genai engineer

Cloudscaler

<p><b>Responsibilities</b></p><ul><li>Provide technical leadership for GenAI use cases, including prompt design, grounding, orchestration, safety controls, and architecture patterns.</li><li>Drive best practices in prompt engineering, RAG systems, system design, and integration approaches.</li><li>Mentor Agent Makers and support complex builds, reviews, and problem-solving.</li><li>Define ALM strategy for Genesys AI Studio and Rabo AI assistants to ensure consistency and reliability across environments.</li><li>Own AI governance and testing frameworks, including regression testing, LLM-as-a-judge, and end-to-end testing.</li><li>Collaborate with cross-functional teams on design, requirements, and integrations.</li><li>Represent the team in stakeholder discussions and translate technical solutions into business value.</li></ul><p><b>Platforms:</b> Genesys AI Studio, Azure Functions<br/><b>AI &amp; Orchestration:</b> GenAI, prompt engineering, prompt chaining, agent-based automation<br/><b>Integration:</b> REST APIs, event-driven &amp; legacy systems<br/><b>Language:</b> Python<br/><b>Cloud &amp; DevOps:</b> Azure, Azure DevOps<br/><b>Analytics:</b> Databricks, Power BI</p><p><b>Nice to Have</b></p><p>Copilot Studio, Power Platform, Genesys Cloud APIs<br/>JavaScript/TypeScript (Node.js), C#<br/>Security: IAM, VNet, private endpoints, API security, secrets management</p><p><b>Profile</b></p><ul><li>6+ years in Data Engineering / Data Science / Software Engineering, with 2+ years in GenAI</li><li>Experience building conversational AI or agent-based automation</li><li>Strong in AI assistant development, prompt engineering, orchestration, and API integration</li><li>Able to mentor peers and improve engineering quality</li><li>Agile mindset with strong communication skills</li><li>Good understanding of AI governance and testing (regression, LLM-as-a-judge, E2E)</li><li>Passion for building customer-focused GenAI solutions with an innovative mindset</li></ul><p>Job Type: Contract</p><p>Work Location: Hybrid remote in Utrecht</p>

GenAI Engineer

IQVIA

<div><p>We are seeking a highly skilled Senior Generative AI Engineer to join our pioneering team. In this role, you will be instrumental in developing the Orion platform, focused on accelerating the development and delivery of GenAI products. You will help standardize and simplify our technology stack, provide essential components to foster innovation, and shield application teams from technical complexities, enabling them to focus on what they do best.</p>
<p><b>Key Responsibilities</b></p>
<ul><li><p>Platform Development: Design and architect the Orion GenAI Platform to support scalable and efficient GenAI product deployment.</p>
</li><li><p>Accelerate Development: Implement strategies to speed up the development and delivery lifecycle of GenAI products.</p>
</li><li><p>Standardization: Develop and enforce standardized technology stacks and best practices to ensure consistency and quality across projects.</p>
</li><li><p>Innovation Facilitation: Provide standard components and tools that enable application teams to innovate rapidly without reinventing the wheel.</p>
</li><li><p>Technical Shielding: Abstract and manage complex technical challenges, allowing application teams to focus on application-specific functionalities.</p>
</li><li><p>Collaboration: Work closely with cross-functional teams to understand their needs and integrate GenAI solutions seamlessly.</p>
</li></ul><p><b>Qualifications</b></p>
<ul><li><p>Software Design &amp; Architecture: Proven experience in designing robust software architectures for large-scale platforms.</p>
</li><li><p>Development Best Practices: Strong understanding of software development and delivery best practices, including CI/CD pipelines, version control, and testing methodologies.</p>
</li><li><p>Generative AI Expertise: Deep knowledge of transformer architectures, embedding models, large language models (LLMs), Retrieval-Augmented Generation (RAG), and AI agents.</p>
</li><li><p>Data &amp; ML Engineering: Solid background in data engineering and machine learning engineering. Experience with Databricks is a plus.</p>
</li><li><p>Cloud Proficiency: Extensive experience with cloud platforms, preferably Azure .</p>
</li><li><p>Advanced Python: Expertise in advanced Python programming for developing complex AI models and applications.</p>
</li><li><p>Web Technologies: Proficiency in web development technologies, particularly JavaScript , for building interactive and user-friendly interfaces.</p>
</li><li><p>Performance Optimization: Experience with parallel processing and GPU/CUDA programming is a nice-to-have.</p>
</li><li><p>Master's Degree in Machine Learning, Statistics, Computer Science, Physics, Math, or related field is preferred.</p>
</li><li><p>at least 8 years of software engineering experience, relevant experience with AI with a focus on GenAI.</p>
</li></ul><p>IQVIA is a leading global provider of clinical research services, commercial insights and healthcare intelligence to the life sciences and healthcare industries. We create intelligent connections to accelerate the development and commercialization of innovative medical treatments to help improve patient outcomes and population health worldwide. Learn more at https://jobs.iqvia.com</p>
<p>IQVIA is committed to integrity in our hiring process and maintains a zero tolerance policy for candidate fraud. All information and credentials submitted in your application must be truthful and complete. Any false statements, misrepresentations, or material omissions during the recruitment process will result in immediate disqualification of your application, or termination of employment if discovered later, in accordance with applicable law. We appreciate your honesty and professionalism.</p></div>

