technical support agent

Tata Consultancy Services

<div>
 
 
 Job Title
 
 &ndash; AI Platform Engineer
 
 
 
 Company
 
 &ndash; TCS (MEA)
 
 
 
 Location
 
 &ndash; Dubai
 
 
 
 Job type
 
 &ndash; Full time
 
 
 
 
 
 
 About Us:
 
 
 
 Tata Consultancy Services (TCS) is an IT services, consulting and business solutions organization that has been partnering with many of the world&rsquo;s largest businesses in their transformation journeys for over 50 years. TCS offers a consulting-led, cognitive powered, integrated portfolio of business, technology and engineering services and solutions. This is delivered through its unique Location Independent Agile&trade; delivery model, recognized as a benchmark of excellence in software development.
 
 
 A part of the Tata group, India's largest multinational business group, TCS has over 616,171 of the world&rsquo;s best-trained consultants with 157 nationalities in 53 countries. For more information, visit www.tcs.com and follow TCS news at @TCS_News.
 
 
 
 
 
 
 
 Job Description:
 
 
 
 
 
 
 
 
 Key Accountabilities:
 
 
 
 
 AI Platform Operations
 
 
 
 Provide day-to-day monitoring and operational support of Gernas, its dependent AI applications, deployed AI agents, agent runtime environments, AI gateways, and supporting Azure and AWS cloud services. Ensure continuous availability and consistent service performance across production environments, proactively identifying degradation signals before they become user-visible incidents. Maintain operational dashboards covering platform health, agent execution, LLM consumption, gateway throughput, and integration endpoints, and ensure that ownership of every production component is unambiguous and documented.
 
 
 
 Incident and Problem Management
 
 
 
 Lead L2 and L3 incident response across the Gernas platform and its surrounding AI products. Conduct structured root-cause analysis, drive service restoration within agreed P1&ndash;P4 service-level commitments, and coordinate major incidents involving multiple engineering, cloud, security, and vendor stakeholders. Maintain problem records for recurring or systemic issues, ensure that post-incident reviews are completed with clear corrective and preventive actions, and own the closure of those actions through to engineering remediation. Act as the bridge between the L1 AI Operations Centre and L3 engineering teams during complex incidents.
 
 
 
 AI Agent and Workflow Support
 
 
 
 Troubleshoot the behaviour of single-agent and multi-agent workflows, including agent orchestration, tool invocation, skill execution, prompt construction, memory and context handling, planning loops, and inter-agent coordination. Diagnose failures across Microsoft Agent Framework and LangGraph execution, including state-graph traversal, conditional routing, tool-call mismatches, retries, and human-in-the-loop checkpoints. Investigate integrations with enterprise systems, MCP-exposed tools, and downstream APIs, and work with engineering teams to harden agent designs where production data reveals weaknesses.
 
 
 
 LLM and Model Service Support
 
 
 
 Support the operational stability of the model-serving layer across Azure OpenAI PTU deployments, AWS Bedrock, and Core42 Compass. Monitor and troubleshoot PTU capacity utilisation, token consumption patterns, quota and throttling behaviour, latency profiles, content filtering outcomes, and model endpoint availability. Coordinate with the AI Hub team on routing decisions between frontier and in-region models, assess cost and capacity headroom, and manage capacity escalations and vendor tickets where service degradation originates upstream.
 
 
 
 AKS and Runtime Support
 
 
 
 Provide hands-on operational support of the Azure Kubernetes Service estate hosting Gernas agent runtimes and supporting services. Troubleshoot pod, deployment, service, ingress, namespace, secret, and configuration issues; investigate autoscaling behaviour and resource utilisation anomalies; and analyse container logs, network connectivity, and runtime failures. Work closely with the Cloud Platform team on cluster-level concerns, image security, node health, and platform upgrades, ensuring AKS-hosted workloads remain stable through patching and lifecycle events.
 
 
 
 API, AI Gateway and MCP Support
 
 
 
 Operate and troubleshoot the Azure API Management layer that serves as both the enterprise API gateway and the MCP Gateway for Gernas. Diagnose issues with API policies, authentication, authorisation, routing, rate limiting, quotas, caching, and backend connectivity. Provide deep support for MCP servers, MCP tools, and the MCP Gateway pattern &mdash; including tool discovery, schema validation, protocol-level failures, and the coordination between MCP clients hosted in agents and the underlying tool endpoints. Ensure that the gateway remains a secure, observable, and policy-compliant control plane for all AI traffic.
 
