artificial intelligence specialist

Roads and Transport Authority

<div>
 
 
 Job Purpose:
 
 
 
 Support the Artificial Intelligence and Data Science Center by contributing to AI use case development, dashboard design, and model validation. Assist in requirements analysis, technical evaluations, testing, and project documentation to ensure effective delivery of AI solutions. Participate in monitoring industry trends, collaborating with suppliers, and attending events and training workshops. Strengthen project outcomes by applying AGILE practices and supporting ongoing improvements to departmental processes.
 
 
 
 
 
 
 Job Qualifications &amp; Requirements:
 
 
 
 
 Education:
 
 Bachelor/master&rsquo;s degree in computer science
 
 
 
 Experience:
 
 0+ years in case of master&rsquo;s degree (2+ years in case of bachelor&rsquo;s degree)
 
 
 
</div>

AI Developer - Artificial Intelligence and Data Science Center

Ministry of Economy & Tourism

<div>
 
 
 الهدف العام للوظيفة
 
 
 
 خبير في الذكاء الاصطناعي ضمن قسم الذكاء الاصطناعي في الوزارة، يتولى ضمان نشر أنظمة الذكاء الاصطناعي بصورة فعّالة وأخلاقية وقابلة للتوسع عبر عمليات الوزارة. يجسّر هذا الدور الفجوة بين القدرات التقنية للذكاء الاصطناعي والاحتياجات المؤسسية الحيوية، من خلال الإشراف البشري المتمحور حول الإنسان وحوكمة الأنظمة وتحسينها المستمر. يجمع هذا المنصب بين العمق التقني ومعرفة الأعمال وإدارة أصحاب المصلحة وقيادة التغيير لضمان استدامة تطبيقات الذكاء الاصطناعي وتحقيقها لقيمة مؤسسية قابلة للقياس
 
 
 
 المسؤوليات الرئيسية
 
 
 <ul>
 <li>
 تحديد متطلبات البيانات الجاهزة للذكاء الاصطناعي والإشراف عليها بما يشمل بيانات التدريب وبيانات الإدخال التشغيلية ونماذج القرار، وضمان جودتها واتساقها.
 </li>
 <li>
 تصميم وإدارة سير عمل الإشراف البشري-الآلي (Human-in-the-Loop) للتحقق من مخرجات الذكاء الاصطناعي قبل النشر التشغيلي.
 </li>
 <li>
 مراقبة دقة نماذج الذكاء الاصطناعي ومستويات الثقة والكشف عن انحراف الأداء للحفاظ على الفاعلية التشغيلية وضمان الاستمرارية.
 </li>
 <li>
 اختبار وكلاء الذكاء الاصطناعي وتدريبهم من خلال آليات التغذية الراجعة وهندسة الطلبات وبروتوكولات التعلم الخاضع للإشراف.
 </li>
 <li>
 تطوير وتطبيق أطر حوكمة الذكاء الاصطناعي والمبادئ التوجيهية الأخلاقية وسياسات الامتثال المتوافقة مع اللوائح الحكومية المعتمدة.
 </li>
 <li>
 المشاركة في تصنيف تطبيقات الذكاء الاصطناعي حسب مستوى مخاطرها وتأثيرها، وإرساء معايير رقابة متدرجة تتناسب مع درجة خطورة كل تطبيق.
 </li>
 <li>
 قيادة الإشراف على الموردين وحوكمة العقود لضمان السلطة التقنية والشفافية ومنع الاحتكار التقني من قِبل الموردين.
 </li>
 <li>
 تحديد مخاطر الذكاء الاصطناعي والتخفيف منها بما تشمل عدم موضوعية النموذج في نتائجه، ومحاولات التلاعب بالنموذج، وإفساد بيانات التدريب، وتراجع أداء النماذج بمرور الوقت.
 </li>
 <li>
 تصميم وقيادة برامج التثقيف في الذكاء الاصطناعي للمطورين والمستخدمين وأصحاب المصلحة المتأثرين بتطبيقات الذكاء الاصطناعي عبر الوزارة.
 </li>
 <li>
 إرشاد فرق العمل المشتركة وتنسيق التعاون متعدد التخصصات بين فرق تطوير الحلول والهندسة والتشغيل والأعمال.
 </li>
 <li>
 ترجمة القدرات والقيود التقنية لأنظمة الذكاء الاصطناعي إلى توصيات عملية وتوجيهات واضحة تُمكّن الإدارة وأصحاب المصلحة من اتخاذ قرارات مدروسة.
 </li>
 <li>
 تحديد مؤشرات أداء نماذج الذكاء الاصطناعي ومتابعتها بما تشمل نسبة الدقة، ودرجة يقين النموذج، ومعدلات قبول المخرجات، ومؤشرات انحراف الأداء، وإعداد التقارير الدورية.
 </li>
 <li>
 إدارة حلقات التغذية الراجعة من مستخدمي الذكاء الاصطناعي والفرق التشغيلية لتحسين النماذج والعمليات باستمرار.
 </li>
 <li>
 تحديد حالات استخدام الذكاء الاصطناعي الناشئة والتوصية بتحسينات على سير العمل والواجهات لتحقيق أقصى قيمة مؤسسية.
 </li>
 <li>
 دعم التوسع في نشر تطبيقات الذكاء الاصطناعي عبر الوزارة من خلال توحيد المعايير، وبناء قوالب وأدوات جاهزة قابلة لإعادة الاستخدام، وتوثيق أفضل الممارسات لضمان الاتساق والكفاءة.
 </li>
 <li>
 التعامل مع عزوف الموظفين وترددهم في تبني الذكاء الاصطناعي من خلال ربط تطبيقاته بواقع عمل كل فريق، وبناء الثقة، وتعزيز الانفتاح لتسريع تبني الحلول الذكية.
 </li>
 <li>
 بناء شبكات تبادل المعرفة والخبرات في مجال الذكاء الاصطناعي عبر الجهات الحكومية، وتعزيز التعاون ومشاركة أفضل الممارسات بين الكوادر المتخصصة.
 </li>
 <li>
 التحقق من مخرجات نماذج الذكاء الاصطناعي وتحديد الحالات الحدية والاستثنائية، وتطبيق الحكم البشري المدروس في القرارات المعقدة لضمان موثوقية الأنظمة ودقتها.
 </li>
 <li>
 تنفيذ أي مهام أخرى يكلف بها رئيس القسم في نطاق اختصاصه.
 </li>
 </ul>
 