Senior Generative AI Engineer (M/W/D)

Nagarro

<div>
 <strong>
  Job Description
  <br/>
  <br/>
 </strong>
 Must have Skills : Python (Strong), Model Packaging &amp; Deployment (Strong), RAG Workflow (Strong), Prompt Engineering with LLMs (Strong)
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 Good To Have Skills : Vector Databases and Embeddings (Capable)
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 Job description: Generative AI developer (experience 1 to 2 years) Early-career AI/ML Engineer supporting the development and deployment of Generative AI solutions (LLMs, RAG systems). Focus on hands-on implementation, integration, and learning-by-delivery, under guidance.
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 Must Have: 1. Strong foundation in Python (data handling, APIs, scripts) 2. Understanding of APIs, Docker, or deployment workflows 3. Exposure to deploying ML/AI models (even in projects/internships) 4. Understanding of embeddings, retrieval flow 5. Hands-on exposure through projects 6. Experience working with GPT/Llama APIs 7. Ability to structure prompts and evaluate outputs 8. Vector Databases (Exposure Level) - Familiarity with FAISS / Chroma / Pinecone and basic usage in projects. 9. . ML Fundamentals: Core concepts - overfitting, evaluation metrics, basic algorithms. Ability to reason about model behavior.
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 Core Responsibilities (Execution Under Guidance) 1. Assist in building RAG-based GenAI solutions for enterprise use cases. 2. Develop Python-based services/APIs integrating LLMs. 3. Support data preprocessing, embeddings, and retrieval pipelines. 4. Contribute to model deployment and integration tasks. 5. Debug and improve existing pipelines under supervision. 6. Work closely with senior engineers to understand production constraints (latency, cost, accuracy)
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 Good to Have 1. LangChain / LlamaIndex exposure 2. Cloud basics (Azure / AWS / GCP) 3. Basic understanding of CI/CD or MLOps concepts 4. Internship/project experience in GenAI or ML use cases Experience &amp; Qualification 1. Bachelor's in Computer Science / Data Science or related field. 2. 12 years of experience in AI/ML (including internships and project work). 3. Exposure to at least one end-to-end ML/GenAI project (academic and professional).
</div>


GENERATIVE-AI ENGINEER (OMANI NATIONAL)

Google

<div><h3 class="jobSectionHeader"><b>Minimum qualifications:</b></h3><ul>
<li>Bachelor&rsquo;s degree or equivalent practical experience.</li>
<li>2 years of experience with software development in one or more programming languages (Python, C++, etc.)</li>
<li>1 year of experience with GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).</li>
<li>Experience developing and launching products leveraging Agent Development Kit (ADK).</li>
<li>Experience architecting and developing solutions on Google Cloud Platform.</li></ul><br>
<h3 class="jobSectionHeader"><b>
Preferred qualifications:</b></h3><ul>
<li>Master's degree or PhD in Computer Science or a related technical field.</li>
<li>Experience developing accessible technologies.</li>
<li>Experience with Deep Learning/Machine Learning.</li>
</ul><div><h2 class="jobSectionHeader"><b>About the job</b></h2>
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google&rsquo;s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. Google Cloud accelerates every organization&rsquo;s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google&rsquo;s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
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US: $147000 - $211000 (USD) + 15% bonus target + equity + benefits<br>
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Learn more about benefits at Google.</div><div><h2 class="jobSectionHeader"><b>
Responsibilities</b></h2><ul>
<li>Implement Generative AI, Large Language Models (LLMs), and Agent-based systems.</li>
<li>Utilize Google Cloud Platform (GCP) and MLOps practices to support system operations.</li>
<li>Contribute to the design of agent architectures, prompts, and interaction models, often in collaboration with senior engineers.</li>
<li>Identify and address potential issues in agent behavior, including error managing, safety, and reliability.<br>
</li>
<li>Implement and optimize agent capabilities, including reasoning loops, RAG, MCP tools, and multi-agent architecture.</li>
</ul></div>Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.</div>

