devops engineer

senior network engineer

ai platform engineer

aws devops senior

data engineer

senior cloud engineer aws professional services

ZainTECH

<div>
 The Senior Cloud Engineer - AWS plays a key role within the Professional Services team by designing and delivering complex, enterprise-grade AWS solutions. This role combines deep technical expertise with strong leadership and client-facing capabilities.
 
 
 In addition to hands-on technical delivery, you will actively participate in client discussions, translating business requirements into actionable designs, and present recommendations to technical and non- technical stakeholders. Your work will directly impact the success of strategic cloud projects, helping clients maximize the value and resilience of their AWS environments
 
 
 Responsibilities:
 
 
 <ul>
 <li>
 Design and implement complex and scalable AWS architectures across IaaS, PaaS, and SaaS services
 </li>
 <li>
 Lead technical workstreams within large, multi-phased migration or modernization projects
 </li>
 <li>
 Conduct AWS Well-Architected Reviews and provide recommendations to improve clients' cloud environments
 </li>
 <li>
 Design and implement secure networking architectures on AWS, ensuring high availability and compliance
 </li>
 <li>
 Develop infrastructure as code using CloudFormation, Terraform, and scripting languages like Python
 </li>
 <li>
 Lead or support migrations to AWS using native migration services, tools, and best practices
 </li>
 <li>
 Troubleshoot complex technical issues across compute, networking, databases, and storage
 </li>
 <li>
 Collaborate with cross-functional teams and present technical solutions to internal and client stakeholders
 </li>
 <li>
 Provide technical mentorship to mid-level engineers, promoting best practices in design and delivery
 </li>
 <li>
 Document technical designs, implementation plans, and operational runbooks
 </li>
 <li>
 Support DevOps initiatives, CI/CD pipelines, and automation to increase efficiency and consistency
 
 
 </li>
 </ul>
 
 Requirements
 
 
 
 <ul>
 <li>
 4-6 years of experience in cloud infrastructure and systems engineering, 3 years hands-on experience with AWS.
 </li>
 <li>
 Strong hands-on experience in AWS migration tools and frameworks
 </li>
 <li>
 Experience with AWS database migrations
 </li>
 <li>
 Strong knowledge of AWS networking, compute, storage, databases, IAM, and high availability design
 </li>
 <li>
 Good understanding of AWS disaster recovery, backup strategies, and cross-region deployments
 </li>
 <li>
 Solid Linux and Windows Server administration skills
 </li>
 <li>
 Working knowledge of Docker and Kubernetes for deploying and managing containerized workloads
 </li>
 <li>
 Experience in multi-cloud environments (AWS, Azure, or GCP) and hybrid cloud setups
 </li>
 <li>
 Excellent communication and client interaction skills
 </li>
 <li>
 AWS Architect Professional certification preferred
 </li>
 <li>
 Knowledge of Terraform, AWS CLI, and automation scripting (Shell, Python, PowerShell)
 </li>
 </ul>
</div>

Senior Cloud Engineer - AWS (Professional Services)

<div>
 The Senior Cloud Engineer &ndash; AWS plays a key role within the Professional Services team by designing and delivering complex, enterprise-grade AWS solutions. This role combines deep technical expertise with strong leadership and client-facing capabilities.
 
 
 In addition to hands-on technical delivery, you will actively participate in client discussions, translating business requirements into actionable designs, and present recommendations to technical and non- technical stakeholders. Your work will directly impact the success of strategic cloud projects, helping clients maximize the value and resilience of their AWS environments
 
 
 Responsibilities:
 
