.
 </li>
 <li>
 Ensure every release meets enterprise quality standards before production.
 </li>
 </ul>
 
 AI Quality &amp; Validation
 
 <ul>
 <li>
 Build automated frameworks to evaluate AI model responses.
 </li>
 <li>
 Validate prompt behaviour and AI-generated outputs.
 </li>
 <li>
 Measure AI performance using established evaluation techniques and custom benchmarks.
 </li>
 <li>
 Help improve the reliability and consistency of enterprise AI services.
 </li>
 </ul>
 
 Observability &amp; Monitoring
 
 <ul>
 <li>
 Define Service Level Indicators (SLIs) and Service Level Objectives (SLOs).
 </li>
 <li>
 Develop dashboards and proactive monitoring solutions.
 </li>
 <li>
 Configure intelligent alerting to identify issues before they impact customers.
 </li>
 <li>
 Analyse platform health, reliability and performance trends.
 </li>
 </ul>
 
 Security &amp; Compliance
 
 <ul>
 <li>
 Automate security testing and compliance validation.
 </li>
 <li>
 Ensure AI services comply with banking security, privacy and data residency requirements.
 </li>
 <li>
 Support continuous platform governance and operational excellence.
 </li>
 </ul>
 
 Cloud Optimisation
 
 <ul>
 <li>
 Identify inefficient infrastructure and unnecessary cloud spend.
 </li>
 <li>
 Improve platform performance while optimising operational costs.
 </li>
 <li>
 Support FinOps initiatives across AI workloads.
 </li>
 </ul>
 
 
 
 
 
 What We're Looking For
 
 
 
 Essential Skills &amp; Experience
 
 <ul>
 <li>
 Strong commercial experience in Site Reliability Engineering, Platform Engineering or Test Automation
 </li>
 <li>
 Expert Python development skills
 </li>
 <li>
 Experience with automation frameworks including
 
 PyTest, Selenium or Robot Framework
 
 </li>
 <li>
 Hands-on experience with Azure and/or AWS cloud platforms
 </li>
 <li>
 Experience building automated CI/CD testing pipelines
 </li>
 <li>
 Strong understanding of cloud networking, scalability and reliability
 </li>
 <li>
 Experience with infrastructure validation and Infrastructure-as-Code concepts
 </li>
 <li>
 Knowledge of monitoring and observability tools including:
 </li>
 <li>
 Azure Monitor
 </li>
 <li>
 AWS CloudWatch
 </li>
 <li>
 Grafana
 </li>
 <li>
 Prometheus
 </li>
 <li>
 Experience with performance testing tools such as
 
 K6 or JMeter
 
 </li>
 </ul>
 
 
 
 
 
 AI Experience
 
 
 
 We're looking for engineers who have worked on
 
 AI, Generative AI, Conversational AI or Agentic AI
 
 projects and understand how to evaluate AI system performance.
 
 
 Experience with any of the following would be highly beneficial:
 
 <ul>
 <li>
 Prompt Engineering
 </li>
 <li>
 LLM evaluation
 </li>
 <li>
 Retrieval-Augmented Generation (RAG)
 </li>
 <li>
 AI benchmarking
 </li>
 <li>
 ROUGE/BLEU scoring
 </li>
 <li>
 AI quality evaluation frameworks such as
 
 DeepEval, Ragas or LangSmith
 
 </li>
 </ul>
 
 
 Nice to Have
 
 
 <ul>
 <li>
 Bash scripting
 </li>
 <li>
 GitHub Actions
 </li>
 <li>
 Terraform (validation and governance)
 </li>
 <li>
 Serverless cloud architectures
 </li>
 <li>
 FinOps or cloud cost optimisation
 </li>
 <li>
 Experience working within highly regulated industries such as banking or financial services
 </li>
 </ul>
 
 
 
 
 
 Why Join?
 
 
 
 This is a chance to work on one of the most ambitious enterprise AI programmes in the Middle East.
 
</div>

Full Stack Engineer

<div>
 
 
 Job Title &ndash; Kafka Engineer
 
 
 
 
 Salary &ndash; AED 20,00 &ndash; AED 25,400 Per Month
 
 
 
 
 Contract Length &ndash; 12 Months Contract
 
 
 
 
 Location &ndash; Dubai
 
 
 
 
 Start Date - ASAP
 
 
 
 
 
 
 My client is a global data, AI, digital transformation, and consulting company that helps organisations use data and technology to improve business performance, customer experience, and decision-making.
 