Software Engineer III, AI Agents

Dautom

<div>
 <p>
  The AI Infrastructure Engineer is a platform specialist responsible for architecting, building, and operating high-performance AI infrastructure to support advanced AI workloads, including LLMs, GenAI, Computer Vision, and MLOps. This role will focus on managing GPU clusters (NVIDIA A100/H100), deploying and maintaining Red Hat OpenShift AI (RHODS), and ensuring secure, scalable, and cost-efficient AI platforms across SDD&rsquo;s Sovereign Cloud and hybrid/multi-cloud environments. The engineer will enable enterprise-grade AI adoption for 200+ government entities.
 </p>
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  <br/>
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  Key Responsibilities &amp; Deliverables
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  <br/>
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  GPU &amp; AI Platform Architecture
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  Design and implement GPU-based compute clusters. Define reference architectures for LLM hosting, Vector Databases, MLOps, and high-performance storage/networking.
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  Fully operational GPU-based AI infrastructure. GPU Cluster Uptime and Performance Utilization. Reduction in Cost per Training/Inference Workload.
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  GPU Cluster Operations
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  Install, configure, and optimize core components: CUDA, cuDNN, NCCL, NVIDIA Drivers, and GPU Operators. Implement GPU partitioning, scheduling, and performance tuning for high-end GPUs (e.g., A100/H100).
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  High-availability architecture for all AI workloads. Complete documentation and runbooks.
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  OpenShift AI (RHODS) Management
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  Deploy, configure, and maintain the Red Hat OpenShift AI (RHODS) platform for multi-tenant use. Manage the integration of NVIDIA GPU Operator for efficient GPU scheduling and support Data Scientists with Notebooks, Training, and Inference Endpoints.
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  Production-ready OpenShift AI (RHODS) platform. AI Project Onboarding Speed.
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  LLM &amp; Model Serving
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  Build and manage infrastructure for hosting and serving open-source LLM frameworks (Llama, Falcon, Mistral) and supporting RAG pipelines, LoRA adapters, and Vector Databases (Milvus, pgvector).
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  Multi-model LLM serving environment for entities. MLOps Pipeline Success Rate and Deployment Frequency.
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  MLOps &amp; Automation
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  Implement IaC (Terraform, Ansible) and GitOps for the automated lifecycle management of the AI platform (node onboarding, scaling, model rollout/rollback). Build robust MLOps pipelines for data prep, training, evaluation, and monitoring (using tools like MLflow/Kubeflow).
 </p>
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  Infrastructure automation via Terraform &amp; Ansible. Automation Coverage for AI Infrastructure.
 </p>
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  <br/>
 </p>
 <p>
  Required Qualifications &amp; Experience
 </p>
 <ul>
  <li>
   Experience: 7&ndash;12 years in Cloud Infrastructure, DevOps, ML Infrastructure, or Platform Engineering.
  </li>
  <li>
   Deep Hands-On Expertise:
  </li>
  <li>
   GPU Systems (NVIDIA A100/H100), Linux, Containers, and Kubernetes.
  </li>
  <li>
   OpenShift AI (RHODS) or equivalent Kubernetes GPU orchestration.
  </li>
  <li>
   LLM Hosting (Llama, Mistral, Falcon, etc.) and supporting Vector Databases/RAG systems.
  </li>
  <li>
   Strong Experience In: TensorFlow, PyTorch, Hugging Face, Distributed Training (DDP, Deep Speed), and ML Ops Stacks (ML flow, Kubeflow).
  </li>
 </ul>
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  Essential Skills &amp; Competencies
 </p>
 <ul>
  <li>
   Technical: Deep understanding of GPU compute, HPC architectures, and ML performance profiling. Strong skills in IaC (Terraform/Ansible), CI/CD, and OpenShift/Kubernetes operators.
  </li>
  <li>
   Soft Skills: Strong troubleshooting, optimization, and performance engineering mindset. Excellent cross-functional collaboration and documentation skills.
  </li>
 </ul>
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  Preferred Certifications
 </p>
 <ul>
  <li>
   NVIDIA Deep Learning / AI Infrastructure Certification
  </li>
  <li>
   Red Hat OpenShift AI specialization
  </li>
  <li>
   Kubernetes CKA/CKAD
  </li>
  <li>
   Azure AI or Oracle Cloud AI certifications
  </li>
  <li>
   Terraform &amp; Ansible certifications
  </li>
 </ul>
 <p>
  <br/>
 </p>
</div>


