infrastructure engineering manager (developer experience manager)

ai engineer, light

full stack engineer, gisacc Jobs in Cairo

full stack engineer, gisacc

infrastructure engineering manager (developer experience manager) Jobs in Cairo

machine learning engineer

system engineer, light

mlops engineer

<div>
 
 
 Company Overview:
 
 
 
 
 
 
 Dar, the founding member of the Sidara group, is an international multidisciplinary consulting organization specializing in engineering, architecture, planning, environment, project management, facilities management, and economics. Sidara operates in 60 countries with 20,500 professionals, Dar connects people, places, and communities through innovative solutions to the world's most complex challenges. We deliver projects from inception through completion, embracing challenges to empower communities worldwide. Learn more at
 
 www.dar.com
 
 .
 
 
 
 
 
 
 Our Vision and Values:
 
 
 
 
 
 
 We aspire to be the chosen home of those with a gift for crafting solutions that empower people and an unwavering passion for learning and innovation. Our core values shape our culture and guide our decision-making. We are committed to:
 
 
 
 
 <ul>
 <li>
 Excellence
 </li>
 <li>
 Responsibility
 </li>
 <li>
 Empowerment
 </li>
 <li>
 Connectivity
 </li>
 <li>
 Courage
 </li>
 </ul>
 
 
 
 
 
 Job Purpose:
 
 
 
 
 
 
 The MLOps Engineer will streamline and automate the deployment, monitoring, and scaling of machine learning models in production. This role bridges the gap between data science and operations to ensure reliable, efficient, and continuous model delivery within the AEC technology ecosystem.
 
 
 
 
 
 
 Responsibilities:
 
 
 
 
 
 
 The role includes, but is not limited to:
 
 <ul>
 <li>
 Maintain ML pipelines, including data and model versioning, for re-training, testing, and deployment.
 </li>
 <li>
 Implement automated CI/CD workflows for machine learning models and data pipelines.
 </li>
 <li>
 Monitor model performance using KPIs and metrics, ensuring system reliability in production.
 </li>
 <li>
 Manage infrastructure, version control, and model reproducibility using cloud, on-prem, and containerization tools.
 </li>
 <li>
 Integrate MLOps modules into the existing AI platform to enhance operational efficiency.
 </li>
 <li>
 Optimize cost, performance, and scalability of ML environments.
 </li>
 <li>
 Collaborate with data scientists, AI engineers, computational designers, and design engineers to embed advanced digital solutions into enterprise workflows.
 </li>
 <li>
 Stay current with MLOps trends and recommend improvements to existing systems and processes.
 </li>
 <li>
 Support project planning, documentation, and presentations.
 </li>
 <li>
 Mentor colleagues and contribute to knowledge sharing within the team.
 </li>
 </ul>
 
 
 
 
 
 Values Alignment
 
 
 <ul>
 <li>
 Demonstrates analytical thinking, independence, professionalism, transparency, and accountability.
 </li>
 <li>
 Communicates effectively and fosters collaboration across cross-functional teams.
 </li>
 <li>
 Upholds quality outcomes while embodying the organization&rsquo;s core values in all actions.
 </li>
 </ul>
 
 
 
 
 
 Requirements:
 
 
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or master&rsquo;s in computer science, Software Engineering, Artificial Intelligence, Data Engineering, or a related field.
 </li>
 <li>
 1&ndash;3 years of professional experience in at least three of the following: ML deployment, automated CI/CD, ML monitoring, machine learning, deep learning, LLMs, NLP, reinforcement learning, or computer vision.
 </li>
 <li>
 Strong proficiency in Python, C#/C++, and SQL.
 </li>
 <li>
 Experience with cloud platforms (AWS, Azure, or GCP).
 </li>
 <li>
 Hands-on experience with ML frameworks (TensorFlow, PyTorch) and MLOps tools (MLflow, Docker, Kubernetes).
 </li>
 <li>
 Knowledge of CI/CD systems, data versioning, and monitoring tools.
 </li>
 <li>
 Excellent problem-solving, collaboration, software development, and automation skills.
 </li>
 <li>
 Familiarity with AEC tools (Revit, Rhino, AutoCAD, Navisworks) is a plus.
 </li>
 </ul>
 
 
 
 
 
 
 Kind Note
 
 :
 
 
 
 
 
 
 
 * While we carefully review all applications, only candidates meeting the specified requirements will be contacted for further consideration. We appreciate your understanding and thank all applicants for their interest.
 
