mlops engineer

Hyatt

<div><div>Summary<div>
The Opportunity
Hyatt Hotels Corporation seeks an enthusiastic Senior ML Engineer to join our Data Science and Machine Learning department. In this role, you will be collaborating closely with the broader Data and Analytics team, where you&rsquo;ll be instrumental in continuing to make Hyatt a leading hospitality company. You will be part of a team that is passionate about our purpose, committed to nurturing curiosity and new skills, and building connections across the organization with colleagues, customers, and guests.
At Hyatt, we believe in the power of belonging and creating a culture of care, where our colleagues become family. Since 1957, our colleagues and our guests have been at the heart of our business and helped Hyatt become one of the best, and fastest growing hospitality brands in the world. Our transformative growth and the addition of new hotels, brands and business lines can open the door for exciting career and growth opportunities to our colleagues.
As we continue to grow, we never lose sight of what&rsquo;s most important: People. We turn trips into journeys, encounters into experiences and jobs into careers.
Why Now?
This is an exciting time to be at Hyatt. We are growing rapidly and are looking for passionate changemakers to be a part of our journey. The hospitality industry is resilient and continues to offer dynamic opportunities for upward mobility, and Hyatt is no exception.
How We Care for Our People
What sets us apart is our purpose&mdash;to care for people so they can be their best. Every business decision is made through the lens of our purpose, and it informs how we have and will continue to support each other as members of the Hyatt family. Our care for our colleagues is the key to our success. We&rsquo;re proud to have earned a place on Fortune&rsquo;s prestigious 100 Best Companies to Work For&reg; list for the last ten years. This recognition is a testament to the tremendous way our Hyatt family continues to come together to care for one another, our commitment to a culture of inclusivity, empathy and respect, and making sure everyone feels like they belong.
We&rsquo;re proud to offer exceptional corporate benefits which include:<ul><li> Annual allotment of free hotel stays at Hyatt hotels globally</li><li> Flexible work schedule</li><li> Work-life benefits including wellbeing initiatives such as a complimentary Headspace subscription, and a discount at the on-site fitness center</li><li> A global family assistance policy with paid time off following the birth or adoption of a child as well as financial assistance for adoption</li><li> Paid Time Off, Medical, Dental, Vision, 401K with company match</li></ul>
Who You Are
As our ideal candidate, you understand the power and purpose of our culture of care, and embody our core values of Empathy, Inclusion, Integrity, Experimentation, Respect and Wellbeing. You enjoy working with others, are results driven and are looking for a variety of opportunities to develop personally and professionally.
The Role
The Machine Learning Engineer partners with data science, data engineering, and platform teams to design, build, and operate scalable AI services. This role is responsible for translating machine learning models into reliable, production-grade systems through strong infrastructure design, MLOps automation, and performance optimization. The position also contributes to cross-functional initiatives that advance the organization&rsquo;s AI platform capabilities.
Responsibilities<ul><li> Design and implement end-to-end ML systems, including data ingestion, feature processing, model training, and model serving</li><li> Architect and deploy scalable AI services supporting real-time and batch inference use cases</li><li> Build and maintain ML infrastructure across cloud environments (e.g., EC2, EKS, SageMaker, specialized inference hardware)</li><li> Develop and evolve MLOps platforms, including training pipelines, deployment workflows, feature stores, and model observability</li><li> Implement CI/CD and infrastructure-as-code patterns to automate model lifecycle management</li><li> Optimize model training and inference performance for cost, latency, and hardware efficiency</li><li> Monitor production ML systems for accuracy, reliability, and operational health</li><li> Partner cross-functionally with data engineering, architecture, governance, and security teams to ensure compliant and scalable solutions</li><li> Mentor team members on ML engineering, system design, and operational best practices</li><li> Contribute to special initiatives that advance AI platform maturity and engineering standards</li></ul></div></div><div>
Qualifications<div>
Experience Required<ul><li> Master&rsquo;s degree in Computer Science, Software Engineering, Machine Learning, or a related field</li><li> 5+ years of experience building and operating machine learning solutions in cloud environments, with focus on AI services and MLOps foundations</li><li> Demonstrated hands-on experience delivering end-to-end ML systems, spanning model development, deployment, and production infrastructure</li><li> Proficiency with modern ML engineering tooling, including cloud platforms, data pipelines, and CI/CD workflows</li></ul>
Experience Preferred<ul><li> Experience designing and scaling real-time and batch inference systems in production</li><li> Hands-on experience with deep learning frameworks and model optimization for performance and cost</li><li> Experience building or contributing to shared MLOps platforms, feature stores, or ML observability solutions</li><li> Familiarity with cloud security, governance, and compliance standards</li></ul>
We welcome you:
Research shows that individuals tend to apply to jobs, only if they meet all the listed job qualifications. Unsure if you check every box, but feeling inspired to enhance your career? Apply. We&rsquo;d love to consider your unique experiences and how you could make Hyatt even better.
The salary range for this position is $133,200 - $173,000
The final pay rate/salary offered to the successful candidate will depend on experience, skill level and other qualifications for the role, as well as the location of the performance of work. Pay for the successful candidate will meet local requirements, including the local minimum wage rate.
We value our relationships with recruitment partners and require that agencies contact us first before submitting any candidates. Hyatt will not be responsible for any fees and obligations associated with unsolicited submissions unless a formal agreement is in place.</div></div></div>