AI Infrastructure Engineer

Capital One

<div>Lead Machine Learning Engineer
<p></p><p>As a Capital One Lead Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing Generative AI and advanced agentic systems at scale. You&rsquo;ll lead the detailed technical design, development, and implementation of core agentic architectures and multi-agent workflows using emerging technologies. You&rsquo;ll focus on system-level architectural design, develop and review complex models and application code, and ensure the high availability, performance, and security of our generative AI applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in generative and agentic machine learning engineering.
</p><p></p><p><b>What you&rsquo;ll do in the role:
</b></p><ul><li><p>Architect Agentic Platforms: Design, develop, and scale core agentic engines and multi-agent workflow solutions, enabling seamless composition of conversational and business automation workflows.
</p></li><li><p>Drive AI Evaluation &amp; Trust: Build and integrate scalable evaluation (Evals) and observability frameworks into solutions to ensure model predictability, performance monitoring, and mitigation of model risk.
</p></li><li><p>Deliver High-Impact Use Cases: Partner with cross-functional product and business teams to deploy production AI solutions, including next-generation consumer AI experiences, intelligent recommendation engines, and advanced conversational assistants.
</p></li><li><p>Enforce Enterprise Guardrails: Ensure all AI/ML applications strictly adhere to robust data privacy standards, regulatory postures, and framework auditability/explainability.
</p></li><li><p>Translate Practical Research: Stay abreast of practical advancements in LLM optimization, retrieval-augmented generation (RAG), and multi-agent design patterns, judiciously applying these novel techniques to production systems.
</p></li><li><p>Technical Leadership &amp; Code Excellence: Provide technical direction, architectural oversight, and rigorous code reviews for engineering teams, fostering a culture of modern engineering excellence.
</p></li></ul><p></p><p><b>Basic Qualifications:
</b></p><ul><li><p>Bachelor&rsquo;s Degree
</p></li><li><p>At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
</p></li><li><p>At least 4 years of experience programming with Python, Scala, or Java
</p></li></ul><p></p><p><b>Preferred Qualifications:
</b></p><ul><li><p>Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
</p></li><li><p>3+ years of experience with GenAI frameworks (e.g., LangChain, LangGraph, LlamaIndex) and Vector Databases
</p></li><li><p>3 years of experience building, scaling, and optimizing Large Language Model (LLM) or GenAI orchestration systems in production
</p></li><li><p>2+ years of experience building automated evaluations (Evals) and observability pipelines for LLMs
</p></li><li><p>3+ years of on-the-job experience with an industry-recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
</p></li><li><p>Experience deploying AI solutions within a strictly regulated environment, incorporating data privacy and model risk governance
</p></li><li><p>Demonstrated ability to lead technical architecture design and provide deep technical guidance to engineering teams
</p></li><li><p>Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
</p></li><li><p>ML industry impact through conference presentations, papers, blog posts, open-source contributions, or patents
</p></li></ul><p></p><p><b><i>At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer).
</i></b></p><p></p><p>The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
</p><p></p>Plano, TX: $179,400 - $204,700 for Lead Machine Learning Engineer<p><br>
</p><p></p><br>
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<p>Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate&rsquo;s offer letter.
</p><p></p>This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.
<p></p><p>Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
</p><p></p>This role is expected to accept applications for a minimum of 5 business days.
<p></p>No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City&rsquo;s Fair Chance Act; Philadelphia&rsquo;s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.
<p></p><p>If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
</p><p></p><p>For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com
</p><p></p><p>Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.
</p><p></p><p>Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).</p></div>