 
 <ul>
 <li>
 Design and implement complex and scalable AWS architectures across IaaS, PaaS, and SaaS services
 </li>
 <li>
 Lead technical workstreams within large, multi-phased migration or modernization projects
 </li>
 <li>
 Conduct AWS Well-Architected Reviews and provide recommendations to improve clients' cloud environments
 </li>
 <li>
 Design and implement secure networking architectures on AWS, ensuring high availability and compliance
 </li>
 <li>
 Develop infrastructure as code using CloudFormation, Terraform, and scripting languages like Python
 </li>
 <li>
 Lead or support migrations to AWS using native migration services, tools, and best practices
 </li>
 <li>
 Troubleshoot complex technical issues across compute, networking, databases, and storage
 </li>
 <li>
 Collaborate with cross-functional teams and present technical solutions to internal and client stakeholders
 </li>
 <li>
 Provide technical mentorship to mid-level engineers, promoting best practices in design and delivery
 </li>
 <li>
 Document technical designs, implementation plans, and operational runbooks
 </li>
 <li>
 Support DevOps initiatives, CI/CD pipelines, and automation to increase efficiency and consistency
 
 
 </li>
 </ul>
 
 Requirements
 
 
 
 <ul>
 <li>
 4&ndash;6 years of experience in cloud infrastructure and systems engineering, 3 years hands-on experience with AWS.
 </li>
 <li>
 Strong hands-on experience in AWS migration tools and frameworks
 </li>
 <li>
 Experience with AWS database migrations
 </li>
 <li>
 Strong knowledge of AWS networking, compute, storage, databases, IAM, and high availability design
 </li>
 <li>
 Good understanding of AWS disaster recovery, backup strategies, and cross-region deployments
 </li>
 <li>
 Solid Linux and Windows Server administration skills
 </li>
 <li>
 Working knowledge of Docker and Kubernetes for deploying and managing containerized workloads
 </li>
 <li>
 Experience in multi-cloud environments (AWS, Azure, or GCP) and hybrid cloud setups
 </li>
 <li>
 Excellent communication and client interaction skills
 </li>
 <li>
 AWS Architect Professional certification preferred
 </li>
 <li>
 Knowledge of Terraform, AWS CLI, and automation scripting (Shell, Python, PowerShell)
 </li>
 </ul>
</div>

Siemens

<div>
 
 About Brightly Software
 
 
 
 Brightly Software is a leader in intelligent asset management and operational optimization, empowering organizations with data‑driven insights. As we expand our AI and ML capabilities, we are seeking a Senior MLOps Engineer to build and scale the infrastructure that powers our next generation of predictive and autonomous solutions.
 
 
 
 Role Overview
 
 
 
 As a Senior MLOps Engineer, you will architect, develop, and operate end‑to‑end machine learning infrastructure on AWS. You will work at the intersection of ML engineering, cloud infrastructure, and developer productivity&mdash;enabling Brightly's data science teams to move seamlessly from experimentation to reliable, secure, and cost‑efficient production systems.
 
 
 Your work will ensure that ML models and data pipelines are scalable, observable, and compliant with best‑in‑class MLOps practices.
 
 
 
 Key Responsibilities
 
 
 
 
 ML Platform &amp; Infrastructure (AWS‑focused)
 
 
 
 <ul>
 <li>
 Design, build, and operate ML/AI development platforms on AWS, leveraging services such as Amazon SageMaker (Studio, Training, Real‑Time &amp; Async Inference, Pipelines, Feature Store), S3, Glue, Lambda, ECS/EKS, and related cloud infrastructure.
 </li>
 <li>
 Implement infrastructure‑as‑code using Terraform or equivalent, and manage workflow orchestration using AWS Step Functions or Airflow.
 
 
 </li>
 </ul>
 
 Data &amp; Model Pipelines
 
 
 
 <ul>
 <li>
 Build automated data ingestion and transformation pipelines using S3, Glue, EMR/Spark, and Redshift, incorporating data quality and lineage tooling (e.g., Great Expectations, Deequ).
 
 
 </li>
 </ul>
 
 CI/CD for Machine Learning
 
 
 
 <ul>
 <li>
 Develop CI/CD pipelines for ML with CodeBuild, CodePipeline, or GitHub Actions, integrating unit tests, data contract checks, model validation, canary/shadow deployments, and automated rollback strategies.
 