 
 
 
</div>

MLOps Engineer

HireOn

<div>
 
 Senior MLOps Engineer (Onsite | Cairo, Egypt)
 
 
 
 
 Experience:
 
 5&ndash;8 Years
 
 
 
 Employment Type:
 
 Full-Time
 
 
 
 Location:
 
 Cairo, Egypt (Onsite)
 
 
 
 Salary Range:
 
 80,000 EGP &ndash; 100,000 EGP per month
 
 
 
 About The Opportunity
 
 
 
 HireOn is recruiting for its reputed international client seeking a highly skilled
 
 Senior MLOps Engineer
 
 to lead the operationalization of machine learning models in production environments.
 
 
 This role requires a hands-on expert who can bridge Data Science and Cloud Engineering teams, ensuring scalable, secure, and automated ML systems on AWS infrastructure. The ideal candidate will drive end-to-end MLOps architecture, CI/CD automation, and cloud-native ML deployment strategies.
 
 
 
 Core Requirements Experience
 
 
 
 <ul>
 <li>
 5&ndash;8 years of overall experience with minimum 3+ years in MLOps or production ML environments
 </li>
 <li>
 Strong experience managing the full ML lifecycle (training, deployment, monitoring, optimization)
 </li>
 <li>
 Proven ability to work independently and collaborate across cross-functional teams
 
 
 </li>
 </ul>
 
 AWS &amp; Cloud Expertise (Mandatory)
 
 
 
 
 Hands-on Experience With
 
 
 
 Amazon SageMaker, S3, EC2, Lambda, IAM, CloudWatch, ECR, ECS, EKS
 
 
 Strong understanding of secure, scalable, and highly available AWS architecture
 
 
 
 MLOps &amp; Machine Learning
 
 
 
 <ul>
 <li>
 Model deployment and monitoring in production
 </li>
 <li>
 Experience with TensorFlow, PyTorch, or Scikit-learn
 </li>
 <li>
 Experiment tracking tools such as MLflow
 </li>
 <li>
 Model performance monitoring and drift detection
 
 
 </li>
 </ul>
 
 DevOps &amp; Automation
 
 
 
 <ul>
 <li>
 Docker and containerization
 </li>
 <li>
 CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, or AWS CodePipeline
 </li>
 <li>
 Infrastructure as Code (Terraform or CloudFormation)
 
 
 </li>
 </ul>
 
 Programming &amp; Data
 
 
 
 <ul>
 <li>
 Strong Python programming expertise
 </li>
 <li>
 Experience with SQL and working knowledge of NoSQL databases
 </li>
 <li>
 Experience handling structured and unstructured datasets
 
 
 </li>
 </ul>
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Design and implement scalable end-to-end MLOps pipelines
 </li>
 <li>
 Deploy and manage ML models using AWS-native services
 </li>
 <li>
 Build and maintain CI/CD pipelines for ML workflows
 </li>
 <li>
 Implement model monitoring, logging, and performance tracking
 </li>
 <li>
 Containerize ML applications and deploy on ECS/EKS
 </li>
 <li>
 Automate infrastructure using Terraform or CloudFormation
 </li>
 <li>
 Ensure system scalability, reliability, and security
 </li>
 <li>
 Troubleshoot ML pipelines and cloud infrastructure issues
 </li>
 <li>
 Collaborate closely with Data Science and Engineering teams to productionize ML solutions
 
 
 </li>
 </ul>
 
 Nice to Have
 
 
 
 <ul>
 <li>
 Exposure to feature stores and data versioning
 </li>
 <li>
 AWS Associate-level certification
 </li>
 <li>
 Understanding of ML governance, compliance, and model risk management
 