 
 
 
 
 
 Job Summary:
 
 
 
 
 
 
 The client is seeking a highly skilled Confluent Kafka Engineer to join the team. The ideal candidate will be responsible for the development, User Acceptance Testing (UAT), and production support of our Confluent Kafka-based systems. This role requires deep expertise in Kafka architecture, including Confluent Control Center, KSQLDB, Kafka Streams, and Kafka Connect. The engineer will collaborate closely with cross-functional teams to ensure the smooth operation of the data streaming services and provide support across various environments.
 
 
 
 
 
 
 Key Responsibilities:
 
 
 
 
 
 
 Development:
 
 
 
 
 <ul>
 <li>
 Design, develop, and implement Kafka-based solutions to meet business requirements, utilizing Confluent Control Center, KSQLDB, Kafka Streams, and Kafka Connect.
 </li>
 <li>
 Write and maintain high-quality code for Kafka producers, consumers, and stream processing applications.
 </li>
 <li>
 Develop and manage Kafka connectors for seamless integration with external systems, ensuring data consistency and reliability.
 </li>
 <li>
 Utilize Kafka Streams for real-time processing of streaming data, transforming and enriching data as it flows through the pipeline.
 </li>
 <li>
 Employ KSQLDB for stream processing tasks, including real-time analytics and transformations.
 </li>
 <li>
 Collaborate with data engineers, software developers, and DevOps teams to integrate Kafka solutions with existing systems.
 </li>
 <li>
 Ensure all Kafka-based solutions are scalable, secure, and optimized for performance.
 </li>
 </ul>
 
 
 
 
 User Acceptance Testing (UAT):
 
 <ul>
 <li>
 Develop and execute UAT plans to validate Kafka solutions, including components such as Kafka Streams, KSQLDB, and Kafka Connect, before deployment to production.
 </li>
 <li>
 Work closely with QA teams to identify and resolve defects during the UAT phase.
 </li>
 <li>
 Ensure all UAT activities comply with the organization&rsquo;s standards and best practices.
 </li>
 <li>
 Provide detailed reports on UAT outcomes and work with developers to implement necessary fixes.
 </li>
 </ul>
 
 
 
 
 Production Support:
 
 <ul>
 <li>
 Monitor and maintain Kafka clusters, including components like Confluent Control Center, to ensure high availability and reliability of data streaming services.
 </li>
 <li>
 Troubleshoot and resolve issues related to Kafka performance, latency, and data integrity, including issues specific to Kafka Streams, KSQLDB, and Kafka Connect.
 </li>
 <li>
 Perform routine maintenance tasks such as patching, upgrades, and backups for Kafka clusters and associated components.
 </li>
 <li>
 Implement monitoring solutions to proactively identify and mitigate potential production issues, leveraging Confluent Control Center for comprehensive cluster visibility.
 </li>
 <li>
 Provide 24/7 support for production systems, including participation in on-call rotation
 </li>
 </ul>
 
 
 
 
 
 
 
 
 Experience:
 
 
 
 
 
 <ul>
 <li>
 3+ years of hands-on experience with Apache Kafka and Confluent Kafka in a production environment, including experience with Confluent Control Center, KSQLDB, Kafka Streams, and Kafka Connect.
 </li>
 <li>
 Proven experience in Kafka development, including producer and consumer API, stream processing, and connector development.
 </li>
 <li>
 Experience with Kafka cluster management, including setup, configuration, monitoring, and troubleshooting.
 </li>
 <li>
 Familiarity with distributed systems, microservices architecture, and event-driven design patterns.
 </li>
 <li>
 Experience with cloud platforms (e.g., AWS, Azure) and containerization (Kubernetes) is a plus.
 </li>
 </ul>
 
 
 
 
 
 Technical Skills:
 