 
 </li>
 </ul>
 
 Model Deployment &amp; Operations
 
 
 
 <ul>
 <li>
 Deploy real‑time inference endpoints (SageMaker endpoints or FastAPI‑based services on Lambda/ECS/EKS) and scalable batch processing jobs.
 </li>
 <li>
 Define SLOs, implement autoscaling, and drive cost/performance optimizations across ML workloads.
 
 
 </li>
 </ul>
 
 Monitoring, Observability &amp; Governance
 
 
 
 <ul>
 <li>
 Implement production monitoring for drift, bias, and performance using SageMaker Model Monitor and service telemetry tools like CloudWatch, Prometheus, and Grafana.
 </li>
 <li>
 Enforce security and governance best practices, including least‑privilege IAM, VPC‑isolated architectures, encryption, and secret management.
 
 
 </li>
 </ul>
 
 Cross‑Functional Collaboration
 
 
 
 <ul>
 <li>
 Partner closely with data scientists, ML engineers, and backend engineers to productionize ML models and streamline development workflows.
 </li>
 <li>
 Contribute to the integration of emerging GenAI workloads, including Amazon Bedrock, vector databases (e.g., OpenSearch), and RAG pipelines.
 
 
 </li>
 </ul>
 
 Required Qualifications
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Engineering, or a related field.
 </li>
 <li>
 7+ years of professional experience in ML engineering, DevOps, cloud engineering, or MLOps roles, with at least 3 years in a senior or lead capacity.
 </li>
 <li>
 3+ years of proven track record in designing and architecting robust, scalable ML systems and infrastructure in cloud environments, particularly on AWS.
 </li>
 <li>
 5+ years of deep experience building on the AWS ML ecosystem, including SageMaker, S3, Lambda, ECR, EKS/ECS, Step Functions, IAM, VPC networking, and CI/CD tooling.
 </li>
 <li>
 3+ years of hands-on experience deploying, maintaining, and scaling ML models in production environments.
 </li>
 <li>
 3+ years of strong Python development skills and familiarity with Docker‑based workflows.
 </li>
 <li>
 5+ years of solid understanding of ML lifecycles, model evaluation, and monitoring patterns.
 </li>
 <li>
 5+ years of extensive experience with infrastructure‑as‑code (Terraform, CloudFormation).
 </li>
 <li>
 5+ years of expertise in designing system architecture for ML platforms, including microservices, container orchestration, and cloud networking.
 </li>
 <li>
 3+ years of familiarity with MLOps best practices as defined by AWS and industry standards.
 </li>
 <li>
 2+ years of experience with data quality frameworks (Great Expectations, Deequ).
 </li>
 <li>
 2+ years of experience optimizing distributed training workflows on AWS.
 </li>
 <li>
 3+ years of knowledge of security and compliance requirements for ML in enterprise settings, such as IAM, encryption, and secret management.
 </li>
 <li>
 2+ years of experience with monitoring tools (CloudWatch, Prometheus, Grafana) and implementing model observability solutions.
 </li>
 <li>
 5+ years of effective cross-functional collaboration skills, working closely with data scientists, ML engineers, and software engineers to deliver production-grade ML solutions.
 </li>
 <li>
 7+ years of excellent problem-solving and communication abilities, with a focus on delivering scalable, reliable, and cost-effective ML platforms.
 