 
 
 Observability and Performance Management
 
 
 
 Use Comet Opik as the primary observability surface for agent and LLM execution, working with traces, prompts, agent execution paths, latency breakdowns, token usage, errors, and model quality indicators. Build and maintain operational dashboards, alert rules, and correlation views that combine Opik telemetry with Azure Monitor, Application Insights, Log Analytics, and CloudWatch data. Lead performance optimisation initiatives where trace evidence shows hotspots in prompts, tools, retrieval steps, or model selection, and ensure that observability coverage keeps pace with platform evolution.
 
 
 
 Voice AI Support
 
 
 
 Support the production operation of ElevenLabs-based voice AI capabilities, including speech generation, voice-agent connectivity, real-time audio session handling, and API consumption patterns. Investigate latency, audio quality, dropped sessions, and integration failures across the voice channel and its dependent platforms, and coordinate with ElevenLabs and integration partners on upstream issues.
 
 
 
 Release and Change Management
 
 
 
 Validate releases prior to and immediately following deployment, exercising production verification scripts, smoke tests, and rollback procedures. Maintain release readiness through clear configuration management, environment parity checks, and pre-deployment risk reviews. Operate within FAB's change-management framework, ensuring that all production changes including patches, upgrades, configuration adjustments, and model or prompt rotations pass through the appropriate change controls and post-implementation review.
 
 
 
 Security, Risk and Compliance
 
 
 
 Uphold the security and compliance posture of Gernas and dependent AI products. Manage identity and access controls, secrets, certificates, and managed identities across the platform; coordinate vulnerability remediation and patching cycles; and maintain audit evidence for internal audit, supervisory reviews, and external assurance. Operate responsible-AI controls including content filtering, PII and PCI detection, data egress controls, and model-access governance and ensure that secure integration patterns are followed across every API, MCP tool, and external dependency.
 
 
 
 Service Improvement and Automation
 
 
 
 Drive continuous reduction of manual support effort through automation of routine operational tasks, self-healing patterns, monitoring enhancements, and proactive remediation. Maintain a current and high-quality library of support playbooks, runbooks, knowledge articles, and standard operating procedures. Identify and lead service-improvement initiatives that lift platform reliability metrics, reduce incident volume, and shorten mean time to resolution.
 
 
 
 Stakeholder and Vendor Coordination
 
 
 
 Operate as a credible technical counterpart to business units, engineering teams, the Cloud Platform team, Cybersecurity, Architecture, AI Governance, and Service Management. Lead vendor engagement with Microsoft, AWS, Core42, ElevenLabs, and other technology partners on incidents, capacity reviews, roadmap items, and product issues, ensuring that vendor accountability is exercised and that escalations are progressed effectively.
 
 
 
 On-Call and Operational Readiness
 
 
 
 Participate in a 24&times;7 support model, including a structured on-call rotation, major incident leadership, disaster-recovery exercises, business-continuity testing, and production-readiness assessments for new agents, models, and integrations entering the platform. Treat operational readiness as a release gate rather than an afterthought, and ensure that nothing reaches production without explicit operational sign-off.
 
 
 
 
 
 
 TECHNICAL SKILLS: minimum 5-7 yrs of working experience mandatory
 
 
 
 
 Cloud AI Services
 
 
 
 
 
 
 
 Agentic AI Frameworks
 
 
 
 
 LLM Operations
 
 
 
 
 Kubernetes and Containers
 
 
 
 
 API and MCP Gateway
 
 
 
 
 Observability
 
 
 
 
 DevOps and Automation
 
 
 
 
 Voice AI
 
 
 
 
 Security and Governance
 
 
 
 
 Service Management
 
 
 
 
 
 
 
 KNOWLEDGE, SKILLS, &amp; EXPERIENCE
 
 
 
 
 Education
 
 
 
 Bachelor's degree in Computer Science, Artificial Intelligence, Information Technology, Engineering, or a closely related discipline. A relevant master's degree in AI, Machine Learning, or Cloud Computing will be considered an advantage.
 
 
 
 Professional Experience
 
 
 
 Approximately 5&ndash;7 years of overall IT experience, with significant time spent in cloud application support, platform engineering, DevOps, Site Reliability Engineering, production operations, or senior technical support roles. Of this, a minimum of 2&ndash;3 years of relevant experience supporting AI, machine learning, Generative AI, conversational AI, cloud-native platforms, or other data-intensive enterprise systems is required.
 
