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

Valleysoft

<div>Valleysoft | Center of Excellence is a regional IT services provider based in Egypt, serving clients globally since 2006. The company collaborates with global partners like Oracle to address diverse business and technical challenges, from enterprise application development to process management. Valleysoft's vendor-neutral and process-oriented approach, coupled with operational maturity, ensures high-quality and cost-effective services for clients.
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> 
Requirements 

Required Skills &amp; QualificationsMLOps &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:</li><li><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

Finaira

<div>
 
 The MLOps Engineer II will independently design, implement, and manage the automation and streamlining of the machine learning lifecycle, ensuring reliable, efficient, and scalable deployment and monitoring of models. This role requires a solid understanding of DevOps practices, cloud technologies, and machine learning frameworks, with the ability to bridge the gap between data science and operations effectively.
 
 
 
 Focus:
 
 Independent automation of ML pipelines, deployment strategies, and monitoring in production.
 
 
 
 
 
 
 
 The difference you will make
 
 
 
 :&bull; Independently design and implement CI/CD pipelines for machine learning models
 
 
 .&bull; Automate the process of model training, validation, testing, and deployment using relevant tools. &bull; Develop and maintain automated testing frameworks for machine learning models
 
 
 .&bull; Develop and manage various model deployment strategies (e.g., A/B testing, canary deployments)
 
 
 .&bull; Build and maintain scalable and reliable infrastructure for model serving (e.g., using Kubernetes, serverless functions)
 
 
 .&bull; Implement and manage model versioning and rollback mechanisms
 
 
 .&bull; Optimize model serving for latency, throughput, and resource utilization
 
 
 .&bull; Implement and manage comprehensive monitoring and logging systems to track model performance and identify issues (e.g., model drift, data drift)
 
 
 .&bull; Set up and manage alerting systems to notify the team of performance degradation
 
 
 .&bull; Contribute to the development and implementation of model governance policies and procedures
 
 
 .&bull; Ensure compliance with security and privacy requirements
 
 
 .&bull; Collaborate effectively with data scientists to understand model requirements and dependencies. &bull; Work with software engineers to integrate machine learning models into applications and services
 
 
 .&bull; Develop and maintain APIs and interfaces for model access
 
 
 
 
 
 
 .
 
 What you will bring to the rol
 
 
 
 
 e:Educatio
 
 
 <ul>
 <li>
 n:Bachelor's degree in Computer Science, Engineering, or a related fiel
 </li>
 </ul>
 
 
 
 
 
 d.
 
 Experien
 
 
 <ul>
 <li>
 ce:3+ years of experience in DevOps, software engineering, or a related role with a focus on machine learning deployment and operations. Proven experience with cloud platforms and containerization technologies. Experience in the financial services industry is a pl
 </li>
 </ul>
 
 
 
 
 
 us.
 
 Technical Skil
 
 
 <ul>
 <li>
 ls: Strong proficiency in DevOps practices and tools (e.g., Jenkins, GitLab CI, Docker, Kubernet
 </li>
 <li>
 es).Solid knowledge of cloud computing platforms (e.g., AWS, Azure, GCP) and their machine learning servi
 </li>
 <li>
 ces.Knowledge of MLflow or Kubeflow for model management, or simi
 </li>
 <li>
 lar.Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-lea
 </li>
 <li>
 rn).Strong experience with scripting and automation (e.g., Python, Ba
 </li>
 <li>
 sh).Solid understanding of software engineering principles and best practi
 </li>
 <li>
 ces.Experience with infrastructure as code (IaC) tools (e.g., Terraform, CloudFormati
 </li>
 </ul>
 
 
 
 
 
 on).
 