 
 </li>
 </ul>
 Skills: aws codepipeline,terraform,tensorflow,aws sagemaker,mlops,python,aws,ml
</div>

Senior MLOps Engineer

NorthBay Solutions

<div>
 
 
 Senior MLOps Engineer &ndash; AWS
 
 
 
 
 Experience:
 
 5&ndash;7 Years
 
 
 Location:
 
 &nbsp;Cairo, Egypt (Onsite Role)
 
 
 Employment Type:
 
 Full-Time
 
 
 
 Job Summary
 
 
 
 We are looking for a skilled and hands-on
 
 Senior MLOps Engineer
 
 with strong AWS expertise to support the deployment, automation, and monitoring of machine learning models in production. The ideal candidate will collaborate closely with Data Science and Engineering teams to operationalize ML models using cloud-native best practices.
 
 
 
 Key Responsibilities
 
 
 <ul>
 <li>
 Design and implement end-to-end MLOps pipelines from data ingestion to model deployment
 </li>
 <li>
 Deploy and manage ML models using AWS-native services such as SageMaker
 </li>
 <li>
 Build and maintain CI/CD pipelines for ML workflows
 </li>
 <li>
 Implement model monitoring, performance tracking, and basic drift detection
 </li>
 <li>
 Containerize ML workloads using Docker and deploy on EKS/ECS
 </li>
 <li>
 Support infrastructure automation using Terraform or CloudFormation
 </li>
 <li>
 Ensure scalability, availability, and security of ML systems
 </li>
 <li>
 Collaborate with cross-functional teams to productionize ML solutions
 </li>
 <li>
 Troubleshoot ML pipelines and cloud infrastructure issues
 </li>
 </ul>
 
 
 Required Skills &amp; Qualifications
 
 
 
 
 MLOps &amp; Machine Learning
 
 
 <ul>
 <li>
 5&ndash;7 years of overall experience with at least 3+ years in MLOps or ML production environments
 </li>
 <li>
 Experience managing ML lifecycle (training, deployment, monitoring)
 </li>
 <li>
 Hands-on experience with TensorFlow, PyTorch, or Scikit-learn
 </li>
 <li>
 Experience with MLflow or similar experiment tracking tools
 </li>
 </ul>
 
 AWS Cloud (Mandatory)
 
 <ul>
 <li>
 Hands-on experience with:
 <ul>
 <li>
 Amazon SageMaker
 </li>
 <li>
 S3, EC2, Lambda
 </li>
 <li>
 IAM, CloudWatch
 </li>
 <li>
 ECR, ECS or EKS
 </li>
 </ul>
 </li>
 <li>
 Understanding of secure and scalable AWS architecture
 </li>
 </ul>
 
 DevOps &amp; Automation
 
 <ul>
 <li>
 Docker and containerization
 </li>
 <li>
 CI/CD using GitHub Actions, GitLab CI, Jenkins, or AWS CodePipeline
 </li>
 <li>
 Infrastructure as Code (Terraform or CloudFormation)
 </li>
 </ul>
 
 Programming &amp; Data
 
 <ul>
 <li>
 Strong Python programming skills
 </li>
 <li>
 Experience with SQL and working knowledge of NoSQL databases
 </li>
 <li>
 Experience handling structured and unstructured datasets
 </li>
 </ul>
 
 
 Good to Have
 
 
 <ul>
 <li>
 Exposure to feature stores and data versioning
 </li>
 <li>
 AWS Associate-level certification
 </li>
 <li>
 Basic understanding of ML governance and compliance
 </li>
 </ul>
</div>

Senior MLOps Engineer – AWS

Capgemini Invent

<div>
 At Capgemini Invent, we believe difference drives change. As inventive transformation consultants, we blend our strategic, creative and scientific capabilities, collaborating closely with clients to deliver cutting-edge solutions. Join us to drive transformation tailored to our client's challenges of today and tomorrow. Informed and validated by science and data. Superpowered by creativity and design. All underpinned by technology created with purpose.
 