 
 
 Banking and Regulated Environment
 
 
 
 
 
 
 
 Technical Troubleshooting
 
 
 
 
 
 
 
 Operational Skills
 
 
 
 
 
 
 
 Communication and Leadership
 
 
 
 
 
 
 
 
 
 Thank you for your interest in applying for this position with TCS. We will review your application and will get back to you if we are considering your interest in this opportunity.
 
 
 
 
 
 
 Application Deadline: 30- July -2026
 
 
 
 
 
 
 
 Privacy Note:
 
 
 
 https://www.tcs.com/connect-with-tcs/privacy-policy
 
</div>

AI Platform Engineer

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

Boundless

<div>
 
 
 Company Overview
 
 
 
 Our client, a global and rapidly growing financial services group, operates across international markets through a technology-driven, regulated business model. The organisation is investing significantly in Artificial Intelligence, advanced analytics, intelligent automation, and next-generation software engineering capabilities to enhance its platforms, products, internal operations, and customer experience.
 
 
 
 
 
 
 Role Overview
 
 
 
 We are seeking an experienced AI Engineering Lead to establish and drive the organisation&rsquo;s LLM Engineering capability and accelerate AI-powered software development across multiple technology teams. This role will define the standards, architecture, tooling, governance, and development practices required to build secure, scalable, production-ready AI solutions. You will guide engineering teams in adopting modern AI-assisted development workflows, leveraging frontier LLMs, agentic architectures, enterprise RAG systems, and intelligent automation platforms.
 
 
 Working closely with Engineering Managers, Solution Architects, Product Owners, and senior leadership, you will ensure that AI becomes an embedded part of the software development lifecycle. The role combines hands-on technical leadership, architecture oversight, team enablement, and enterprise-wide AI transformation.
 
 
 
 
 
 
 Responsibilities
 
 
 
 
 AI Engineering Leadership &amp; Delivery
 
 
 <ul>
 <li>
 Lead the design, development, and implementation of enterprise-grade LLM applications, AI agents, intelligent automation solutions, and AI-native software products.
 </li>
 <li>
 Guide and mentor engineering teams on AI architecture, prompting strategy, agent design, model selection, code quality, and production deployment.
 </li>
 <li>
 Conduct AI solution design and technical architecture reviews before development begins.
 </li>
 <li>
 Establish engineering standards for secure, scalable, maintainable, and measurable AI implementations.
 </li>
 <li>
 Support teams in adopting AI-first development methodologies while maintaining strong software engineering quality, security, and delivery discipline.
 </li>
 </ul>
 
 
 AI-Assisted Software Engineering
 
 
 <ul>
 <li>
 Champion the use of AI coding and development environments including Claude Code, OpenAI Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, and emerging AI engineering tools.
 </li>
 <li>
 Define enterprise standards for AI-assisted software engineering, developer prompting, code generation, review workflows, and responsible use of AI development tools.
 </li>
 <li>
 Develop reusable prompt libraries, engineering playbooks, technical documentation, and best-practice frameworks.
 </li>
 <li>
 Introduce practical AI-assisted development workflows that improve engineering productivity, code quality, testing, documentation, and delivery speed.
 </li>
 <li>
 Establish methods to measure and improve developer productivity through AI-enabled engineering practices.
 </li>
 </ul>
 
 
 LLM Engineering &amp; Agentic Systems
 
 
 <ul>
 <li>
 Design and optimise production-grade LLM applications using leading models including Claude, OpenAI, Gemini, Mistral, Llama, DeepSeek, and other suitable providers.
 </li>
 <li>
 Build AI agents and multi-agent workflows using frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, Haystack, and LlamaIndex.
 </li>
 <li>
 Design and implement enterprise RAG systems using vector databases, knowledge graphs, structured data sources, and internal documentation repositories.
 </li>
 <li>
 Develop scalable MCP servers, tool integrations, API connectors, and secure AI access layers for enterprise systems.
 </li>
 <li>
 Evaluate new LLMs, frameworks, AI platforms, and engineering tools, making recommendations for adoption based on technical fit, security, cost, performance, and business value.
 </li>
 </ul>
 