 
 </li>
 </ul>
 
 The Brightly culture
 
 
 
 We&rsquo;re guided by a vision of community that serves the ambitions and wellbeing of all people, and our professional communities are no exception. We model that ideal every day by being supportive, collaborative partners to one another, conscientiously making space for our colleagues to grow and thrive. Our passionate team is driven to create a future where smarter infrastructure protects the environments that shape and connect us all. That brighter future starts with us.
</div>

Senior Machine Learning Operations Engineer

Valeo

<div>
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Deploy and maintain secure cloud infrastructure primarily on GCP (Google Cloud Platform) and AWS, ensuring seamless integration between services.
 </li>
 <li>
 Manage and optimize GKE (Google Kubernetes Engine) clusters for high-availability AI applications and microservices.
 </li>
 <li>
 Infrastructure as Code: Build and enforce Terraform strategies to provision and manage infrastructure, ensuring environments are reproducible and version-controlled.
 </li>
 <li>
 CI/CD &amp; Software Verification: Design and implement advanced CI/CD workflows using GitHub Actions, moving beyond simple deployments to create intelligent automation.
 </li>
 <li>
 Build robust verification pipelines that include automated testing, linting, security scanning, and quality gates before production release.
 </li>
 <li>
 Streamline the release process for backend and frontend applications, ensuring "one-click" reliability.
 </li>
 <li>
 Oversee the deployment, maintenance, and backup strategies for databases.
 </li>
 <li>
 Implement comprehensive monitoring and logging solutions (Prometheus, Grafana, Cloud Ops, Cloud Monitoring) to ensure system health, performance, and rapid incident response.
 </li>
 <li>
 Implement security best practices (IAM, VPC configuration, encryption) to protect sensitive AI data and intellectual property.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 
 
 Education &amp; Experience
 
 
 
 
 <ul>
 <li>
 B.Sc. or M.Sc. in Computer Science, Computer Engineering, or a related technical field.
 </li>
 <li>
 5+ years of relevant experience in DevOps, Cloud Engineering, or Site Reliability Engineering (SRE).
 </li>
 <li>
 Proven experience acting as a Senior or Lead engineer, guiding architectural decisions.
 
 
 </li>
 </ul>
 
 
 Technical Requirements
 
 
 
 
 <ul>
 <li>
 Advanced hands-on experience with GCP (specifically GKE, VPC, IAM) and a working knowledge of AWS.
 </li>
 <li>
 Proven ability to design and implement robust, scalable, and secure cloud architectures.
 </li>
 <li>
 Mastery of Docker and Kubernetes administration.
 </li>
 <li>
 Strong proficiency in Terraform.
 </li>
 <li>
 Expert knowledge of GitHub Actions for building, test, build, and deploy pipelines.
 </li>
 <li>
 Experience managing PostgreSQL and ClickHouse databases.
 </li>
 <li>
 Deep understanding of Linux System Administration.
 </li>
 <li>
 Experience with AI/ML lifecycle tools (e.g., Kubeflow, MLflow, Vertex AI).
 </li>
 <li>
 Strong proficiency in Python and Bash scripting.
 </li>
 <li>
 Familiarity with DevSecOps tools and practices.
 </li>
 <li>
 Bonus: Official GCP Certifications (e.g., Professional Cloud Architect, Professional Cloud DevOps Engineer) and Kubernetes Certifications (e.g., CKA, CKAD, CKS) are highly preferred.
 </li>
 <li>
 Bonus: Knowledge of Javascript/Node.js is a strong plus.
 
 
 </li>
 </ul>
 
 
 Soft Skills &amp; Mindset
 
 
 
 
 <ul>
 <li>
 Ability to design long-term solutions rather than quick fixes.
 </li>
 <li>
 Excellent ability to explain complex cloud concepts to Data Scientists and business stakeholders.
 </li>
 <li>
 Comfortable working in a fast-paced environment with evolving requirements.
 </li>
 <li>
 Fluent English is a must.
 </li>
 </ul>
</div>

Senior DevOps Engineer, AI4ALL

<div>
 
 About Brightly
 
 
 
 Brightly is a global leader in intelligent, cloud‑based asset lifecycle management, empowering 12,000+ clients with predictive insights to optimize maintenance, energy, and capital planning&mdash;backed by Siemens. Our products help schools, hospitals, cities, and manufacturers keep critical infrastructure running efficiently and sustainably.
 