SENIOR MACHINE LEARNING / MLOPS ENGINEER (REMOTE OPPORTUNITY)

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

NavitasPartners

<div>ML / AI Engineer &ndash; MLOps &amp; GenAI Platforms

Location: US / Canada (Remote/Hybrid) 
Type: Contract / Full-Time

Overview:

We are seeking an ML/AI Engineer to contribute to large-scale AI and data transformation programs. This role focuses on building, deploying, and scaling machine learning and GenAI solutions in cloud environments.

Key Responsibilities:
<ul>
<li>Design and deploy scalable ML and GenAI solutions</li>
</ul><ul><li>Build and manage end-to-end MLOps pipelines</li>
</ul><ul><li>Collaborate with data engineers, architects, and business teams</li>
</ul><ul><li>Ensure model performance, governance, and lifecycle management</li>
</ul>
Required Skills:
<ul>
<li>Strong experience in ML/AI engineering and MLOps practices</li>
</ul><ul><li>Proficiency in Python and frameworks such as PyTorch</li>
</ul><ul><li>Experience with cloud platforms (AWS, Azure, GCP)</li>
</ul><ul><li>Hands-on experience with model deployment and monitoring</li>
</ul>
Nice to Have / Coverage:
<ul>
<li>Experience with LangChain and GenAI/agentic AI implementations</li>
</ul><ul><li>Exposure to Databricks, Snowflake, Azure Synapse, or BigQuery</li>
</ul><ul><li>Familiarity with AI governance, Responsible AI, and compliance frameworks</li>
</ul><ul><li>Experience working with cloud-native data platforms and architectures</li>
</ul>
For more details reach at resumes@navitassols.com.</div>

ML / AI Engineer – MLOps & GenAI Platforms - AIRLHV

Franklin Fitch

<div>
 
 
 MLOps Engineer | Hybrid | Atlanta, GA | SaaS | $150K - $200K
 
 
 
 
 
 
 A technology company is
 
 embedding AI into its core product workflows
 
 - predictive models, intelligent automation, and LLM-enabled features - and needs an engineer who can build and own these systems in production. This is a hands-on role at the intersection of applied ML and platform engineering.
 
 You'll be setting the standard for how AI is built and operated here.
 
 
 
 
 
 
 
 What you'll do
 
 
 
 
 
 <ul>
 <li>
 Design, build, and deploy production ML models - REST inference services, batch pipelines, real-time scoring
 </li>
 <li>
 Establish MLOps practices: model versioning, monitoring, alerting, retraining, and lifecycle governance
 </li>
 <li>
 Evaluate new AI use cases and select the right approach - supervised learning, embeddings, retrieval-driven architectures, or hybrid
 </li>
 <li>
 Integrate ML outputs into product workflows alongside application engineering teams
 </li>
 <li>
 Partner with Data Engineering to ensure AI-ready data pipelines and structures
 </li>
 <li>
 Work with external AI partners initially, then progressively take ownership in-house
 </li>
 </ul>
 
 
 