 
 <ul>
 <li>
 Proficiency in programming languages such as Java, Python, or Scala.
 </li>
 <li>
 Strong knowledge of Kafka internals, including brokers, zookeepers, topics, partitions, and offsets.
 </li>
 <li>
 Experience with monitoring tools (e.g., Prometheus, Grafana) and logging frameworks (e.g., Log4j, ELK Stack).
 </li>
 <li>
 Proficiency in using Confluent Control Center for monitoring, managing, and optimizing Kafka clusters.
 </li>
 <li>
 Expertise in Kafka Streams for building scalable, fault-tolerant stream processing applications.
 </li>
 <li>
 Experience with KSQLDB for real-time processing and analytics on Kafka topics.
 </li>
 <li>
 Strong understanding of Kafka Connect for integrating Kafka with external data sources and sinks.
 </li>
 <li>
 Understanding of networking, security, and compliance aspects related to Kafka.
 </li>
 <li>
 Familiarity with CI/CD pipelines and automation tools (e.g., Jenkins, GitLab CI).
 </li>
 </ul>
 
 
 
 
 
 Soft Skills:
 
 
 <ul>
 <li>
 Strong analytical and problem-solving abilities.
 </li>
 <li>
 Excellent communication and teamwork skills.
 </li>
 <li>
 Ability to work independently and manage multiple tasks effectively.
 </li>
 <li>
 A proactive approach to learning new technologies and improving existing processes.
 </li>
 </ul>
 
 
 
 
 Regards,
 
 
 Ollie
 
</div>

Kafka Engineer

<div>
 
 
 Staff Full-Stack Engineer (Java + React)
 
 
 
 
 Building the Next Generation of Agentic AI Platforms
 
 
 
 
 
 
 My client is building the next generation of agentic platforms and AI-powered enterprise systems.
 
 
 As a
 
 Staff Full-Stack Engineer
 
 , you won&rsquo;t just ship features- you&rsquo;ll architect the core systems that power intelligent workflows at scale. You&rsquo;ll design both the
 
 brains and nervous system
 
 of the platform: robust Java backend services paired with high-quality React.js interfaces.
 
 
 
 
 
 
 What You&rsquo;ll Do
 
 
 <ul>
 <li>
 Design, build, and scale end-to-end full-stack systems
 </li>
 <li>
 Architect backend services in
 
 Java (Spring Boot / microservices)
 
 </li>
 <li>
 REST APIs
 </li>
 <li>
 Asynchronous workflows
 </li>
 <li>
 Distributed state management
 </li>
 <li>
 Build modern frontend applications using
 
 React.js + TypeScript
 
 </li>
 <li>
 Integrate LLM-based agents, RAG pipelines, vector stores, and orchestration workflows
 </li>
 <li>
 Define and own API contracts for frontend, third-party, and enterprise integrations
 </li>
 <li>
 Ensure system performance, reliability, observability, and security
 </li>
 <li>
 Champion clean architecture, testing strategies, CI/CD, and documentation
 </li>
 <li>
 Influence platform-level architecture decisions
 </li>
 <li>
 Mentor engineers and elevate engineering standards across the team
 </li>
 </ul>
 
 
 
 
 
 What You Bring
 
 
 <ul>
 <li>
 9+ years of full-stack engineering experience
 </li>
 <li>
 Strong expertise in
 
 Java (Spring Boot / microservices)
 
 </li>
 <li>
 Deep experience building applications with
 
 React.js
 
 </li>
 <li>
 Experience with REST APIs, PostgreSQL, Redis, Kafka (or similar event systems)
 </li>
 <li>
 Strong frontend architecture and performance optimization skills
 </li>
 <li>
 Hands-on experience with GCP, Docker, and CI/CD pipelines
 </li>
 <li>
 Comfort operating in 0-to-1 and fast-scaling startup environments
 </li>
 <li>
 Strong communication skills and cross-functional collaboration ability
 </li>
 <li>
 Ownership mindset with a passion for building high-quality systems
 </li>
 </ul>
 
 
 
 
 *** Only successful candidates will be contacted ****
 
</div>

Staff Full Stack Engineer

<div>
 
 
 Marketing AI &amp; Analytics Lead
 
 
 
 
 Dubai (Onsite)
 
 
 
 
 
 
 My client, Dubai-based
 
 PropTech
 
 company, is seeking a
 
 Marketing AI &amp; Analytics Lead
 
 to lead marketing analytics, attribution, reporting, and AI-driven transformation across the business. The role will be responsible for building a single source of truth for marketing data, improving campaign measurement, automating reporting, and delivering actionable insights that drive growth, reduce CAC, and improve ROAS.
 