 
 
 The Opportunity
 
 
 
 We&rsquo;re looking for a Senior Machine Learning Engineer to lead LLM‑powered application development&mdash;from prototype to production&mdash;on AWS. You&rsquo;ll design robust ML/LLM services that power search, recommendations, copilots, and workflow automation in Brightly&rsquo;s platform, partnering closely with product, data, and engineering teams. Responsibilities and skill expectations reflect current industry practice for senior ML/LLM engineers, including end‑to‑end model lifecycle ownership, production‑grade code, and MLOps.
 
 
 
 What You&rsquo;ll Do
 
 
 
 <ul>
 <li>
 Build LLM applications: Design and implement RAG pipelines, prompt orchestration, tools/agents, safety/guardrails, and evaluation harnesses; instrument for latency, cost, and quality. (Guided by current LLM engineer role practices.)
 </li>
 <li>
 Own the ML lifecycle: Data curation, feature engineering, training/fine‑tuning (LoRA/QLoRA), A/B testing, deployment, monitoring, and continuous improvement of models and prompts.
 </li>
 <li>
 Productionize on AWS: Ship scalable services on EKS/ECS/Lambda; leverage SageMaker, Bedrock, EMR, MSK, Step Functions; apply observability (CloudWatch/OpenTelemetry) and cost controls. (Duties aligned to modern AWS ML roles.)
 </li>
 <li>
 Scale training &amp; inference: Use distributed training (FSDP/DeepSpeed), quantization, caching, vector databases, and GPU/Inferentia for performance and efficiency.
 </li>
 <li>
 MLOps &amp; governance: Establish CI/CD for models (MLflow/Kedro/SageMaker Pipelines), model/version registries, data and prompt lineage, evaluation gates, and responsible‑AI controls. (Aligned with contemporary MLOps templates.)
 </li>
 <li>
 Partner across Brightly: Translate asset‑management use cases into ML/LLM solutions; collaborate with product managers and UX to ship customer‑visible features that measurably improve reliability, safety, and sustainability.
 </li>
 <li>
 Mentor &amp; lead: Provide technical leadership, review designs/PRs, and raise the bar on ML engineering excellence across the team. (Common senior ML expectations.)
 </li>
 <li>
 Perform Exploratory Data Analysis (EDA) on structured, semi‑structured, and unstructured datasets to identify patterns, correlations, feature importance, and data quality issues.
 
 
 </li>
 </ul>
 (Consistent with ML engineer responsibilities to analyze data before model development.)
 
 
 <ul>
 <li>
 Conduct deep research on asset-related, operational, and domain-specific datasets to understand root causes, trends, and predictive signals.
 
 
 </li>
 </ul>
 
 What You&rsquo;ll Bring
 
 
 
 
 Required experience
 
 
 
 <ul>
 <li>
 5&ndash;7 years total software/ML engineering experience, with 3+ years building and operating ML systems in production.
 </li>
 <li>
 1+ years hands‑on LLM application development (e.g., RAG, fine‑tuning, prompt engineering, evaluators/guardrails, agentic workflows) using packages such as Langchain and Langgraph.
 </li>
 <li>
 AWS proficiency (3+ years): Strong with core services (EKS/ECS, Lambda, S3, DynamoDB/RDS, Step Functions, IAM) and ML stack (SageMaker, Bedrock or HF on AWS). (Representative AWS ML role skills.)
 </li>
 <li>
 Modeling &amp; frameworks: Python, PyTorch, Hugging Face ecosystem; vector stores (e.g., OpenSearch, PGVector, Pinecone), embeddings, retrieval, and evaluation metrics for NLP/LLMs. (In line with senior LLM roles.)
 </li>
 <li>
 MLOps: CI/CD for ML, model registries, experiment tracking, telemetry/monitoring, automated retraining; Docker/Kubernetes, GitHub Actions/GitLab CI. (Current MLOps expectations.)
 </li>
 <li>
 Data engineering fluency: ETL/ELT, streaming/batch (Spark/Flink), data quality and governance controls for ML.
 