 
 المؤهلات والخبرة
 
 
 <ul>
 <li>
 دكتوراه - علوم الحاسب / الذكاء الاصطناعي / هندسة / إحصاء أو ما يعادلها (بدون خبرة)
 </li>
 <li>
 ماجستير - علوم الحاسب / الذكاء الاصطناعي / علوم البيانات / هندسة أو ما يعادلها (3 سنوات خبرة)
 </li>
 <li>
 بكالوريوس - علوم الحاسب / الذكاء الاصطناعي / هندسة الحاسوب / إحصاء أو ما يعادلها (7 سنوات خبرة)
 </li>
 </ul>
 
 
 الشهادات المهنية
 
 
 <ul>
 <li>
 شهادة معتمدة في الذكاء الاصطناعي وتعلم الآلة على المنصات السحابية مثل Google Cloud Professional ML Engineer / AWS Machine Learning Specialty / Microsoft Azure AI Engineer Associate
 </li>
 <li>
 شهادة في نماذج اللغة الكبيرة وهندسة الطلبات مثل DeepLearning.AI Generative AI / Prompt Engineering Specialization
 </li>
 <li>
 شهادة في حوكمة الذكاء الاصطناعي وأخلاقياته مثل IAPP AI Governance Professional / ISO 42001 Lead Implementer)
 </li>
 <li>
 شهادة في علوم البيانات وهندستها مثل IBM Data Science Professional / Databricks Data Engineer Associate)
 </li>
 <li>
 شهادة في تشغيل نماذج الذكاء الاصطناعي مثل MLflow MLOps Certified / Kubeflow Practitioner / DataRobot MLOps
 </li>
 <li>
 شهادة في أمن الذكاء الاصطناعي والهجمات المعادية مثل Certified AI Security Professional / OWASP AI Security
 </li>
 <li>
 شهادة في إدارة المشاريع وقيادة التغيير أو ما يعادلها PMP / PRINCE2 / Prosci Change Management
 </li>
 <li>
 دورة في تصميم سير العمل الإنساني-الآلي (Human-in-the-Loop) واختبار النماذج وتقييم الانجراف
 </li>
 <li>
 دورة في بنية بيانات الذكاء الاصطناعي ورسم خرائط المعرفة (Knowledge Graphs / Data Mapping)
 </li>
 <li>
 دورة في قياس أداء نماذج الذكاء الاصطناعي ومؤشرات الدقة ومعدلات القبول
 </li>
 <li>
 دورة في أساسيات أمن المعلومات وحماية البيانات في بيئات الذكاء الاصطناعي الحكومية
 </li>
 </ul>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Job Purpose
 