 
 
 What you need
 
 
 
 
 
 <ul>
 <li>
 7+ years in ML engineering or applied ML, with production-grade systems in enterprise environments
 </li>
 <li>
 Strong Python and ML library experience - scikit-learn, PySpark, pandas
 </li>
 <li>
 Solid MLOps background: MLflow or equivalent model lifecycle tooling
 </li>
 <li>
 Cloud-native experience - Azure preferred; AWS or GCP considered
 </li>
 <li>
 Pragmatic approach to modelling - picks the right tool for the problem, not the most fashionable one
 </li>
 <li>
 Experience with ML and backend development tools: Python, MLflow / MLOps, Azure, scikit-learn / PySpark, REST, inference, APIs, Model monitoring, LLMs
 </li>
 </ul>
 
 
 
 
 
 Nice to have
 
 
 
 
 
 <ul>
 <li>
 Databricks - for scalable training pipelines and unified data + ML workflows
 </li>
 <li>
 Vector search, embeddings, or retrieval-augmented generation (RAG) experience
 </li>
 <li>
 Hospitality, travel, or similarly data-rich consumer industry background
 </li>
 </ul>
</div>

Machine Learning Engineer

Intellias

<div>
 
 
 MLOps Engineer
 
 
 
 We have a dream: to change industries through the power of digital technology. At Intellias, you will work with top-tier engineers to build and deploy cutting-edge machine learning solutions that bring AI models into real-world production at scale.
 
 
 You will join a dynamic team focused on designing and maintaining robust MLOps infrastructure for deploying and managing ML/AI systems across cloud environments, enabling the next generation of intelligent products.
 
 
 
 Requirements
 
 
 <ul>
 <li>
 5+ years of experience in
 
 MLOps, ML engineering, or similar roles
 
 </li>
 <li>
 Master&rsquo;s or higher degree in Computer Science or related field
 </li>
 <li>
 Strong experience in
 
 Computer Vision, NLP, or Generative AI
 
 </li>
 <li>
 Advanced programming skills in
 
 Python or Rust
 
 </li>
 <li>
 Experience building
 
 scalable ML pipelines
 
 (training, validation, deployment)
 </li>
 <li>
 Hands-on experience with
 
 AWS / Azure / GCP
 
 </li>
 <li>
 Experience with
 
 Kubernetes, Docker, and CI/CD
 
 </li>
 <li>
 Knowledge of
 
 Kubeflow, Airflow, Argo, or similar orchestration tools
 
 </li>
 <li>
 Experience with
 
 model registries and feature stores
 
 </li>
 <li>
 Experience deploying DL models using
 
 PyTorch or TensorFlow
 
 </li>
 <li>
 Experience building
 
 REST APIs (FastAPI or similar)
 
 </li>
 <li>
 Experience with
 
 GenAI agents (LangChain or similar)
 
 and LLM evaluation/observability
 </li>
 <li>
 Knowledge of
 
 Infrastructure as Code (Terraform)
 
 </li>
 <li>
 Strong communication and collaboration skills
 </li>
 </ul>
 
 
 Nice to Have
 
 
 <ul>
 <li>
 Experience deploying LLMs on-prem
 </li>
 <li>
 GCP ML Engineer certification
 </li>
 <li>
 Industry experience in retail, finance, healthcare, telecom, energy, or manufacturing
 </li>
 <li>
 Experience with data science projects
 </li>
 </ul>
 
 
 Responsibilities
 
 
 <ul>
 <li>
 Lead and support technical POCs and ML projects
 </li>
 <li>
 Design and improve MLOps architectures following best practices
 </li>
 <li>
 Support discovery phases and technical consulting
 </li>
 <li>
 Collaborate on presales and upsales activities
 </li>
 <li>
 Contribute to knowledge sharing and public speaking initiatives
 </li>
 </ul>
 
 
 Why Join Us
 
 
 
 At Intellias, people come first. We foster a culture where innovation thrives, enabling engineers to work on impactful AI solutions while continuously growing their expertise in a supportive and forward-thinking environment.
 
</div>

Machine Learning Ops Engineer

Yogi Careers

<div>
 
 
 Overview:
 
 
 
 
 
 We&rsquo;re looking for a Data Engineer to build and maintain the data infrastructure that powers machine learning initiatives. You&rsquo;ll work at the intersection of software engineering and data science.
 