 
 </li>
 </ul>
 
 Nice to have
 
 
 
 <ul>
 <li>
 Experience with distributed training (FSDP, DeepSpeed), RLHF, or Inferentia/Trainium optimization.
 </li>
 <li>
 Exposure to sustainability/asset/intelligent operations domains.
 </li>
 <li>
 Familiarity with security &amp; compliance for ML systems in enterprise environments. (Frequently included in senior ML roles.)
 
 
 </li>
 </ul>
 How you&rsquo;ll work
 
 
 <ul>
 <li>
 Pragmatic and product‑oriented: You bias to measurable outcomes and iterate quickly with stakeholders. (Modern senior ML role framing.)
 </li>
 <li>
 Engineering excellence: You write production‑quality Python, design reliable APIs/services, and uphold testing/observability standards. (Common duties in senior templates.)
 </li>
 <li>
 Collaborative leadership: You mentor peers and influence architecture across teams. (Industry‑standard senior expectations.)
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s in CS/EE/Math or related field (Master&rsquo;s preferred) or equivalent practical experience. (Typical for senior ML roles.)
 
 
 </li>
 </ul>
 Join a mission‑driven team building technology that keeps communities running&mdash;safer, greener, and more resilient&mdash;at global scale. You&rsquo;ll pair startup‑speed product work with the reach and rigor of Siemens.
 
 
 
 The Brightly culture
 
 
 
 We&rsquo;re guided by a vision of community that serves the ambitions and wellbeing of all people, and our professional communities are no exception. We model that ideal every day by being supportive, collaborative partners to one another, conscientiously making space for our colleagues to grow and thrive. Our passionate team is driven to create a future where smarter infrastructure protects the environments that shape and connect us all. That brighter future starts with us.
</div>

Senior Machine Learning Engineer

DeepSource Technologies

<div>
 This role combines cloud engineering, automation, CI/CD, and operational excellence across
 
 Microsoft Azure
 
 and
 
 Amazon Web Services (AWS)
 
 .The engineer designs, builds, and maintains scalable, secure, and automated cloud environments while enabling continuous delivery and high‑velocity software deployment.
 
 
 
 Key Responsibilities:
 
 
 
 
 
 Cloud Infrastructure Engineering (Azure + AWS) :
 
 
 
 <ul>
 <li>
 Architect, deploy, and manage cloud environments using Azure and AWS core services
 </li>
 <li>
 Implement Infrastructure‑as‑Code using Terraform, ARM templates, and AWS CloudFormation (inferred from IaC skills listed across AWS DevOps sources)
 </li>
 <li>
 Optimize performance, cost, and reliability across multi‑cloud environments
 </li>
 <li>
 Implement monitoring and observability using Azure Monitor, CloudWatch, and APM tools
 </li>
 <li>
 AWS DevOps roles emphasize observability and monitoring as core skills
 
 
 
 </li>
 </ul>
 
 DevOps, CI/CD &amp; Automation :
 
 
 
 <ul>
 <li>
 Build and maintain CI/CD pipelines using Azure DevOps, GitHub Actions, Jenkins, and AWS Code Pipeline/CodeBuild / CodeDeploy
 </li>
 <li>
 AWS DevOps engineers require deep CD expertise and familiarity with AWS-native CI/CD tools.
 </li>
 <li>
 Automate deployments, testing, and configuration management using Ansible, Terraform, and scripting languages
 </li>
 <li>
 AWS DevOps roles highlight automation, scripting, and IaC as essential.
 