 Soft Sk
 
 
 <ul>
 <li>
 ills:Strong ownership and accountability for assigned
 </li>
 <li>
 work.Excellent communication, interpersonal, and conflict resolution sk
 </li>
 <li>
 ills.Effective time management and task prioritiza
 </li>
 <li>
 tion.Analytical thinking and problem-solving sk
 </li>
 <li>
 ills.Ability to work independently and collaborate with teams within and across funct
 </li>
 <li>
 ions.Ability to actively listen to customers and accurately understand their needs, expectations, and challe
 </li>
 <li>
 nges.Adaptability and learning agility in a fast-paced environ
 </li>
 <li>
 ment.Strong attention to detail and a commitment to qua
 </li>
 <li>
 lity.Receptiveness to feedback and continuous improvement min
 </li>
 </ul>
 
 
 
 dset.
</div>

MLOps Engineer II

Openkyber

<div>Sr. Principal Technologist Cloud FinOps (AWS / Azure / Google Cloud Platform):
Location: 100% Remote Employment Type: Contract / Long Term
Company Overview Kanshe Infotech Kanshe Infotech is a technology consulting and staffing solutions company delivering highly skilled IT professionals to enterprise clients across the United States. We specialize in Cloud, Data Engineering, AI/ML, DevOps, and Enterprise Application development. Our mission is to help organizations accelerate digital transformation by connecting them with top technology talent and delivering scalable technology solutions.
Position Overview: Seeking for a Senior Principal Technologist Cloud Financial Operations (FinOps) to lead and drive cloud cost optimization, financial governance, and operational excellence across a multi-cloud environment including AWS, Microsoft Azure, and Google Cloud Platform (Google Cloud Platform). This strategic role partners with Cloud Engineering, Enterprise Architecture, Finance, and Application Development teams to ensure the organization maximizes value from its cloud investments while maintaining strong governance, transparency, and financial accountability. The ideal candidate will have deep expertise in Cloud FinOps strategy, cloud cost optimization, financial governance frameworks, and enterprise-scale cloud environments.
Key Responsibilities:
<ul><li>Cloud Financial Operations &amp; Cost Optimization</li>
<li>Develop and implement FinOps strategies to optimize cloud spending across AWS, Azure, and Google Cloud Platform.</li>
<li>Monitor and analyze cloud usage patterns to identify cost optimization opportunities.</li>
<li>Investigate cloud cost anomalies and collaborate with engineering teams to resolve and prevent recurrence.</li>
<li>Implement cost-efficiency practices including resource rightsizing, reservation planning, and usage optimization.</li>
</ul><ul><li>Governance &amp; Financial Accountability</li>
<li>Design, enhance, and enforce cloud governance frameworks aligned with enterprise standards and industry best practices.</li>
<li>Establish financial accountability models including showback and chargeback processes.</li>
<li>Strengthen governance across a multi-cloud enterprise architecture.</li>
</ul><ul><li>Financial Reporting &amp; Forecasting</li>
<li>Manage cloud financial reporting including: Budgeting Forecasting Cost allocation Showback / Chargeback models</li>
<li>Provide financial insights and reporting to leadership and business stakeholders.</li>
</ul><ul><li>Cross-Functional Collaboration</li>
<li>Partner with Development Teams, Cloud Engineering, Enterprise Architecture, and Finance teams to identify and implement cost savings opportunities.</li>
<li>Provide expert guidance on cloud cost optimization strategies and financial governance best practices.</li>
</ul><ul><li>Continuous Improvement</li>
<li>Stay current with cloud technologies, FinOps frameworks, and cost optimization methodologies.</li>
<li>Drive adoption of industry best practices in cloud financial management.</li>
</ul>Required Qualifications
<ul><li>10+ years of overall IT experience with strong focus on Cloud and FinOps.</li>
<li>Deep expertise in AWS and Azure cloud services and cost structures.</li>
<li>Exposure to Google Cloud Platform (Google Cloud Platform) environments.</li>
<li>Strong knowledge of cloud pricing models and cost management strategies.</li>
<li>Experience implementing FinOps governance frameworks in large enterprise environments.</li>
<li>Strong analytical skills with ability to interpret complex cloud billing data and deliver actionable insights.</li>
<li>Proven ability to manage multiple projects and internal stakeholders simultaneously.</li>
<li>Excellent communication and problem-solving skills.</li>
</ul>Preferred Qualifications
<ul><li>FinOps Certified Practitioner certification (preferred).</li>
<li>Experience with FinOps and cloud management tools, such as: Apptio Cloudability Cloud cost management platforms</li>
<li>Experience working in large enterprise or multi-cloud environments.</li>
</ul>For applications and inquiries, contact: hirings@openkyber.com</div>