 
 AI Platform, Architecture &amp; Governance
 
 
 <ul>
 <li>
 Define enterprise AI architecture standards, reference architectures, reusable components, and internal AI platform capabilities.
 </li>
 <li>
 Design secure integrations between LLMs, internal systems, APIs, databases, workflows, and customer-facing applications.
 </li>
 <li>
 Establish guardrails, AI evaluation frameworks, observability standards, model monitoring, prompt testing, and responsible AI practices.
 </li>
 <li>
 Lead technical decision-making around AI infrastructure, deployment patterns, cloud architecture, model hosting, orchestration, and scalability.
 </li>
 <li>
 Support the development of AI governance frameworks covering data privacy, security, access controls, explainability, quality assurance, and risk management.
 </li>
 </ul>
 
 
 Capability Development &amp; Adoption
 
 
 <ul>
 <li>
 Develop an AI Engineering Maturity Model to assess and improve AI adoption across engineering teams.
 </li>
 <li>
 Deliver internal workshops, technical training sessions, and practical enablement programmes for developers, architects, and engineering leaders.
 </li>
 <li>
 Train teams on LLM engineering workflows, AI coding assistants, agentic architectures, RAG implementation, and AI observability practices.
 </li>
 <li>
 Create and maintain internal documentation, coding standards, architecture guidelines, and engineering playbooks.
 </li>
 <li>
 Stay current with emerging AI technologies and continuously identify opportunities to strengthen the organisation&rsquo;s AI engineering capability.
 </li>
 </ul>
 
 
 
 
 
 Requirements
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Artificial Intelligence, Software Engineering, or a related technical discipline.
 </li>
 <li>
 At least 7 years of software engineering experience, including strong exposure to software architecture, enterprise applications, APIs, cloud platforms, and modern software delivery practices.
 </li>
 <li>
 At least 3 years of practical experience designing and deploying production LLM applications, AI agents, RAG systems, or intelligent automation solutions.
 </li>
 <li>
 Demonstrated experience leading AI engineering initiatives across multiple engineering teams or business units.
 </li>
 <li>
 Strong hands-on knowledge of LLM application development, prompt engineering, RAG, agentic architectures, MCP, model evaluation, orchestration frameworks, and AI observability.
 </li>
 <li>
 Experience with agent frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, Haystack, or similar technologies.
 </li>
 <li>
 Experience working with vector databases and knowledge systems such as Pinecone, Weaviate, pgvector, Qdrant, Chroma, or similar tools.
 </li>
 <li>
 Strong programming ability in Python and ideally TypeScript, with experience developing APIs using FastAPI or similar frameworks.
 </li>
 <li>
 Strong understanding of REST APIs, Docker, Kubernetes, Git, CI/CD, cloud deployment, and enterprise software integration.
 </li>
 <li>
 Experience with AWS, Microsoft Azure, Google Cloud Platform, or multi-cloud environments.
 </li>
 <li>
 Familiarity with AI observability and evaluation tools such as LangSmith, Helicone, MLflow, Weights &amp; Biases, OpenTelemetry, or similar platforms.
 </li>
 <li>
 Proven ability to mentor engineers, influence technical direction, establish standards, and communicate complex AI concepts to both technical and non-technical stakeholders.
 </li>
 </ul>
 
 
 
 
 
 Preferred Experience
 
 
 <ul>
 <li>
 Building or leading AI Engineering Centres of Excellence.
 </li>
 <li>
 Establishing enterprise AI governance, responsible AI, and AI risk-management frameworks.
 </li>
 <li>
 Driving AI transformation programmes across software engineering organisations.
 </li>
 <li>
 Defining AI coding standards, AI-SDLC frameworks, and AI-assisted development methodologies.
 </li>
 <li>
 Building enterprise LLM platforms, internal AI tools, reusable AI components, or shared AI services.
 </li>
 <li>
 Implementing enterprise RAG, multi-agent architectures, MCP servers, and AI workflow automation.
 </li>
 <li>
 Measuring productivity, quality, adoption, and delivery improvements resulting from AI-enabled software development.
 </li>
 <li>
 Experience within financial services, fintech, trading, payments, regulated technology environments, or large-scale enterprise platforms.
 </li>
 </ul>
 
 
 
 
 