 
 
 
 
 Responsibilities:
 
 
 
 
 <ul>
 <li>
 Develop and maintain feature stores and ML-ready datasets
 </li>
 <li>
 Automate data preprocessing pipelines for ML model training and evaluation
 </li>
 <li>
 Collaborate with ML engineers to enable scalable experimentation workflows
 </li>
 <li>
 Monitor and improve data reliability, lineage, and reproducibility
 
 
 </li>
 </ul>
 
 
 Requirements:
 
 
 
 
 <ul>
 <li>
 BS/MS in Computer Science, Data Engineering, or similar
 </li>
 <li>
 Experience with ML platforms (Databricks, AWS Sagemaker, Vertex AI)
 </li>
 <li>
 Strong Python and SQL skills, with familiarity in Spark or Dask
 </li>
 <li>
 Experience with Airflow, MLflow, or Kubeflow pipelines
 </li>
 <li>
 Solid understanding of MLOps, data validation, and model versioning
 
 
 </li>
 </ul>
 
 Job Category:
 
 Data Engineer
 
 
 
 Job Type:
 
 Full Time
 
 
 
 Job Location:
 
 Boston
 
 
 Apply for this position
 
 
 Full Name *
 
 
 Email *
 
 
 Phone *
 
 
 Cover Letter *
 
 
 Upload CV/Resume *Allowed Type(s): .pdf, .doc, .docx
 
 
 By using this form you agree with the storage and handling of your data by this website. *
 
 
 
 
 Responsibilities:
 
 
 
 
 <ul>
 <li>
 Develop and maintain feature stores and ML-ready datasets
 </li>
 <li>
 Automate data preprocessing pipelines for ML model training and evaluation
 </li>
 <li>
 Collaborate with ML engineers to enable scalable experimentation workflows
 </li>
 <li>
 Monitor and improve data reliability, lineage, and reproducibility
 
 
 </li>
 </ul>
 
 
 Requirements:
 
 
 
 
 <ul>
 <li>
 BS/MS in Computer Science, Data Engineering, or similar
 </li>
 <li>
 Experience with ML platforms (Databricks, AWS Sagemaker, Vertex AI)
 </li>
 <li>
 Strong Python and SQL skills, with familiarity in Spark or Dask
 </li>
 <li>
 Experience with Airflow, MLflow, or Kubeflow pipelines
 </li>
 <li>
 Solid understanding of MLOps, data validation, and model versioning
 
 
 </li>
 </ul>
 
 Job Category:
 
 Data Engineer
 
 
 
 Job Type:
 
 Full Time
 
 
 
 Job Location:
 
 Boston
 
 
 
 
 Responsibilities:
 
 
 
 
 <ul>
 <li>
 Develop and maintain feature stores and ML-ready datasets
 </li>
 <li>
 Automate data preprocessing pipelines for ML model training and evaluation
 </li>
 <li>
 Collaborate with ML engineers to enable scalable experimentation workflows
 </li>
 <li>
 Monitor and improve data reliability, lineage, and reproducibility
 
 
 </li>
 </ul>
 
 
 Requirements:
 
 
 
 
 <ul>
 <li>
 BS/MS in Computer Science, Data Engineering, or similar
 </li>
 <li>
 Experience with ML platforms (Databricks, AWS Sagemaker, Vertex AI)
 </li>
 <li>
 Strong Python and SQL skills, with familiarity in Spark or Dask
 </li>
 <li>
 Experience with Airflow, MLflow, or Kubeflow pipelines
 </li>
 <li>
 Solid understanding of MLOps, data validation, and model versioning
 </li>
 </ul>
</div>