Cloud-Native MLOps Engineer

Cambium Biomaterials

<div>AI Data Platform / Pipeline Engineer
(Full-time, onsite in El Segundo, CA &mdash; U.S. citizenship required due to the nature of our work)
About Cambium
Cambium is an advanced materials startup building next generation polymer and composite systems for aerospace, defense, and critical industries. We combine physics-based modeling, experimentation, and cutting-edge AI&mdash;including multimodal foundation models&mdash;to accelerate materials discovery and deployment.
Role Summary
We are seeking an AI Data Platform + MLOps engineer to build the pipelines, infrastructure, and automation that powers our AI foundation models, computational chemistry, and operational tools. You will build the data backbone that accelerates foundation model development and drives AI deployment into real manufacturing and operational workflows&mdash;with a mandate to reduce friction, eliminate manual pipelines, and enable rapid iteration.
Key Responsibilities<ul><li>Build robust pipelines for chemistry, materials, and manufacturing data ingestion and transformation.</li><li>Define data contracts, dataset versioning, and labeling/standardization workflows.</li><li>Operate ETL/training orchestration, experiment tracking, model registry, and deployment automation.</li><li>Manage cloud workflows (Azure + GCP), reproducible environments, monitoring, and cost/performance tuning.</li><li>Connect R&amp;D and Ops systems (ELN/LIMS/ERP/MES) into unified data flows.</li></ul>
Qualifications<ul><li>10+ years in data science, Dev/Ops, MLOps, or data engineering in an operational setting</li><li>Expert data pipelines, data tech stacks, orchestration, and MLOps/DevOps tooling.</li><li>Ability to design resilient, scalable pipelines in ambiguous, fast-moving environments.</li><li>Strong systems thinking, requirements gathering, and cross functional collaboration skills.</li><li>Familiarity with scientific or manufacturing data is a plus.</li></ul> 
BENEFITS
Cambium offers a highly competitive compensation and benefits plan. This includes excellent medical/dental/vision benefits, and a matching 401(k) plan, professional development reimbursement, and an opportunity for equity for most full time positions.
As a condition of employment, the candidate may need to successfully complete a medical evaluation and obtain clearance to wear respiratory personal protective equipment (PPE) as required by OSHA 29 CFR 1910.134</div>

AI Data Platform/Pipeline Engineer

KAUST (King Abdullah University of Science and Technology)

<div>
 
 
 About the Role
 
 
 
 
 
 
 The AI/ML Support Automation Analyst will be a key member of the KSL AI Support Team, focusing on MLOps infrastructure, container orchestration, and workflow automation at a supercomputing scale.
 
 
 This role is responsible for developing and maintaining secure, OCI-compliant container images, robust CI/CD pipelines, and cloud-native MLOps workflows that enable researchers to efficiently deploy and manage AI/ML workloads. The Analyst will bridge the gap between cutting-edge Kubernetes-based infrastructure and the diverse needs of the research community, contributing to governance, technical enablement, and community development initiatives.
 
 
 
 
 
 
 Responsibilities
 
 
 
 
 
 
 
 MLOps and Container Development
 
 
 <ul>
 <li>
 Providing timely and useful user support via telephone, walk-in, email, and ticketing system submissions for all types of inquiries.
 </li>
 <li>
 Maintain high customer service standards in dealing with and responding to user issues and questions.
 </li>
 <li>
 Develop and maintain secure, OCI-compliant, and HPC-ready AI/ML and data science software container images.
 </li>
 <li>
 Design and implement robust MLOps workflows and pipelines at supercomputing scale.
 </li>
 <li>
 Develop and maintain CI/CD pipelines for reproducible infrastructure and workflow deployment.
 </li>
 <li>
 Design and deploy APIs for AI/ML services and inference endpoints.
 </li>
 <li>
 Implement and manage Kubernetes-based orchestration, including CNI, CSI, and service mesh configurations and optimization.
 </li>
 <li>
 Deploy and maintain container registries (Harbor) and model registries (MLFlow, Kubeflow Model Registry).
 </li>
 </ul>
 
 
 
 
 
 Governance and Compliance Support
 
 
 <ul>
 <li>
 Assist in computational readiness reviews for AI research projects.
 </li>
 <li>
 Assist in AI model and artifact control reviews to ensure compliance with institutional standards.
 </li>
 <li>
 Provide consultation to users on efficient resource usage for AI/ML and MLOps workflows.
 </li>
 <li>
 Ensure container images and workflows comply with security policies and best practices.
 </li>
 <li>
 Support the implementation of usage monitoring and reporting systems.
 </li>
 </ul>
 