 Key Technology Areas
 
 
 <ul>
 <li>
 Frontier LLMs: Claude, OpenAI GPT models, Gemini, Mistral, Llama, DeepSeek
 </li>
 <li>
 AI Development Tools: Claude Code, Cursor, GitHub Copilot, Gemini CLI, Windsurf, OpenAI Codex, Continue.dev
 </li>
 <li>
 Agent Frameworks: LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, Haystack
 </li>
 <li>
 Automation: n8n, Make, Zapier, Apache Airflow
 </li>
 <li>
 Knowledge Systems: Pinecone, Weaviate, pgvector, Qdrant, Chroma, LlamaIndex
 </li>
 <li>
 Engineering: Python, TypeScript, FastAPI, REST APIs, Docker, Kubernetes, Git, CI/CD
 </li>
 <li>
 Cloud Platforms: AWS, Azure, Google Cloud Platform
 </li>
 <li>
 AI Observability: LangSmith, Helicone, MLflow, Weights &amp; Biases, OpenTelemetry
 </li>
 </ul>
</div>

AI Engineering Lead – LLM Engineering, AI Development & Intelligent Automation

Huspy

<div>
 
 The Story So Far: We&rsquo;re Building a Global Brand in Real Estate
 
 
 
 Huspy is one of the leading property technology companies in EMEA.
 
 
 Launched in 2020, we now operate in multiple cities across the UAE and Spain, expanding into Saudi Arabia and 3 more European markets by 2026. Today, we own the largest portion of the UAE mortgage market and are one of the fastest-growing players in every European city we&rsquo;ve entered.
 
 
 We&rsquo;ve raised over $140 million (Series A and Series B) from the world&rsquo;s top investors, including Sequoia Capital, Founders Fund and Balderton Capital, to reshape the homebuying journey through powerful technology and agent-first tools.
 
 
 We&rsquo;ve built a SuperApp that empowers real estate agents and mortgage brokers, bringing cutting‑edge technology to one of the world&rsquo;s most traditional industries. We&rsquo;re transforming how property transactions happen &mdash; faster, smarter, and better for everyone.
 
 
 We&rsquo;re not slowing down. The question is: will you be part of what&rsquo;s next?
 
 
 
 The Main Event: What You&rsquo;ll Drive, Build, and Own
 
 
 
 You will be working closely with Product, Data, and Engineering teams responsible for developing and enhancing AI-powered solutions across our Real Estate Agent Platform and Mortgage Broker Portal. You'll leverage modern machine learning technologies and cloud infrastructure to build intelligent, data-driven products in an innovative and collaborative environment.
 
 
 
 Tech stack:
 
 Python, PostgreSQL, AWS, FastAPI
 
 
 
 Hands-On Engineering:
 
 Contribute to the development, deployment, and optimisation of machine learning models and AI-driven services. Take ownership of key technical deliverables while leading by example in engineering best practices.
 
 
 
 Machine Learning Excellence:
 
 Drive best practices in machine learning development, including data preparation, feature engineering, model training, evaluation, deployment, and monitoring. Ensure solutions are scalable, reliable, and maintainable in production environments.
 
 
 
 Stakeholder Collaboration:
 
 Work closely with Product, Data, Design, and Engineering teams to identify opportunities for AI-driven innovation, translate business requirements into technical solutions, and deliver measurable impact.
 
 
 
 The Perfect Match: What It Takes to Succeed at Huspy
 
 
 
 
 AI/ML Engineering Experience:
 
 Strong technical background in Python and machine learning, with a passion for building intelligent products and solving complex data-driven challenges.
 
 
 
 Product-Focused Mindset:
 
 Ability to work cross-functionally and collaborate with Product, Data, and Engineering teams to deliver impactful AI-powered solutions that enhance customer experiences and business outcomes.
 
 
 
 Technical Expertise:
 
 Experience developing and deploying machine learning models using Python and modern ML frameworks. Strong knowledge of data processing, model lifecycle management, APIs, CI/CD, cloud platforms (AWS preferred), and production-grade software engineering practices. Experience in AI development practices and tools is a must-have.
 
 
 
 Data &amp; Machine Learning Knowledge:
 
 Experience working with structured and unstructured data, feature engineering, model evaluation, experimentation, and performance optimisation. Familiarity with MLOps practices and model monitoring is highly desirable.
 
 
 
 Collaborative Attitude:
 
 Excellent communication skills with the ability to explain technical concepts to both technical and non-technical stakeholders while fostering an inclusive, engaging team environment.
 
 
 
 Work Setup:
 
 Hybrid in Dubai or Madrid, remote anywhere in the EU. Or we offer relocation support to Dubai, if preferred.
 
 
 By submitting this application, I agree that my personal data will be collected, processed, and retained by the company solely for the purposes of managing and assessing my candidacy.
 