 
 
 Serve as an Artificial Intelligence (AI) expert within the Ministry's AI Department, responsible for ensuring the effective, ethical, and scalable deployment of AI systems across the Ministry's operations. This role bridges the gap between AI technical capabilities and critical business needs through human-centered oversight, AI governance, and continuous optimization. The position combines deep technical expertise with business knowledge, stakeholder management, and change leadership to ensure AI applications remain sustainable and deliver measurable organizational value.
 
 
 
 Key Responsibilities
 
 
 <ul>
 <li>
 Define and oversee AI-ready data requirements, including training datasets, operational input data, and decision models, while ensuring data quality, consistency, and integrity.
 </li>
 <li>
 Design and manage Human-in-the-Loop (HITL) workflows to validate AI outputs before operational deployment.
 </li>
 <li>
 Monitor AI model accuracy, confidence levels, and performance drift to maintain operational effectiveness and ensure service continuity.
 </li>
 <li>
 Test and train AI agents using feedback mechanisms, prompt engineering, and supervised learning protocols.
 </li>
 <li>
 Develop and implement AI governance frameworks, ethical guidelines, and compliance policies aligned with approved government regulations.
 </li>
 <li>
 Participate in classifying AI applications according to their risk and impact levels and establish proportionate control measures based on the level of risk.
 </li>
 <li>
 Lead vendor oversight and contract governance to ensure technical ownership, transparency, and prevent vendor lock-in.
 </li>
 <li>
 Identify and mitigate AI-related risks, including model bias, adversarial attacks, training data poisoning, and model performance degradation over time.
 </li>
 <li>
 Design and lead AI awareness and education programs for developers, end users, and stakeholders affected by AI implementations across the Ministry.
 </li>
 <li>
 Guide cross-functional teams and coordinate multidisciplinary collaboration among solution development, engineering, operations, and business teams.
 </li>
 <li>
 Translate the technical capabilities and limitations of AI systems into practical recommendations and clear guidance to support informed decision-making by management and stakeholders.
 </li>
 <li>
 Define and monitor AI model performance metrics, including accuracy rates, confidence scores, output acceptance rates, and model drift indicators, and prepare periodic performance reports.
 </li>
 <li>
 Manage feedback loops from AI users and operational teams to continuously improve AI models and business processes.
 </li>
 <li>
 Identify emerging AI use cases and recommend workflow and user interface enhancements to maximize organizational value.
 </li>
 <li>
 Support the enterprise-wide scaling of AI applications by standardizing practices, developing reusable templates and tools, and documenting best practices to ensure consistency and efficiency.
 </li>
 <li>
 Address employee resistance and hesitation toward AI adoption by aligning AI solutions with team-specific business needs, building trust, and promoting openness to accelerate the adoption of intelligent solutions.
 </li>
 <li>
 Build knowledge-sharing networks and AI communities of practice across government entities, fostering collaboration and the exchange of best practices among AI professionals.
 </li>
 <li>
 Validate AI model outputs, identify edge cases and exceptional scenarios, and apply sound human judgment in complex decision-making to ensure the reliability and accuracy of AI systems.
 </li>
 <li>
 Perform any other duties assigned by the Head of the AI Department within the scope of the role.
 </li>
 </ul>
 
 
 Qualifications &amp; Experience
 
 
 <ul>
 <li>
 PhD in Computer Science, Artificial Intelligence, Engineering, Statistics, or a related field (no experience required).
 </li>
 <li>
 Master's Degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field (minimum 3 years of relevant experience).
 </li>
 <li>
 Bachelor's Degree in Computer Science, Artificial Intelligence, Computer Engineering, Statistics, or a related field (minimum 7 years of relevant experience).
 </li>
 </ul>
 