 
 
 
 
 Performance and Benchmarking
 
 
 <ul>
 <li>
 Perform performance debugging and tuning of MLOps and cloud-native workflows.
 </li>
 <li>
 Develop and maintain AI/ML and MLOps workload benchmarks for procuring new systems.
 </li>
 <li>
 Create and maintain regression testing workloads for existing clusters.
 </li>
 <li>
 Deploy and maintain observability and resource monitoring stacks using Prometheus, Grafana, NVIDIA DCGM, and Grafana Loki.
 </li>
 <li>
 Contribute to technology evaluation and benchmarking exercises for future infrastructure investments.
 </li>
 </ul>
 
 
 
 
 
 Training and Documentation
 
 
 <ul>
 <li>
 Create comprehensive training content for users on MLOps platforms, Kubernetes, and containerization.
 </li>
 <li>
 Develop and maintain high-quality user documentation for automation tools and workflows.
 </li>
 <li>
 Support the delivery of workshops on CI/CD, container orchestration, and MLOps best practices.
 </li>
 <li>
 Contribute to knowledge transfer initiatives within the KAUST research community.
 </li>
 <li>
 Provide one-on-one consultation to researchers on efficient use of automation infrastructure.
 </li>
 </ul>
 
 
 
 
 
 Qualifications
 
 
 
 
 
 <ul>
 <li>
 Bachelor's or master&rsquo;s degree in computer science, Data Science, Computational Science, Artificial Intelligence, or a related field.
 </li>
 <li>
 Certifications such as CKA (Certified Kubernetes Administrator), CKAD (Certified Kubernetes Application Developer), CKS (Certified Kubernetes Security Specialist), or CNPE (Certified Cloud Native Platform Engineer) are highly valued.
 </li>
 </ul>
 
 
 
 
 
 Required Skills
 
 
 
 
 
 <ul>
 <li>
 Demonstrated experience developing robust and complex MLOps pipelines.
 </li>
 <li>
 Hands-on experience with API design and deployment.
 </li>
 <li>
 Experience developing robust and portable CI/CD pipelines for reproducible infrastructure and workflow deployment.
 </li>
 <li>
 Experience supporting researchers or working in academic/research computing settings preferred.
 </li>
 </ul>
 
 
 
 
 
 Technical Skills - Essential
 
 
 <ul>
 <li>
 Kubernetes: Strong expertise in Kubernetes, Container Network Interface (CNI), Container Storage Interface (CSI), and Service Mesh.
 </li>
 <li>
 MLOps: Experience developing and maintaining MLOps pipelines and workflows.
 </li>
 <li>
 CI/CD: Proficiency in building CI/CD pipelines for infrastructure and application deployment.
 </li>
 <li>
 Containerization: Experience building secure, OCI-compliant container images.
 </li>
 <li>
 API Development: Experience in API design, development, and deployment.
 </li>
 <li>
 Programming: Proficiency in Python; experience with Go, Bash scripting.
 </li>
 <li>
 Linux: Strong Linux/Unix systems administration skills.
 </li>
 </ul>
 
 
 
 
 
 Technical Skills - Desired
 
 
 <ul>
 <li>
 Experience with ArgoCD, Airflow, DASK, Spark for workflow orchestration.
 </li>
 <li>
 Experience with Kubeflow, KServe, and Seldon for ML serving and pipelines.
 </li>
 <li>
 Experience deploying and maintaining observability stacks (Prometheus, Grafana, NVIDIA DCGM, Grafana Loki).
 </li>
 <li>
 Knowledge of Model Context Protocol (MCP) and agentic frameworks.
 </li>
 <li>
 Experience deploying inference services at scale.
 </li>
 <li>
 Experience deploying and maintaining container registries (Harbor) and model registries (MLFlow, Kubeflow Model Registry, Artifact Hub).
 </li>
 <li>
 Experience with GitOps practices and Infrastructure as Code (Terraform, Ansible).
 </li>
 <li>
 Experience with HPC schedulers (SLURM) and HPC-cloud integration.
 </li>
 </ul>
 
 
</div>

AI/ML Support Automation Analyst

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