 
 By submitting this application, I agree that my personal data will be collected, processed, and retained by the company solely for the purposes of managing and assessing my candidacy.
</div>

AI Engineer

Thinkn

<div>
 
 
 About Thinkn
 
 
 
 
 
 
 Thinkn is a technology company focused on building and deploying digital infrastructure across multiple industries, including government systems, financial platforms, and emerging digital asset solutions.
 
 
 
 
 
 We work closely with governments and enterprise partners to design, develop, and implement scalable digital platforms, ranging from national-level infrastructure to private-sector innovation projects. Our focus is on delivering practical, high-impact solutions that combine technology, efficiency, and long-term value.
 
 
 
 
 
 As part of our growth, we are strengthening our finance function to support operational scale, financial control, and compliance across multiple entities and jurisdictions.
 
 
 
 
 
 ----
 
 
 
 
 
 
 Our Needs
 
 
 
 
 
 
 We are seeking a Full Stack Engineer to join our engineering team. This role will be essential in developing scalable applications, delivering new product features, and supporting the continued growth of our platforms. You will work across key technical areas including frontend development, backend services, APIs, system architecture, and AI-powered solutions, bringing flexibility and value across the business.
 
 
 
 
 
 You'll be responsible for developing and maintaining robust applications, designing efficient data structures, and building high-quality user experiences. You'll work closely with our product and engineering teams and will play a hands-on role in delivering features, integrating systems, and improving development workflows.
 
 
 
 
 
 We are looking for someone who embraces AI as part of their day-to-day development process and is comfortable working with modern AI tools, coding assistants, and AI agents. This role requires someone who can work independently, proactively identify improvements, and contribute meaningfully to both product development and technical innovation.
 
 
 
 
 
 Our headquarters are based in Dubai. While this role is remote-friendly, preference will be given to candidates already based in, or willing to relocate to Dubai. For the right candidate, we are open to fully remote arrangements.
 
 
 
 
 
 ----
 
 
 
 
 
 
 Responsibilities
 
 :
 
 
 
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Design, develop, and maintain scalable web applications across both frontend and backend systems
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Build and maintain APIs, integrations, and backend services that support business and product requirements
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Develop intuitive, responsive, and high-quality user interfaces using modern frontend frameworks
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Design and optimise application architecture, data models, and system performance
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Work closely with product, design, and engineering teams to deliver new features and enhancements
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Leverage AI tools, coding assistants, and agent-based workflows to improve development efficiency and software quality
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Develop and integrate AI-powered features, automations, and workflows into products and internal systems
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Maintain and improve CI/CD pipelines, testing processes, and deployment workflows
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Troubleshoot, debug, and resolve issues across the full technology stack
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Maintain documentation for applications, integrations, and technical processes
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Continuously identify opportunities to improve applications, development workflows, and system performance
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Contribute to technical decision-making, architecture discussions, and product innovation initiatives
 
 
 
 
 
 ----
 
 
 
 
 
 
 Qualifications
 
 :
 
 
 
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5+ years of hands-on experience in full-stack software development
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Strong experience with modern frontend and backend frameworks and technologies
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Experience designing, building, and maintaining scalable web applications and APIs
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Proficiency with one or more cloud platforms (AWS, Azure, GCP)
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Experience with containerisation technologies such as Docker
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Strong knowledge of Git, version control, and collaborative development workflows
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Experience working with databases, system integrations, and application architecture
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Experience using AI-assisted development tools such as ChatGPT, Claude, Cursor, GitHub Copilot, or similar
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Understanding of AI agents, LLM integrations, prompt engineering, and AI-powered workflows
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Experience building or integrating AI-powered features, automations, or agent-based solutions is highly desirable
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Strong problem-solving skills and the ability to work independently in a fast-paced environment
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Understanding of security principles and best practices for modern web applications
 
 
 &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Strong communication skills and ability to collaborate across product, design, and engineering teams
 
 
 
 
 
 ----
 
 
 
 
 
 Thinkn is committed to equal opportunity for all, regardless of gender, age, disability, race, ethnicity, religion, political beliefs, marital status, or sexual orientation. We value diversity and encourage applicants from all backgrounds to apply.
 