 
 Professional Certifications
 
 
 <ul>
 <li>
 Professional certification in Artificial Intelligence and Machine Learning on cloud platforms, such as
 
 Google Cloud Professional Machine Learning Engineer
 
 ,
 
 AWS Machine Learning Specialty
 
 , or
 
 Microsoft Azure AI Engineer Associate
 
 .
 </li>
 <li>
 Certification in Large Language Models (LLMs) and Prompt Engineering, such as
 
 DeepLearning.AI Generative AI Specialization
 
 or
 
 Prompt Engineering Specialization
 
 .
 </li>
 <li>
 Certification in AI Governance and Ethics, such as
 
 IAPP AI Governance Professional (AIGP)
 
 or
 
 ISO/IEC 42001 Lead Implementer
 
 .
 </li>
 <li>
 Certification in Data Science and Data Engineering, such as
 
 IBM Data Science Professional Certificate
 
 or
 
 Databricks Data Engineer Associate
 
 .
 </li>
 <li>
 Certification in AI Operations (MLOps), such as
 
 MLflow MLOps Certified
 
 ,
 
 Kubeflow Practitioner
 
 , or
 
 DataRobot MLOps
 
 .
 </li>
 <li>
 Certification in AI Security and Adversarial AI, such as
 
 Certified AI Security Professional
 
 or
 
 OWASP AI Security
 
 .
 </li>
 <li>
 Certification in Project Management and Change Leadership, such as
 
 PMP
 
 ,
 
 PRINCE2
 
 , or
 
 Prosci Change Management
 
 .
 </li>
 <li>
 Training course in
 
 Human-in-the-Loop (HITL)
 
 workflow design, AI model testing, and model drift evaluation.
 </li>
 <li>
 Training course in AI data architecture, data mapping, and knowledge graphs.
 </li>
 <li>
 Training course in AI model performance measurement, including accuracy metrics, confidence scores, and output acceptance rates.
 </li>
 <li>
 Training course in information security fundamentals and data protection in government AI environments.
 </li>
 </ul>
 
 
 
</div>

خبير الذكاء الاصطناعي - Artificial Intelligence Expert

Tata Consultancy Services

<div>
 
 
 Job Title
 
 &ndash; AI 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:
 
 
 <ul>
 <li>
 
 AI Solution Design &amp; Development:
 
 </li>
 <li>
 Design, build, and deploy AI-powered applications using Azure AI Services, including Azure OpenAI Service, Azure Machine Learning, Azure Cognitive Services (Speech, Vision, Language), Azure AI Search, Azure Functions, and Azure Databricks.
 </li>
 <li>
 
 LLM Integration &amp; Generative AI:
 
 </li>
 <li>
 Develop and integrate Large Language Model (LLM) solutions using Azure OpenAI endpoints (GPT-4.1, GPT-4o, GPT-4o-mini) routed through FAB's centralized AI Hub gateway for governance, observability, and capacity management. Build enterprise use cases such as knowledge search, document intelligence, content summarization, call analytics, sentiment analysis, and workflow automation.
 </li>
 <li>
 
 Python Engineering &amp; Backend Development:
 
 </li>
 <li>
 Write clean, modular, and production-grade Python code for AI/ML model development, API integrations, data processing pipelines, backend services, and automation workflows using frameworks such as LangChain, LlamaIndex, Semantic Kernel, LangGraph, and FastAPI.
 </li>
 <li>
 
 RAG &amp; Semantic Retrieval:
 
 </li>
 <li>
 Implement Retrieval-Augmented Generation (RAG) pipelines, embeddings, vector search, semantic retrieval architectures, prompt engineering, and evaluation techniques for enterprise knowledge mining and document intelligence.
 </li>
 <li>
 
 Agentic AI Development:
 
 </li>
 <li>
 Design and develop intelligent agentic AI systems capable of planning, reasoning, tool execution, and orchestration across enterprise systems using FAB's GERNAS OS platform and Agent Development Kit (ADK) in a secure and governed manner.
 </li>
 <li>
 
 Model Evaluation &amp; Responsible AI:
 
 </li>
 <li>
 Implement model evaluation metrics for accuracy, hallucination detection, bias, latency, and throughput in alignment with FAB's Responsible AI framework. Ensure AI solutions comply with FAB's security, data privacy, model governance, and regulatory requirements, including controls for accuracy, hallucination, bias, latency, and resilience.
 </li>
 <li>
 