 
 
 
 
 
 Key Requirements:
 
 
 <ul>
 <li>
 Experience in Marketing Analytics, Business Intelligence, or Performance Marketing Analytics
 </li>
 <li>
 Strong understanding of attribution, tracking, reporting, and marketing measurement frameworks
 </li>
 <li>
 Hands-on experience with GA4, GTM, AppsFlyer/Adjust, Power BI, Looker, and CRM platforms
 </li>
 <li>
 Proven ability to build AI-powered tools, agents, automations, and workflow solutions
 </li>
 <li>
 Experience analysing CAC, LTV, ROAS, funnel performance, and campaign effectiveness
 </li>
 <li>
 Ability to automate reporting and deliver data-driven insights to stakeholders
 </li>
 <li>
 Strong stakeholder management skills with experience working across Marketing, Product, CRM, and Technology teams
 </li>
 </ul>
 
 
 
 
 This is an excellent opportunity for a data-driven, AI-native professional who can bridge marketing, analytics, and technology while driving AI adoption within a high-growth organisation.
 
 
 
 
 
 
 
 *** Only successful candidates will be contacted ***
 
 
 
</div>

Marketing AI & Analytics Lead

<div>
 
 
 Role: Cloudera Technical Architect
 
 
 
 
 Location: Jeddah
 
 
 
 
 Program:
 
 Enterprise Data Lakehouse Implementation (Expansion, Remediation &amp; Operationalization)
 
 
 
 Domain:
 
 Banking &amp; Financial Services (BFSI)
 
 
 
 Immediately available candidate preferred
 
 
 
 
 
 
 My client is a global leader in IT services, consulting, and business solutions.
 
 
 
 
 
 
 Role Summary
 
 
 
 My client is seeking an experienced Cloudera Technical Architect to lead the design, optimization, and governance of a large-scale Enterprise Data Lakehouse platform. The role will focus on ensuring platform scalability, security, performance, and operational excellence while supporting remediation of existing implementations and future expansion initiatives.
 
 
 
 
 
 
 Key Responsibilities
 
 
 <ul>
 <li>
 Design and govern Cloudera CDP architecture aligned with enterprise Data Lakehouse standards.
 </li>
 <li>
 Define architecture patterns for batch, streaming, and analytics workloads.
 </li>
 <li>
 Lead performance optimization across Spark, Hive, Impala, and YARN environments.
 </li>
 <li>
 Architect and support real-time data ingestion using Kafka and NiFi.
 </li>
 <li>
 Implement and enforce security controls using Ranger, Kerberos, encryption, and access governance.
 </li>
 <li>
 Review existing Cloudera implementations, identify gaps, and drive remediation initiatives.
 </li>
 <li>
 Define monitoring, high availability, disaster recovery, and operational best practices.
 </li>
 <li>
 Collaborate with architects, engineering teams, and business stakeholders to ensure successful platform delivery.
 </li>
 </ul>
 
 
 
 
 
 Required Skills
 
 
 <ul>
 <li>
 Strong expertise in Cloudera Data Platform (CDP)
 </li>
 <li>
 Hands-on experience with:
 </li>
 <li>
 HDFS, Spark, Hive, Impala
 </li>
 <li>
 Kafka, NiFi
 </li>
 <li>
 Ozone, HBase, Kudu
 </li>
 <li>
 Iceberg, Hive Metastore
 </li>
 <li>
 Ranger, Knox, Kerberos
 </li>
 <li>
 Cloudera Manager and Cloudera AI
 </li>
 <li>
 Strong knowledge of cluster sizing, capacity planning, workload management, and performance tuning.
 </li>
 <li>
 Experience implementing secure, highly available, enterprise-scale data platforms.
 </li>
 </ul>
 
 
 
 
 
 Experience
 
 
 <ul>
 <li>
 10&ndash;15+ years of experience in Big Data and Data Platform technologies.
 </li>
 <li>
 Proven experience architecting and optimizing large-scale Cloudera environments.
 </li>
 <li>
 
 
 Banking/BFSI or other regulated industry experience preferred.
 
 
 </li>
 <li>
 Experience working with onsite and offshore delivery teams.
 </li>
 </ul>
 
 
 
 
 *** Only successful candidates will be contacted ***
 
</div>

Cloudera Technical Architect

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