 
 
 Job Description
 
 
 
 
 Machine Learning &amp; AI Solutions
 
 
 
 <ul>
 <li>
 Deploy advanced machine learning models and AI-driven solutions across cloud environments.
 </li>
 <li>
 Lead the end-to-end lifecycle of ML models, from data ingestion and feature engineering to model training, evaluation, and deployment.
 </li>
 <li>
 Monitor model performance and implement retraining strategies to ensure long-term accuracy and relevance.
 
 
 </li>
 </ul>
 
 Cloud &amp; Data Engineering
 
 
 
 <ul>
 <li>
 Architect and optimize scalable data pipelines and cloud-based infrastructures (Azure, AWS, or GCP) to support ML workloads.
 </li>
 <li>
 Collaborate with data engineers to ensure robust data preprocessing, transformation, and storage strategies.
 
 
 </li>
 </ul>
 
 MLOps &amp; Automation
 
 
 
 <ul>
 <li>
 Develop and maintain CI/CD pipelines tailored for ML workflows, including automated testing, model versioning, and deployment.
 </li>
 <li>
 Integrate monitoring and alerting systems to track model drift, data quality, and system performance.
 
 
 </li>
 </ul>
 
 Collaboration &amp; Innovation
 
 
 
 <ul>
 <li>
 Work closely with cross-functional teams including data scientists, product managers, and business stakeholders to translate business needs into technical solutions.
 </li>
 <li>
 Mentor junior engineers and contribute to knowledge sharing and best practices across the team.
 
 
 </li>
 </ul>
 
 Technical Skills
 
 
 
 <ul>
 <li>
 Strong proficiency in Python and ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
 </li>
 <li>
 Strong command of SQL and experience with large-scale data processing tools (e.g., Spark, Pandas).
 </li>
 <li>
 Deep understanding of cloud platforms (Azure, AWS, GCP) and their ML services.
 </li>
 <li>
 Skilled in MLOps practices, including CI/CD, containerization (Docker), and orchestration (Kubernetes).
 </li>
 <li>
 Familiarity with Git and collaborative development workflows.
 
 
 </li>
 </ul>
 
 Experience
 
 
 
 <ul>
 <li>
 6+ years of experience working with complex datasets and solving real-world business problems using AI
 </li>
 <li>
 Proven track record of deploying and maintaining ML models in production environments.
 
 
 </li>
 </ul>
 
 Communication Skills
 
 
 
 <ul>
 <li>
 Excellent written and verbal communication skills to collaborate effectively with cross-functional teams.
 </li>
 <li>
 Fluent in Qatari Arabic &amp; English, both spoken and written.
 
 
 </li>
 </ul>
 
 Additional Skills
 
 
 
 <ul>
 <li>
 Strong problem-solving abilities and attention to detail.
 </li>
 <li>
 Ability to work collaboratively in a team environment.
 </li>
 <li>
 Self-motivated with a continuous learning mindset.
 </li>
 <li>
 Team management.
 
 
 </li>
 </ul>
 Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.
</div>

Senior Data Scientist

FlairsTech

<div>
 
 
 About the Role
 
 
 
 
 
 
 We are hiring an elite cohort of highly hands-on AI Engineers to embed directly within Valsoft's Edelweiss Software Group portfolio of vertical market B2B SaaS companies.
 
 
 We are looking for builders &mdash; engineers who operate as system orchestrators rather than traditional syntax typists. You will design, implement, and deploy secure, enterprise-grade AI systems that automate workflows, eliminate manual processes, rapidly modernize massive legacy codebases, and create measurable business impact.
 
 
 You will work directly with operators, product teams, and leadership to turn inefficiencies into intelligent systems. Operating with a distinct startup mindset within a large-scale corporate portfolio, your primary focus is driving rapid product innovation, executing AI-driven legacy modernization via agentic swarm coding, and maximizing ROI through autonomous multi-agent workflows.
 
 
 If you enjoy owning problems end-to-end, possess a relentless bias toward action, and thrive on shipping real systems into production at an unprecedented velocity, this role is for you.
 
 
 Your mission: act as an organizational force multiplier to increase efficiency, reduce CAC payback periods, increase Net Revenue Retention (NRR), and unlock massive scalability.
 
