mlops engineer

Datamatics Technologies

<div>
 Job Description: AI / ML Engineer
 <p>
  <strong>
   Job Title:
  </strong>
  AI / ML Engineer
  <br/>
  <strong>
   Experience:
  </strong>
  3&ndash;11 Years
  <br/>
  <strong>
   Location:
  </strong>
  &nbsp;
  <u>
   <strong>
    Riyadh (Onsite)
    <br/>
   </strong>
  </u>
  <strong>
   Employment Type:
  </strong>
  Full-Time
 </p>
 Job Overview
 <p>
  We are seeking a skilled
  <strong>
   AI / ML Engineer
  </strong>
  with
  <strong>
   3&ndash;11 years of experience
  </strong>
  to design, develop, deploy, and optimize machine learning and generative AI solutions. The ideal candidate will have hands-on expertise in building scalable AI/ML models, working with cloud-native AI platforms, and implementing production-ready machine learning pipelines. Experience with modern AI frameworks, large language models (LLMs), and MLOps practices is highly desirable.
 </p>
 Key Responsibilities
 <ul>
  <li>
   Design, develop, train, and deploy machine learning and deep learning models for enterprise applications
  </li>
  <li>
   Build and optimize end-to-end ML pipelines for data ingestion, model training, evaluation, and deployment
  </li>
  <li>
   Develop Generative AI and LLM-powered applications using modern AI frameworks
  </li>
  <li>
   Collaborate with data engineers, software developers, and business stakeholders to deliver AI-driven solutions
  </li>
  <li>
   Deploy and monitor ML models on cloud platforms while ensuring scalability, reliability, and security
  </li>
  <li>
   Optimize model performance through feature engineering, hyperparameter tuning, and continuous evaluation
  </li>
  <li>
   Implement MLOps best practices including model versioning, monitoring, and CI/CD automation
  </li>
  <li>
   Stay current with advancements in AI, machine learning, and cloud AI services
  </li>
 </ul>
 Required Technical SkillsCloud AI Platforms
 <ul>
  <li>
   Hands-on experience with
   <strong>
    GCP Vertex AI or Azure Machine Learning or AWS SageMaker
   </strong>
  </li>
  <li>
   Experience with
   <strong>
    Azure OpenAI or AWS Bedrock
   </strong>
   for Generative AI solutions
  </li>
  <li>
   Experience with
   <strong>
    BigQuery ML and Dataflow
   </strong>
   for data processing and machine learning workflows
  </li>
 </ul>
 Programming &amp; Machine Learning
 <ul>
  <li>
   Strong proficiency in
   <strong>
    Python
   </strong>
  </li>
  <li>
   Experience developing machine learning solutions using
   <strong>
    TensorFlow or PyTorch
   </strong>
  </li>
  <li>
   Strong understanding of supervised, unsupervised, reinforcement learning, and deep learning concepts
  </li>