 Deployment &amp; Production Operations:
 
 </li>
 <li>
 Deploy, monitor, and optimize AI solutions in production environments using CI/CD practices, containerization, observability, logging, performance monitoring, and cost optimization techniques.
 </li>
 <li>
 
 Collaboration &amp; Stakeholder Engagement:
 
 </li>
 <li>
 Collaborate with data scientists, platform engineers, DevOps/MLOps engineers, business analysts, and product teams to convert business requirements into scalable AI solutions.
 </li>
 <li>
 
 Innovation &amp; Continuous Learning:
 
 </li>
 <li>
 Continuously evaluate emerging Azure AI, Generative AI, and Agentic AI capabilities and recommend practical adoption opportunities aligned with FAB's AI strategy. Stay abreast of state-of-the-art developments in generative AI and agentic architectures.
 </li>
 </ul>
 
 
 
 
 
 TECHNICAL SKILLS: minimum 3-4 yrs of working experience mandatory
 
 
 
 
 Azure AI Services:
 
 Demonstrated working experience with Microsoft Azure AI Services including Azure OpenAI, Azure Machine Learning, Azure Cognitive Services, Azure AI Search, Azure Functions, and Azure Databricks.
 
 
 
 Python Programming:
 
 Strong proficiency in Python programming, including experience with REST APIs, SDKs, asynchronous processing, data manipulation, backend development, and AI application frameworks (LangChain, LlamaIndex, Semantic Kernel, LangGraph, FastAPI).
 
 
 
 LLM &amp; Generative AI:
 
 Deep understanding of machine learning, statistical modeling, NLP, generative AI principles, LLM application development, prompt engineering, RAG architecture, embeddings, vector databases, semantic search, and model evaluation techniques.
 
 
 
 ML Libraries &amp; Frameworks:
 
 Advanced proficiency in ML libraries such as PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, and NLP libraries (spaCy, NLTK).
 
 
 
 Vector Databases:
 
 Experience with vector databases including FAISS, Azure AI Search, ChromaDB, and Pinecone.
 
 
 
 DevOps/MLOps:
 
 Hands-on experience with DevOps/MLOps practices and tools such as Git, Docker, Kubernetes, CI/CD pipelines, MLflow, Terraform, Azure Monitor, and Application Insights.
 
 
 
 Cloud Security &amp; Integration:
 
 Understanding of cloud security, identity and access management, data privacy, encryption, logging, monitoring, and secure API integration patterns.
 
 
 
 AI Ethics &amp; Governance:
 
 Awareness of ethical considerations and responsible AI practices, including fairness, accountability, transparency, bias detection, hallucination mitigation, and compliance in AI systems.
 
 
 
 
 
 
 KNOWLEDGE, SKILLS, &amp; EXPERIENCE
 
 
 
 
 
 Minimum Qualifications:
 
 
 
 
 Bachelor's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, Mathematics, Statistics, or related field; Master's degree is preferred. Microsoft Certified: Azure AI Engineer Associate (AI-102) certification is highly preferred.
 
 
 
 
 Minimum Experience:
 
 
 
 
 3+ years of hands-on experience in designing, developing, and deploying AI/ML or Generative AI solutions in production environments.
 
 
 Mandatory hands-on experience with Microsoft Azure AI Services.
 
 
 Experience working with large-scale datasets and real-time enterprise data.
 
 
 Experience in financial services, banking, risk, compliance, customer service, or regulated enterprise environments is an advantage.
 
 
 
 
 Knowledge and Skills:
 
 
 
 
 
 Business Understanding:
 
 
 
 Strong understanding of business processes and the ability to identify opportunities for AI-driven optimization and automation across banking domains.
 
 
 
 Analytical &amp; Problem-Solving:
 
 
 
 Excellent analytical, problem-solving, debugging, and performance optimization skills with the ability to troubleshoot AI applications in production and translate complex business problems into AI solutions.
 
 
 
 Communication Skills:
 
 
 
 Strong verbal and written communication skills, capable of explaining complex AI concepts to both technical and non-technical stakeholders.
 