 
 What You&rsquo;ll Do
 
 
 Build &amp; Ship AI Systems
 
 <ul>
 <li>
 Design, develop, and deploy production-grade, secure AI systems utilizing scalable polyglot microservices.
 </li>
 <li>
 Integrate state-of-the-art enterprise LLMs (Anthropic Claude Opus 4.6, OpenAI GPT-5.2, Google Gemini 3) into SaaS platforms via dynamic, fault-tolerant API routing gateways.
 </li>
 <li>
 Modernize extensive codebases using agentic swarm coding, automated refactoring tools, and cross-language translation.
 </li>
 <li>
 Develop autonomous multi-agent workflows utilizing parallel orchestration tools like the OpenAI Codex and Claude Code CLI.
 </li>
 <li>
 Automate comprehensive technical documentation for legacy and newly generated codebases using AI orchestration.
 </li>
 <li>
 Rapidly prototype &rarr; validate via adversarial testing &rarr; deploy &rarr; iterate.
 </li>
 </ul>
 
 Architect AI Infrastructure
 
 <ul>
 <li>
 Apply good knowledge of software engineering design patterns and architecture to ensure the scalability and maintainability of AI-generated systems.
 </li>
 <li>
 Implement complex RAG systems and continually optimize trade-offs between massive-context hydration (up to 1-million tokens) and multi-stage semantic retrieval.
 </li>
 <li>
 Work extensively with vector databases (Pinecone, Weaviate, FAISS) and manage persistent document embedding queues.
 </li>
 <li>
 Build polyglot microservices specifically capable of handling massive, long-running operational tasks and streaming continuous token generation via persistent connection protocols (WebSockets and Server-Sent Events).
 </li>
 <li>
 Ensure SOC2 compliance, data residency (via inference_geo parameters), and deterministic execution through policy-as-code agentic governance.
 </li>
 <li>
 Deploy and scale models strictly within secure managed cloud boundaries (Azure AI, AWS Bedrock, GCP Vertex) leveraging Docker and Kubernetes orchestration.
 </li>
 </ul>
 
 Transform the Application Lifecycle &amp; Collaborate
 
 <ul>
 <li>
 Architect "zero-touch deployment" CI/CD pipelines fortified with adversarial gating.
 </li>
 <li>
 Design and implement unit test and benchmark automation workflows, utilizing AI agentic coding to rigorously validate and test produced code.
 </li>
 <li>
 Natively integrate synthetic red teaming into CI/CD to actively prevent prompt drift, reward hacking, and logic degradation.
 </li>
 <li>
 Work directly with non-technical stakeholders to translate ambiguous business problems into secure, scalable AI solutions.
 </li>
 <li>
 Optimize Atlassian workflows (Jira) to automate the translation of unstructured product requirements into structured prompt contexts for AI agents.
 </li>
 </ul>
 
 Required Technical Skills
 
 
 Core Engineering
 
 <ul>
 <li>
 5+ years of demonstrable enterprise software development experience.
 </li>
 <li>
 Good knowledge of software engineering design patterns and architecture.
 </li>
 <li>
 Flexible in most popular programming languages (Python, Javascript, Typescript, .Net, C#) and web frameworks (NextJS, etc.).
 </li>
 <li>
 Ability to switch environments and programming using agentic programming tools to validate and test your produced code.
 </li>
 <li>
 Backend development experience designing polyglot APIs, decoupled asynchronous microservices, and utilizing persistent connection protocols (WebSockets/SSE).
 </li>
 <li>
 Familiarity with containerization (Docker, Kubernetes) and architecting multi-region, fault-tolerant cloud infrastructure (AWS, Azure, or GCP).
 </li>
 </ul>
 
 AI &amp; ML Systems
 
 <ul>
 <li>
 Enterprise LLM integration and dynamic API routing (OpenAI, Anthropic, Gemini, DeepSeek).
 </li>
 <li>
 Multi-agent orchestration and advanced CLI tooling (Claude Code with Auto Hot-Reload and Forking Context, OpenAI Codex).
 </li>
 <li>
 Mastery of localized AI IDE layers (Cursor for repository-wide reasoning, GitHub Copilot).
 </li>
 <li>
 Massive-context RAG pipelines, dynamic chunking strategies, and vector databases (Pinecone, Weaviate, FAISS).
 </li>
 <li>
 Custom evaluations (LangSmith), structured outputs, and managed fine-tuning in secure cloud boundaries.
 </li>
 </ul>
 