Data Engineer – AI/ML Data Infrastructure

Faze 3 Consulting

AI/ML/DevOps EngineerOwn the MLOps platform that powers enterprise AI at scale.A leading Abu Dhabi-based holding group is hiring an AI/ML/DevOps Engineer to architect, operate, and continuously improve the end-to-end MLOps and LLMOps platform for a flagship enterprise AI programme. You'll be the technical authority reviewing, governing, and signing off CI/CD, data and model pipelines, infrastructure, deployment, security, and observability &mdash; ensuring secure, scalable, and compliant delivery across environments. Reports to the AI Product Manager within the AI Excellence Centre.What you'll own:<ul><li>Own the end-to-end MLOps/LLMOps reference architecture: ingestion &rarr; validation &rarr; feature and embedding pipelines &rarr; training and fine-tuning &rarr; evaluation &rarr; registry &rarr; deployment &rarr; monitoring &mdash; including RAG and agentic workflows.</li><li>Architect, review, and approve CI/CD for ML and LLM systems: code, data, prompt, and model artifact versioning; build and release pipelines (Azure DevOps / GitHub Actions); automated unit, integration, and contract testing; and promotion/rollback (blue-green / canary) across dev, test, and production.</li><li>Define and govern AI platform foundations on Azure: IaC (Bicep/Terraform), AML workspaces, AKS GPU node pools and scheduling, private networking (VNet integration / Private Link), identity (Managed Identities / PIM), secrets (Key Vault), and encryption and data residency controls.</li><li>Review and approve production deployment patterns for model and LLM serving (AKS / KServe / AML online endpoints), including containerization, inference optimization (batching, quantization where applicable), API management, autoscaling, resiliency, and RAG runtime components (vector store, retriever, re-ranker, cache).</li><li>Own observability and reliability for AI services: OpenTelemetry tracing, prompt and inference logs (with PII controls), latency/throughput/cost metrics, SLOs/SLIs, model performance monitoring, data and model drift detection, and LLM evaluations (quality, hallucination checks, toxicity and safety guardrails) with incident playbooks.</li><li>Establish and enforce MLOps/LLMOps governance: dataset lineage, data quality validation (schema and tests), feature store and model registry standards, artifact provenance (SBOM/SLSA), vulnerability scanning, approval gates for model and prompt releases, and compliance-aligned documentation for model risk (intended use, limitations, evaluation results).</li><li>Enable delivery squads &mdash; including the primary delivery partner &mdash; with &quot;golden path&quot; templates (AML pipelines, RAG blueprints, evaluation harnesses), reusable IaC modules, and coding standards; run deep technical design and architecture reviews and sign off production readiness (capacity, security, observability, DR) for all AI releases.</li><li>Support the Run &amp; Operate model by enabling issue triage and minor enhancement workflows (ticket intake &rarr; fix &rarr; controlled release), ensuring changes follow the same release governance and quality gates.</li><li>Own the Operational Acceptance Gate: no production release without runbooks, monitoring dashboards, incident playbooks, access model, and DR test evidence.</li></ul>Scope clarity: you provide platform standards, review, and sign-off &mdash; you do not replace the delivery partner's engineering, but you enforce the &quot;golden path&quot; and production readiness bar.What you bring:<ul><li>8&ndash;10 years across DevOps, SRE, and/or ML Engineering with production systems on Azure.</li><li>Hands-on experience with Azure ML, AKS, Azure DevOps or GitHub Actions, IaC, and containerization.</li><li>Bachelor's in Computer Science, Engineering, or equivalent experience.</li></ul>Core skills required:<ul><li>Python, YAML, Docker, Helm, KQL; GitOps (Argo/Flux) awareness.</li><li>Security in CI/CD: SAST/DAST, supply-chain security (Sigstore), secrets management (Key Vault).</li><li>Performance testing (k6 / JMeter), contract testing, and E2E testing.</li><li>Cost optimization and capacity planning for GPU and CPU workloads.</li><li>Strong grasp of model serving, inference optimization, and observability tooling.</li></ul>Required certification:<ul><li>Microsoft Certified: DevOps Engineer Expert (AZ-400)</li></ul>Preferred certifications:<ul><li>Microsoft Certified: Azure Administrator (AZ-104) or Solutions Architect (AZ-305)</li><li>CKA or CKAD (Kubernetes)</li></ul>Location: Abu Dhabi, UAEEmployment Type: Permanent, Full-timeExperience: 8&ndash;10 yearsSalary Range: 25,000 - 33,000 (AED per month)Pay: AED25,000.00 - AED33,000.00 per monthWork Location: In person

AI/ML/DevOps Engineer

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