 
 
 Collaboration:
 
 
 
 Proven ability to work effectively in cross-functional teams, collaborating with data scientists, platform engineers, DevOps engineers, business analysts, and product teams.
 
 
 
 Responsible AI Mindset:
 
 
 
 Understanding of AI governance, model risk, data privacy, security, and compliance requirements in a regulated banking environment.
 
 
 
 Production Engineering:
 
 
 
 Ability to build, deploy, monitor, and optimize production-grade AI systems with attention to reliability, scalability, cost, and operational excellence.
 
 
 
 Adaptability &amp; Learning:
 
 
 
 Willingness to stay updated on the latest AI technologies, agentic frameworks, and cloud services, and the ability to apply them to solve evolving business challenges.
 
 
 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 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

<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

Jorofy Private Limited

We are seeking a highly skilled Senior AI Engineer (PhD) to lead the design, development, and deployment of advanced Artificial Intelligence and Machine Learning solutions. The ideal candidate will possess a PhD in Artificial Intelligence, Machine Learning, Computer Science, Data Science, or a related field, along with extensive industry experience in developing AI-driven products, conducting research, and translating innovative concepts into scalable enterprise solutions.The role requires expertise in Generative AI, Large Language Models (LLMs), Deep Learning, Natural Language Processing (NLP), Computer Vision, and MLOps practices.Key ResponsibilitiesAI Research &amp; Innovation<ul><li>Conduct advanced research in Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI.</li><li>Evaluate emerging AI technologies, frameworks, and methodologies to enhance organizational capabilities.</li><li>Publish research findings, technical papers, patents, or contribute to industry thought leadership initiatives.</li><li>Lead proof-of-concept (PoC) development for innovative AI solutions.</li></ul>Solution Design &amp; Development<ul><li>Architect and develop scalable AI/ML models and intelligent systems.</li><li>Design and optimize Large Language Model (LLM)-based applications and AI agents.</li><li>Build and fine-tune machine learning and deep learning models for production environments.</li><li>Develop AI-powered applications involving NLP, Computer Vision, Predictive Analytics, and Recommendation Systems.</li><li>Integrate AI solutions with enterprise platforms, APIs, and cloud environments.</li></ul>Generative AI &amp; LLM Engineering<ul><li>Develop and optimize Retrieval-Augmented Generation (RAG) architectures.</li><li>Fine-tune foundation models and implement prompt engineering strategies.</li><li>Design AI agents, autonomous workflows, and conversational AI solutions.</li><li>Implement vector databases and semantic search frameworks.</li><li>Evaluate model performance, accuracy, safety, and responsible AI practices.</li></ul>MLOps &amp; Deployment<ul><li>Build end-to-end ML pipelines for model training, deployment, monitoring, and maintenance.</li><li>Implement CI/CD processes for AI applications.</li><li>Optimize model performance, scalability, and infrastructure costs.</li><li>Monitor model drift and ensure continuous improvement.</li></ul>Leadership &amp; Collaboration<ul><li>Provide technical leadership and mentorship to AI Engineers, Data Scientists, and ML Engineers.</li><li>Collaborate with business stakeholders to identify AI opportunities and define solution roadmaps.</li><li>Participate in strategic technology planning and AI transformation initiatives.</li><li>Review code, architecture, and technical deliverables to ensure quality standards.</li></ul>Required QualificationsEducation<ul><li>PhD in Artificial Intelligence, Machine Learning, Computer Science, Data Science, Mathematics, Statistics, or a related discipline.</li></ul>Experience<ul><li>8+ years of experience in AI/ML development, including research and production deployment.</li><li>Proven experience leading AI projects in enterprise environments.</li><li>Experience working with Generative AI, LLMs, and advanced deep learning architectures.</li></ul>Technical SkillsProgramming<ul><li>Python (Advanced)</li><li>SQL</li><li>Java, C++, or Scala (Preferred)</li></ul>AI/ML Frameworks<ul><li>PyTorch</li><li>TensorFlow</li><li>Keras</li><li>Scikit-learn</li><li>Hugging Face Transformers</li></ul>Generative AI &amp; LLM Technologies<ul><li>OpenAI APIs</li><li>LangChain</li><li>LangGraph</li><li>LlamaIndex</li><li>RAG Architectures</li><li>Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS)</li></ul>Cloud Platforms<ul><li>AWS</li><li>Microsoft Azure</li><li>Google Cloud Platform (GCP)</li></ul>MLOps &amp; DevOps<ul><li>Docker</li><li>Kubernetes</li><li>MLflow</li><li>Kubeflow</li><li>Jenkins</li><li>GitHub Actions</li></ul>Databases<ul><li>PostgreSQL</li><li>MongoDB</li><li>Vector Databases</li></ul>Preferred Qualifications<ul><li>Publications in reputed journals or AI conferences (NeurIPS, ICML, ICLR, ACL, CVPR, etc.).</li><li>Experience in Agentic AI, Autonomous Systems, and Multi-Agent Frameworks.</li><li>Knowledge of AI Governance, Responsible AI, Explainable AI (XAI), and AI Security.</li><li>Industry certifications in Cloud, AI, or Machine Learning technologies.</li></ul>Core Competencies<ul><li>Strategic Thinking</li><li>Research &amp; Innovation</li><li>Problem Solving</li><li>Leadership &amp; Team Mentoring</li><li>Stakeholder Management</li><li>Communication &amp; Presentation Skills</li><li>Analytical Thinking</li><li>Project Management</li></ul>Key Deliverables<ul><li>Production-ready AI and Generative AI solutions.</li><li>Scalable machine learning pipelines and architectures.</li><li>AI research initiatives and innovation roadmaps.</li><li>Performance optimization and continuous model improvement.</li><li>Technical mentorship and knowledge-sharing across teams.</li></ul>Experience Required: 8&ndash;15+ Years Education: PhD (Mandatory) Level: Senior / Lead Individual Contributor or Technical Lead Role.Education:<ul><li>Master's (Preferred)</li></ul>Experience:<ul><li>Senior Engineer: 10 years (Required)</li></ul>Work Location: In person