 MLOps &amp; Process Automation
 
 <ul>
 <li>
 Zero-touch CI/CD pipelines and advanced Git workflows (including Git worktree isolation for autonomous sub-agents).
 </li>
 <li>
 Unit test and benchmark automation frameworks fully integrated with AI-driven testing.
 </li>
 <li>
 Adversarial LLM testing and automated synthetic red-teaming integration.
 </li>
 <li>
 Monitoring dynamic access limits and real-time atomic token/credit consumption tracking.
 </li>
 <li>
 Strict hallucination mitigation, enterprise guardrails, and deterministic policy-as-code execution.
 </li>
 </ul>
 
 Profile We&rsquo;re Looking For
 
 <ul>
 <li>
 Highly hands-on &mdash; you ship, not just design. You operate at an unprecedented velocity by orchestrating advanced autonomous tools.
 </li>
 <li>
 Entrepreneurial startup mindset &mdash; you see legacy inefficiencies and act with a relentless bias toward rapid action.
 </li>
 <li>
 Comfortable with ambiguity and translating complex business needs into precise technical execution.
 </li>
 <li>
 Business-oriented &mdash; you care deeply about ROI, operational leverage, and driving specific financial metrics (CAC, NRR).
 </li>
 <li>
 Fast learner &mdash; keeps up with real-time shifts in AI ecosystems, hardware architectures (e.g., Cerebras chips), and vital API migrations.
 </li>
 <li>
 Pragmatic &mdash; uses the right secure enterprise tool for the specific bottleneck, eschewing the fanciest public tool if it compromises stability or compliance.
 </li>
 </ul>
 
 
</div>

Senior AI Engineer

700apps

<div>
 We are looking for a highly skilled
 
 Senior AI Engineer / AI Architect
 
 to lead the design, development, and deployment of scalable AI solutions.
 
 
 In this role, you will combine hands-on model development with high-level system architecture to help shape our AI strategy and deliver production-ready intelligent systems.
 
 
 You will work closely with engineering, product, and leadership teams to transform business needs into powerful AI-driven solutions.
 
 
 
 Key Responsibilities
 
 
 
 AI Architecture &amp; System Design
 
 
 <ul>
 <li>
 Design end-to-end AI/ML system architecture from data ingestion to deployment and monitoring.
 </li>
 <li>
 Define scalable and reliable ML pipelines.
 </li>
 <li>
 Select the appropriate tools, frameworks, and infrastructure.
 </li>
 <li>
 Ensure performance, security, scalability, and maintainability of AI systems.
 </li>
 <li>
 Design APIs and AI services for integration with products and platforms.
 
 
 </li>
 </ul>
 Model Development
 
 
 <ul>
 <li>
 Develop, train, and fine-tune machine learning and deep learning models.
 </li>
 <li>
 Work on advanced AI solutions such as LLMs, NLP systems, or computer vision models.
 </li>
 <li>
 Optimize models for accuracy, speed, and cost efficiency.
 </li>
 <li>
 Conduct experiments and evaluate model performance using best practices.
 
 
 </li>
 </ul>
 MLOps &amp; Deployment
 
 
 <ul>
 <li>
 Build and maintain CI/CD pipelines for machine learning workflows.
 </li>
 <li>
 Deploy models to production using containers and cloud services.
 </li>
 <li>
 Implement monitoring, logging, and automated retraining processes.
 </li>
 <li>
 Manage model lifecycle, versioning, and performance tracking.
 
 
 </li>
 </ul>
 Technical Leadership
 
 
 <ul>
 <li>
 Provide technical leadership to AI engineers and data scientists.
 </li>
 <li>
 Review code and guide best practices in AI development.
 </li>
 <li>
 Collaborate with cross-functional teams to align AI solutions with business goals.
 </li>
 <li>
 Contribute to technical strategy and AI roadmap planning.
 