 
 
 
</div>

Full Stack Engineer

Group AMANA

<div>
 
 Amana is seeking an experienced Data Platform Lead with deep expertise in Databricks, the Microsoft Azure ecosystem, and modern lakehouse architectures. The ideal candidate will play a central role in Amana's ongoing platform refresh, moving workloads from Synapse Analytics to a Databricks lakehouse with Unity Catalog, and building the technical foundation that powers analytics, AI, and governance across the enterprise. This role sits within Amana's Data Science, Governance &amp; AI Centre of Excellence, working alongside architects, engineers, analysts, data scientists, and AI engineers, with future growth opportunities into senior platform leadership.
 
 
 
 
 
 
 Why This Role Matters
 
 
 
 Amana operates across capital-intensive sectors where data reliability, cost discipline, and speed of insight are business-critical. The enterprise data platform is the backbone of every downstream analytics, AI, and governance initiative &mdash; from ERP-integrated reporting and predictive models to Agentic AI and Copilot rollouts across the group.
 
 
 You will be at the technical centre of this transformation, designing, building, and hardening the lakehouse that the rest of the function depends on.
 
 
 
 
 
 
 Core Responsibilities
 
 
 
 
 Lakehouse Architecture &amp; Delivery
 
 
 <ul>
 <li>
 Design and deliver end-to-end Databricks lakehouse components &mdash; ingestion, transformation, semantic layer, and serving.
 </li>
 <li>
 Set and enforce Delta Lake and medallion architecture standards across bronze, silver, and gold layers.
 </li>
 <li>
 Lead the technical execution of the Synapse Analytics to Databricks migration, including parallel run, cutover, and decommissioning.
 </li>
 <li>
 Contribute to and sign off on reference designs, integration patterns, and architecture decision records.
 </li>
 </ul>
 
 
 
 
 
 Data Engineering &amp; Integration
 
 
 <ul>
 <li>
 Build and maintain robust ingestion and transformation pipelines from Amana's core enterprise systems: Oracle Fusion (Finance, HCM, P2P, CRM), Hyperion, CMiC, project and operations systems, SharePoint, and Power Platform.
 </li>
 <li>
 Apply strong CI/CD, DataOps, and infrastructure-as-code practices using Git, Azure DevOps, and Terraform or Bicep.
 </li>
 <li>
 Ensure platform reliability through monitoring, alerting, and structured incident response.
 </li>
 </ul>
 
 
 
 
 
 AI/ML &amp; Analytics Enablement
 
 
 <ul>
 <li>
 Build and operate feature store, MLOps, and LLMOps foundations on Databricks.
 </li>
 <li>
 Enable production-grade RAG, agent, and Copilot patterns in partnership with the Data Scientist and AI Engineer.
 </li>
 <li>
 Deliver Power BI semantic models, certified datasets, and self-service guardrails supporting the Amana Digital Academy and enterprise adoption.
 </li>
 </ul>
 
 
 
 
 
 Data Governance &amp; Trustworthy Data
 
 
 <ul>
 <li>
 Implement lineage, cataloguing, access control, data quality, and PDPL controls inside the platform using Unity Catalog and Microsoft Purview.
 </li>
 <li>
 Partner with the Data Governance Office to translate live policies into enforceable platform controls.
 </li>
 <li>
 Support regulatory, ethical, and internal compliance obligations across the data estate.
 </li>
 </ul>
 
 
 
 
 
 Cloud &amp; Cost Discipline
 
 
 <ul>
 <li>
 Own FinOps hygiene on the platform: cluster policies, workload tiering, rightsizing, and chargeback readiness.
 </li>
 <li>
 Translate cloud cost outcomes into commercial terms understood by finance and business stakeholders.
 </li>
 </ul>
 
 
 
 
 
 Collaboration &amp; Technical Leadership
 
 
 <ul>
 <li>
 Work closely with the Data Architect, Data Engineer, Data Analyst, Data Scientist, and AI Engineer to set and uphold technical standards.
 </li>
 <li>
 Provide hands-on technical guidance and code review across the team.
 </li>
 <li>
 Translate business requirements into technical designs with clear delivery outcomes.
 </li>
 </ul>
 
 
 
 
 