 
 
 </li>
 </ul>
 
 Containers &amp; Orchestration
 
 
 
 <ul>
 <li>
 Deploy and manage containerized applications using Docker and Kubernetes (AKS/EKS)
 </li>
 <li>
 Implement microservices architectures and automated container pipelines
 </li>
 <li>
 Docker and Kubernetes are core AWS DevOps technical skills
 
 
 
 </li>
 </ul>
 
 Security, Compliance &amp; Reliability
 
 
 
 <ul>
 <li>
 Apply cloud security best practices across Azure and AWS IAM, secrets management, and network security
 </li>
 <li>
 Support DR, backup strategies, and high‑availability architectures
 </li>
 <li>
 Implement Dev/Sec/Ops practices and compliance controls (inferred from AWS DevOps security emphasis)
 
 
 
 </li>
 </ul>
 
 Collaboration &amp; Documentation
 
 
 
 <ul>
 <li>
 Work closely with development, QA, and operations teams to streamline delivery
 </li>
 <li>
 Document cloud architectures, pipelines, and operational procedures
 
 
 
 </li>
 </ul>
 
 Requirements
 
 
 
 <ul>
 <li>
 7-10 years of experience
 </li>
 <li>
 Multi-Cloud Mastery: Expert-level architecting and deployment across both Microsoft Azure and AWS core services
 </li>
 <li>
 IaC &amp; Automation: Advanced proficiency in Terraform, Ansible, ARM templates, and CloudFormation to ensure environment consistency
 </li>
 <li>
 DevOps &amp; CI/CD: Deep expertise in building robust pipelines using Azure DevOps, GitHub Actions, and AWS-native tools (CodePipeline/CodeBuild)
 </li>
 <li>
 Orchestration: Hands-on experience managing containerized microservices via Docker and Kubernetes (AKS/EKS)
 
 
 
 </li>
 </ul>
 
 Benefits
 
 
 
 This is a 3-month, full-time onsite engagement offering a premium experience for the right consultant:
 
 
 <ul>
 <li>
 Global Mobility: Full coverage of Visa costs and Airline tickets (Round trip)
 </li>
 <li>
 Premium Stay: High-quality Accommodation provided for the duration of the project in KSA
 </li>
 <li>
 Prestigious Portfolio: The opportunity to add a "Giga-Project" to your professional record, working alongside top-tier industry leaders
 </li>
 <li>
 High Impact: A fast-paced, 3-month sprint where your decisions directly shape the technical infrastructure of a historic landmark
 </li>
 </ul>
</div>

Azure & AWS DevOps Cloud Engineer (3-Months Project), Riyadh,KSA

<div>
 Valeo est une entreprise mondiale de haute technologie qui con&ccedil;oit des solutions r&eacute;volutionnaires pour r&eacute;inventer la mobilit&eacute;. Nous sommes un &eacute;quipementier partenaire des constructeurs automobiles et des acteurs de la nouvelle mobilit&eacute; dans le monde entier. Notre vision ? Inventer une mobilit&eacute; plus verte et plus s&ucirc;re, gr&acirc;ce &agrave; des solutions ax&eacute;es sur la conduite intuitive et la r&eacute;duction des &eacute;missions de CO2. Nous sommes leaders dans nos domaines d'activit&eacute; et reconnus comme l'une des plus grandes entreprises innovantes au monde.
 
 
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Deploy and maintain secure cloud infrastructure primarily on GCP (Google Cloud Platform) and AWS, ensuring seamless integration between services.
 </li>
 <li>
 Manage and optimize GKE (Google Kubernetes Engine) clusters for high-availability AI applications and microservices.
 </li>
 <li>
 Infrastructure as Code~ Build and enforce Terraform strategies to provision and manage infrastructure, ensuring environments are reproducible and version-controlled.
 </li>
 <li>
 CI/CD &amp; Software Verification~ Design and implement advanced CI/CD workflows using GitHub Actions, moving beyond simple deployments to create intelligent automation.
 </li>
 <li>
 Build robust verification pipelines that include automated testing, linting, security scanning, and quality gates before production release.
 </li>
 <li>
 Streamline the release process for backend and frontend applications, ensuring "one-click" reliability.
 </li>
 <li>
 Oversee the deployment, maintenance, and backup strategies for databases.
 </li>
 <li>
 Implement comprehensive monitoring and logging solutions (Prometheus, Grafana, Cloud Ops, Cloud Monitoring) to ensure system health, performance, and rapid incident response.
 </li>
 <li>
 Implement security best practices (IAM, VPC configuration, encryption) to protect sensitive AI data and intellectual property.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 
 