 </ul>
 Generative AI &amp; LLM Frameworks
 <ul>
  <li>
   Experience with
   <strong>
    Hugging Face and LangChain
   </strong>
   for building LLM-powered applications
  </li>
  <li>
   Knowledge of prompt engineering, Retrieval-Augmented Generation (RAG), embeddings, and vector databases is preferred
  </li>
 </ul>
 Data Engineering &amp; Analytics
 <ul>
  <li>
   Experience with
   <strong>
    Databricks
   </strong>
   for data engineering, model development, and analytics workflows
  </li>
  <li>
   Strong understanding of data preprocessing, feature engineering, and large-scale data processing
  </li>
 </ul>
 MLOps &amp; Deployment
 <ul>
  <li>
   Experience deploying machine learning models into production
  </li>
  <li>
   Knowledge of Docker, Kubernetes, CI/CD pipelines, and model monitoring is an advantage
  </li>
 </ul>
 Qualifications
 <ul>
  <li>
   Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field
  </li>
  <li>
   <strong>
    3&ndash;11 years
   </strong>
   of professional experience in AI, Machine Learning, or Data Science
  </li>
  <li>
   Strong analytical, mathematical, and problem-solving skills
  </li>
  <li>
   Experience working in Agile development environments
  </li>
  <li>
   Excellent communication and collaboration skills
  </li>
 </ul>
 Preferred Skills
 <ul>
  <li>
   Experience with Large Language Models (LLMs) and Generative AI applications
  </li>
  <li>
   Knowledge of Retrieval-Augmented Generation (RAG), vector databases, and AI agents
  </li>
  <li>
   Experience with distributed model training and cloud-native AI architectures
  </li>
  <li>
   Cloud certifications in AWS, Azure, or Google Cloud are a plus
  </li>
 </ul>
 Key Technology Stack
 <ul>
  <li>
   <strong>
    Cloud AI:
   </strong>
   <strong>
    GCP Vertex AI or Azure Machine Learning or AWS SageMaker
   </strong>
  </li>
  <li>
   <strong>
    Generative AI:
   </strong>
   <strong>
    Azure OpenAI or AWS Bedrock
   </strong>
   <strong>
    and
   </strong>
   Large Language Models (LLMs)
  </li>
  <li>
   <strong>
    Data Processing:
   </strong>
   <strong>
    BigQuery ML and Dataflow
   </strong>
   <strong>
    and
   </strong>
   Databricks
  </li>
  <li>
   <strong>
    Programming:
   </strong>
   Python
  </li>
  <li>
   <strong>
    Machine Learning Frameworks:
   </strong>
   <strong>
    TensorFlow or PyTorch
   </strong>
  </li>
  <li>
   <strong>
    LLM Frameworks:
   </strong>
   <strong>
    Hugging Face or LangChain
   </strong>
  </li>
  <li>
   <strong>
    MLOps:
   </strong>
   Docker
   <strong>
    and
   </strong>
   Kubernetes
   <strong>
    and
   </strong>
   CI/CD (Preferred)
  </li>
 </ul>
</div>