Senior AI Engineer (PhD)

Mondia

<div>
 We're building production AI agents that change how our engineering org ships code and how employees work with internal knowledge. You'll join a small, high-ownership team. You'll write the agent logic, the tools, the data layer, and the pipelines that make them dependable at scale. Turn architecture into reliable, well-tested, production-grade systems.
 
 
 
 Role and Responsibilities :
 
 
 
 <ul>
 <li>
 Implement agent logic, tools, and orchestration against the given architecture
 </li>
 <li>
 Build and tune RAG pipelines: ingestion &rarr; embeddings &rarr; retrieval &rarr; re-ranking &rarr; evaluation
 </li>
 <li>
 Design and optimize Postgres schemas, queries, and vector indexes for production load
 </li>
 <li>
 Build the test-generation and test-execution engine for the AI Code Pipeline
 </li>
 <li>
 Integrate with Git, CI/CD, and internal services
 </li>
 <li>
 Build evaluation harnesses, observability, and guardrails so agents are measurable and safe
 </li>
 <li>
 Own the reliability, latency, and cost of the systems you ship
 </li>
 <li>
 Write clean, typed, well-tested Python and participate actively in code review
 
 
 </li>
 </ul>
 
 Requirements
 
 
 
 <ul>
 <li>
 4+ years of professional software engineering in Python, with deep, hands-on command of the language : async, typing, packaging, performance, and clean, maintainable code
 </li>
 <li>
 1+ years building LLM / agentic systems &mdash; tool use / function calling, orchestration, prompting, and evaluation
 </li>
 <li>
 Strong problem-solving and debugging on ambiguous, real-world production problems
 </li>
 <li>
 Solid Postgres experience: schema design, query optimization, and extensions (pgvector)
 </li>
 <li>
 Experience shipping and operating production services &mdash; testing, CI/CD, observability, on-call mindset
 </li>
 <li>
 Comfort with Git-based workflows and code review
 </li>
 <li>
 Automating software testing: unit, integration, functional, security, performance
 </li>
 <li>
 Integrating with developer tooling and CI/CD systems
 </li>
 <li>
 6+ months of demonstrated experience building with Claude Code in a development workflow
 
 
 </li>
 </ul>
 
 Benefits
 
 
 
 Opportunity to work for a dynamic international company with a flat hierarchical structure, where your voice matters and your impact is seen.
</div>

Agentic AI Engineer

Explore 164+ Artificial intelligence specialist in Dubai this July. Find roles across top companies and apply now to boost your career.