 Required Qualifications
 
 
 <ul>
 <li>
 Minimum of 7 years in data engineering or data platform work, with strong hands-on delivery experience.
 </li>
 <li>
 At least 3 years hands-on with Databricks, Apache Spark, and Delta Lake at enterprise scale. Snowflake or BigQuery experience is acceptable where the CV shows a real Databricks migration.
 </li>
 <li>
 Contributed to the build or rebuild of an enterprise data platform serving multiple business domains.
 </li>
 <li>
 Deep hands-on experience with the Azure data stack: Databricks, ADLS, Azure Data Factory, Synapse, Azure SQL, Fabric.
 </li>
 <li>
 Direct experience integrating heavy ERP sources: Oracle Fusion, Hyperion, CMiC, or equivalent estates.
 </li>
 <li>
 Governance shipped to production using Unity Catalog or Microsoft Purview, with PDPL or GDPR controls operational, not theoretical.
 </li>
 <li>
 Practical FinOps outcomes on cloud spend.
 </li>
 <li>
 Strong on CI/CD, Git, Azure DevOps, and infrastructure-as-code (Terraform or Bicep).
 </li>
 <li>
 Excellent written and verbal communication skills across technical and business audiences.
 </li>
 </ul>
 
 
 
 
 
 Preferred Qualifications
 
 
 <ul>
 <li>
 Direct experience of a Synapse Analytics to Databricks migration.
 </li>
 <li>
 Exposure to MLOps and LLMOps stacks: MLflow, feature store, vector stores, RAG, agent frameworks.
 </li>
 <li>
 Familiarity with Power BI semantic models and enterprise BI governance.
 </li>
 <li>
 Streaming and event-driven experience: Kafka, Event Hubs, Structured Streaming.
 </li>
 <li>
 Multi-entity, multi-country group exposure with mixed data residency considerations.
 </li>
 <li>
 Industry experience in construction, engineering services, manufacturing, energy, or logistics.
 </li>
 </ul>
 
 
 
 
 
 Certifications
 
 
 
 
 
 Mandatory:
 
 
 
 <ul>
 <li>
 
 Databricks Certified Data Engineer Professional
 
 (Candidates holding the Associate level will be considered on the condition that Professional is achieved within 6 months of joining.)
 </li>
 </ul>
 
 
 Preferred (one or more):
 
 
 <ul>
 <li>
 
 Databricks Certified Machine Learning Professional
 
 </li>
 <li>
 
 Databricks Certified Generative AI Engineer Associate
 
 </li>
 <li>
 
 Microsoft Certified: Azure Data Engineer Associate
 
 (DP-203 / DP-700)
 </li>
 <li>
 
 Microsoft Certified: Fabric Analytics Engineer Associate
 
 (DP-600)
 </li>
 <li>
 
 Microsoft Certified: Azure Solutions Architect Expert
 
 </li>
 <li>
 
 Certified Data Management Professional (CDMP)
 
 &mdash; DAMA
 </li>
 </ul>
 
 
 Education
 
 
 
 M.Tech / B.E. / B.Tech (Computer Science, Information Systems, IT) / MCA / MCS.
 
</div>

Data Platform Lead

Luxwell Real Estate

About Luxwell Real EstateLuxwell Real Estate is a boutique real estate agency in Dubai, built on quality over quantity. We work closely with each Agent, offering proper training, hands-on guidance, and real market exposure. Our focus is on building long-term, successful agents, not just closing short-term deals.Role OverviewWe are looking for motivated Real Estate Consultants who want to grow seriously in the Dubai real estate market. At Luxwell, you won&rsquo;t be just another agent. You will receive structured support, qualified leads, and direct mentorship to help you build a strong and sustainable career in both off-plan and secondary markets.Key Responsibilities<ul><li>Advise clients on off-plan and secondary market properties</li><li>Handle qualified leads and convert them into successful transactions</li><li>Conduct property viewings and client meetings</li><li>Understand client needs and provide tailored investment advice</li><li>Manage listings, follow-ups, and CRM updates</li><li>Negotiate offers and support clients through the full transaction cycle</li><li>Build long-term relationships and a strong personal client base</li></ul>What We&rsquo;re Looking For<ul><li>Strong communication and interpersonal skills</li><li>Hunger to learn and grow in Dubai real estate</li><li>Self-driven, disciplined, and professional</li><li>Prior real estate experience is a plus, but not mandatory</li></ul>What We Offer<ul><li>One-on-one training and continuous guidance</li><li>Highly qualified and warm leads</li><li>Exposure to top off-plan developers and strong secondary inventory</li><li>A boutique environment where your growth actually matters</li><li>Clear path to becoming a top-performing agent</li></ul>Why Join Luxwell<ul><li>If you want to become a successful real estate consultant - with the right support, real training, and quality leads - Luxwell is the place to do it.</li></ul>Job Types: Full-time, FresherWork Location: In person

Real Estate Sales Agent

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