 Education &amp; Experience
 
 
 
 
 <ul>
 <li>
 B.Sc. or M.Sc. in Computer Science, Computer Engineering, or a related technical field.
 </li>
 <li>
 5+ years of relevant experience in DevOps, Cloud Engineering, or Site Reliability Engineering (SRE).
 </li>
 <li>
 Proven experience acting as a Senior or Lead engineer, guiding architectural decisions.
 
 
 </li>
 </ul>
 
 
 Technical Requirements
 
 
 
 
 <ul>
 <li>
 Advanced hands-on experience with GCP (specifically GKE, VPC, IAM) and a working knowledge of AWS.
 </li>
 <li>
 Proven ability to design and implement robust, scalable, and secure cloud architectures.
 </li>
 <li>
 Mastery of Docker and Kubernetes administration.
 </li>
 <li>
 Strong proficiency in Terraform.
 </li>
 <li>
 Expert knowledge of GitHub Actions for building, test, build, and deploy pipelines.
 </li>
 <li>
 Experience managing PostgreSQL and ClickHouse databases.
 </li>
 <li>
 Deep understanding of Linux System Administration.
 </li>
 <li>
 Experience with AI/ML lifecycle tools (e.g., Kubeflow, MLflow, Vertex AI).
 </li>
 <li>
 Strong proficiency in Python and Bash scripting.
 </li>
 <li>
 Familiarity with DevSecOps tools and practices.
 </li>
 <li>
 Bonus~ Official GCP Certifications (e.g., Professional Cloud Architect, Professional Cloud DevOps Engineer) and Kubernetes Certifications (e.g., CKA, CKAD, CKS) are highly preferred.
 </li>
 <li>
 Bonus~ Knowledge of Javascript/Node.js is a strong plus.
 
 
 </li>
 </ul>
 
 
 Soft Skills &amp; Mindset
 
 
 
 
 <ul>
 <li>
 Ability to design long-term solutions rather than quick fixes.
 </li>
 <li>
 Excellent ability to explain complex cloud concepts to Data Scientists and business stakeholders.
 </li>
 <li>
 Comfortable working in a fast-paced environment with evolving requirements.
 </li>
 <li>
 Fluent English is a must.
 
 
 </li>
 </ul>
 
 Position~
 
 
 
 AI4all
 
 
 
 D&eacute;partement~
 
 
 
 Product technical
 
 
 
 Schedule~
 
 
 
 Temps plein
 
 
 
 Employee Status~
 
 
 
 CDI
 
 
 
 Type de contrat~
 
 
 
 
 Job Posting Date~
 
 
 
 2026-02-03
 
 
 
 Join Us !
 
 
 Being part of our team, you will join~
 
 
 <ul>
 <li>
 one of the largest global innovative companies, with more than 20,000 engineers working in Research &amp; Development
 </li>
 <li>
 a multi-cultural environment that values diversity and international collaboration
 </li>
 <li>
 more than 100,000 colleagues in 31 countries... which make a lot of opportunity for career growth
 </li>
 <li>
 a business highly committed to limiting the environmental impact if its activities and ranked by Corporate Knights as the number one company in the automotive sector in terms of sustainable development
 
 
 </li>
 </ul>
 More information on Valeo~ https~//www.valeo.com
 
</div>

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Senior Cloud Engineer - AWS (Professional Services)

Senior Cloud Engineer - AWS (Professional Services)

Senior Machine Learning Operations Engineer

Senior DevOps Engineer, AI4ALL

Senior Machine Learning Engineer

Azure & AWS DevOps Cloud Engineer (3-Months Project), Riyadh,KSA

Senior DevOps Engineer, AI4ALL