Lead Machine Learning Engineer

Luxoft

<div><div><h5 class="jobSectionHeader"><b>Project description
</b></h5><p>The Principal Full-Stack Architect (.NET &amp; Cloud) is a senior technical leader responsible for defining and driving the architecture of enterprise-scale applications across cloud, backend, frontend, data, DevOps, and AI-enabled solutions. The role combines strategic technology leadership with hands-on technical expertise, ensuring that solutions are scalable, secure, maintainable, and aligned with business objectives
</p></div><div><h5 class="jobSectionHeader"><b>Responsibilities</b></h5>
<div><div><p>Drive the full SDLC for Azure solutions: planning, requirement definition, development, testing, and quality assurance.
</p></div><div><p>Develop, enhance, and integrate modules within core applications used by front-office investment teams.
</p></div><div><p>Design and build containerized applications (Docker, Kubernetes/AKS) and cloud-native data stores (Mongo, SQL).
</p></div><div><p>Develop RESTful APIs, microservices, and event-driven systems (Service Bus, Event Hubs) with strict secure-integration standards (OAuth2/OIDC, managed identities).
</p></div><div><p>Build responsive, cloud-connected front-end interfaces (React/TypeScript; real-time via SignalR).
</p></div><div><p>Design and deliver AI-enabled capabilities: document intelligence, retrieval/search (AI Search), and Azure OpenAI-based workloads over structured and unstructured data.
</p></div><div><p>Configure, maintain, and optimize CI/CD pipelines and infrastructure-as-code (Bicep/ARM, Terraform); enable self-service engineering capabilities.
</p></div><div><p>Implement large-scale data processing and advanced analytics solutions in collaboration with architecture and data teams.
</p></div><div><p>Own incident management: troubleshooting, root cause analysis, and preventive measures.
</p></div><div><p>Partner directly with senior IT and business stakeholders; own the platform narrative, adoption strategy, and delivery roadmap.
</p></div><div><p>Mentor cross-functional teams on cloud-native and AI best practices
</p></div></div></div><div><h5 class="jobSectionHeader"><b>Skills</b></h5>
<p>Must have
</p><div><div><p>Must have:
</p></div><div><p>10+ years overall IT experience with a proven track record of architecting and delivering scalable cloud solutions through all SDLC phases.
</p></div><div><p>10+ years of .NET (C#) development within service-oriented and microservices architectures, including: ASP.NET Core / Web API, Entity Framework Core, async and concurrency patterns, and hands-on migration from .NET Framework to .NET 6/
</p></div><div><p>Production code on a current LTS version is expected; legacy-only (.NET Framework, WebForms) profiles do not qualify.
</p></div><div><p>7+ years hands-on Microsoft Azure: App Services, Functions, Logic Apps, AKS/Container Apps, APIM, Service Bus/Event Hubs, AI Search, AI Services/Azure OpenAI, Storage, Key Vault, Application Insights, Monitor. Skills:
</p></div><div><p>Demonstrated architecture ownership: solution/application architecture defined and delivered end-to-end at enterprise scale, including presenting architectural strategy to senior leadership. Candidates with purely implementation-level experience will not be considered.
</p></div><div><p>5+ years containerization and orchestration (Docker, Kubernetes/AKS) in production.
</p></div><div><p>5+ years Azure DevOps and CI/CD, including infrastructure-as-code (Bicep/ARM mandatory; Terraform preferred).
</p></div><div><p>Practical delivery of AI/GenAI workloads on Azure (Azure OpenAI, AI Search, document intelligence, or equivalent) &mdash; in production, within the last 24 months. Proof-of-concept-only or pre-2024 experience does not qualify.
</p></div><div><p>Deep database expertise: NoSQL (Mongo) and relational SQL, including data access patterns for AI use cases.
</p></div><div><p>Expertise in designing and consuming secure REST APIs; strong understanding of network architecture and authentication protocols (OAuth2/OIDC, managed identities).
</p></div><div><p>Modern front-end delivery: React/TypeScript with real-time capabilities (SignalR).
</p></div><div><p>Experience in regulated environments (financial services / investment management strongly preferred). Required certifications:
</p></div><div><p>Microsoft Certified: Azure Solutions Architect Expert: required. Applications without it are not shortlisted.
</p></div><div><p>Plus at least one of: Azure DevOps Engineer Expert or Azure AI Engineer Associate.
</p></div></div><p>Nice to have
</p><div><div><p>AWS and/or GCP exposure (multi-cloud evaluation experience)
</p></div><div><p>Terraform in production
</p></div><div><p>Python
</p></div><div><p>Databricks (recent, Unity Catalog-era experience) or comparable data/AI platforms; Palantir exposure
</p></div><div><p>Azure Security Engineer Associate
</p></div><div><p>Experience establishing self-service platforms for citizen developers
</p></div></div></div><div><h5 class="jobSectionHeader"><b>Other</b></h5>
<div><div><div><p>Languages</p>
<p>English: C2 Proficient
</p></div></div><div><div><p>Seniority</p>
<p>Lead</p></div></div></div></div></div>
<br><div><div><div><p>Abu Dhabi, United Arab Emirates
</p></div><p>Req. VR-123799
</p></div><div></div><div><div><p>C#/VB.NET
</p></div><div><p>BCM Industry</p>
</div><div><p>07/07/2026
</p></div><div><p>Req. VR-123799</p></div></div></div><p></p>

Principal Full-Stack Architect (.NET & Cloud)

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