 
 </li>
 </ul>
 
 Required Qualifications
 
 
 
 <ul>
 <li>
 7+ years of experience in AI / Machine Learning engineering.
 </li>
 <li>
 Strong programming skills in Python.
 </li>
 <li>
 Hands-on experience with PyTorch or TensorFlow.
 </li>
 <li>
 Experience working with LLMs and modern AI frameworks.
 </li>
 <li>
 Strong understanding of system design and scalable architectures.
 </li>
 <li>
 Experience with cloud platforms such as AWS, GCP, or Azure.
 </li>
 <li>
 Experience with Docker and Kubernetes.
 </li>
 <li>
 Familiarity with MLOps tools such as MLflow, Airflow, or similar.
 </li>
 <li>
 Solid understanding of data pipelines and data engineering concepts.
 </li>
 </ul>
</div>

AI Architect

Müller's Solutions

<div>
 M&uuml;ller's Solutions is excited to announce an opening for a Google Cloud AI Specialist to drive our artificial intelligence initiatives leveraging Google Cloud Platform (GCP). In this role, you will be responsible for designing and implementing AI and machine learning solutions to help transform our data into actionable insights and enhance decision-making processes across the organization.
 
 
 
 Key Responsibilities:
 
 
 
 <ul>
 <li>
 Develop and deploy machine learning models using GCP tools such as AI Platform, TensorFlow, and AutoML
 </li>
 <li>
 Collaborate with cross-functional teams to identify business needs and translate them into AI and ML project requirements
 </li>
 <li>
 Conduct data analysis to prepare datasets for model training and evaluation while ensuring data quality and integrity
 </li>
 <li>
 Implement and optimize AI workflows that facilitate real-time data processing and inference
 </li>
 <li>
 Stay updated on the latest advancements in AI and machine learning technologies to continuously improve our offerings
 </li>
 <li>
 Provide technical guidance and support to teams on best practices for using Google Cloud AI services
 </li>
 <li>
 Document AI project implementations, model performance evaluations, and ongoing maintenance procedures
 </li>
 <li>
 Contribute to developing educational materials and training sessions on AI capabilities for staff and stakeholders
 </li>
 <li>
 Monitor and assess the performance of deployed AI models, making adjustments as necessary to ensure effectiveness
 
 
 </li>
 </ul>
 
 Requirements
 
 
 
 
 Requirements:
 
 
 
 <ul>
 <li>
 Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, or a related field
 </li>
 <li>
 3+ years of experience in machine learning or AI solutions development, preferably using Google Cloud Platform
 </li>
 <li>
 Proficient in programming languages such as Python or R, with a strong understanding of machine learning libraries (e.g., TensorFlow, scikit-learn)
 </li>
 <li>
 Experience with cloud-based AI technologies, especially GCP services like Vision AI, Natural Language API, and AutoML
 </li>
 <li>
 Strong knowledge of data preprocessing, feature engineering, and model evaluation techniques
 </li>
 <li>
 Ability to work with large datasets, utilizing BigQuery or similar data processing tools
 </li>
 <li>
 Excellent problem-solving skills and a strong analytical mindset
 </li>
 <li>
 Capable of communicating complex technical concepts to diverse audiences effectively
 
 
 </li>
 </ul>
 
 Preferred Qualifications:
 
 
 
 <ul>
 <li>
 Google Cloud certifications (e.g., Google Cloud Professional Data Engineer or Google Cloud Professional Machine Learning Engineer) are a plus
 </li>
 <li>
 Experience with MLOps practices and tools for model deployment and management
 </li>
 <li>
 Familiarity with DevOps practices as they relate to AI and machine learning
 </li>
 <li>
 A background in statistics or applied mathematics is desirable
 
 
 </li>
 </ul>
 
 Benefits
 
 
 
 1- Attractive Package.
 
 
 2- Family Benefits.
 
 
 3- Visa.
 
 
 4-Air Tickets.
</div>

Google Cloud AI Specialist

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