AI / ML Engineer

Datamatics Global Services Ltd

<div><h1 class="jobSectionHeader"><b>Job Description: AI / ML Engineer</b></h1><p><b>
Job Title:</b> AI / ML Engineer<br>
<b>Experience:</b> 3&ndash;11 Years<br>
<b>Location:</b> <b>Riyadh (Onsite)</b><br>
<b>Employment Type:</b> Full-Time</p><h2 class="jobSectionHeader"><b>
Job Overview</b></h2><p>
We are seeking a skilled <b>AI / ML Engineer</b> with <b>3&ndash;11 years of experience</b> to design, develop, deploy, and optimize machine learning and generative AI solutions. The ideal candidate will have hands-on expertise in building scalable AI/ML models, working with cloud-native AI platforms, and implementing production-ready machine learning pipelines. Experience with modern AI frameworks, large language models (LLMs), and MLOps practices is highly desirable.</p><h2 class="jobSectionHeader"><b>
Key Responsibilities</b></h2><ul><li>
Design, develop, train, and deploy machine learning and deep learning models for enterprise applications.</li><li>
Build and optimize end-to-end ML pipelines for data ingestion, model training, evaluation, and deployment.</li><li>
Develop Generative AI and LLM-powered applications using modern AI frameworks.</li><li>
Collaborate with data engineers, software developers, and business stakeholders to deliver AI-driven solutions.</li><li>
Deploy and monitor ML models on cloud platforms while ensuring scalability, reliability, and security.</li><li>
Optimize model performance through feature engineering, hyperparameter tuning, and continuous evaluation.</li><li>
Implement MLOps best practices including model versioning, monitoring, and CI/CD automation.</li><li>
Stay current with advancements in AI, machine learning, and cloud AI services.</li></ul><h2 class="jobSectionHeader"><b>
Required Technical Skills</b></h2><h3 class="jobSectionHeader"><b>
Cloud AI Platforms</b></h3><ul><li>
Hands-on experience with <b>GCP Vertex AI or Azure Machine Learning or AWS SageMaker</b>.</li><li>
Experience with <b>Azure OpenAI or AWS Bedrock</b> for Generative AI solutions.</li><li>
Experience with <b>BigQuery ML and Dataflow</b> for data processing and machine learning workflows.</li></ul><h3 class="jobSectionHeader"><b>
Programming &amp; Machine Learning</b></h3><ul><li>
Strong proficiency in <b>Python</b>.</li><li>
Experience developing machine learning solutions using <b>TensorFlow or PyTorch</b>.</li><li>
Strong understanding of supervised, unsupervised, reinforcement learning, and deep learning concepts.</li></ul><h3 class="jobSectionHeader"><b>
Generative AI &amp; LLM Frameworks</b></h3><ul><li>
Experience with <b>Hugging Face and LangChain</b> for building LLM-powered applications.</li><li>
Knowledge of prompt engineering, Retrieval-Augmented Generation (RAG), embeddings, and vector databases is preferred.</li></ul><h3 class="jobSectionHeader"><b>
Data Engineering &amp; Analytics</b></h3><ul><li>
Experience with <b>Databricks</b> for data engineering, model development, and analytics workflows.</li><li>
Strong understanding of data preprocessing, feature engineering, and large-scale data processing.</li></ul><h3 class="jobSectionHeader"><b>
MLOps &amp; Deployment</b></h3><ul><li>
Experience deploying machine learning models into production.</li><li>
Knowledge of Docker, Kubernetes, CI/CD pipelines, and model monitoring is an advantage.</li></ul><h2 class="jobSectionHeader"><b>
Qualifications</b></h2><ul><li>
Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.</li><li><b>
3&ndash;11 years</b> of professional experience in AI, Machine Learning, or Data Science.</li><li>
Strong analytical, mathematical, and problem-solving skills.</li><li>
Experience working in Agile development environments.</li><li>
Excellent communication and collaboration skills.</li></ul><h2 class="jobSectionHeader"><b>
Preferred Skills</b></h2><ul><li>
Experience with Large Language Models (LLMs) and Generative AI applications.</li><li>
Knowledge of Retrieval-Augmented Generation (RAG), vector databases, and AI agents.</li><li>
Experience with distributed model training and cloud-native AI architectures.</li><li>
Cloud certifications in AWS, Azure, or Google Cloud are a plus.</li></ul><h2 class="jobSectionHeader"><b>
Key Technology Stack</b></h2><ul><li><b>
Cloud AI:</b> <b>GCP Vertex AI or Azure Machine Learning or AWS SageMaker</b></li><li><b>
Generative AI:</b> <b>Azure OpenAI or AWS Bedrock</b> <b>and</b> Large Language Models (LLMs)</li><li><b>
Data Processing:</b> <b>BigQuery ML and Dataflow</b> <b>and</b> Databricks</li><li><b>
Programming:</b> Python</li><li><b>
Machine Learning Frameworks:</b> <b>TensorFlow or PyTorch</b></li><li><b>
LLM Frameworks:</b> <b>Hugging Face or LangChain</b></li><li><b>
MLOps:</b> Docker <b>and</b> Kubernetes <b>and</b> CI/CD (Preferred)</li></ul><p>
</p><p>kmeseaCmpf</p></div>

Capgemini

<div><div><div><div><p>Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you&rsquo;d like, where you&rsquo;ll be supported and inspired by a collaborative community of colleagues around the world, and where you&rsquo;ll be able to reimagine what&rsquo;s possible. Join us and help the world&rsquo;s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.</p>
<div><div><div><h2 class="jobSectionHeader"><b>Location</b></h2>
</div><div><p>San Francisco CA</p>
</div></div><div><div><h2 class="jobSectionHeader"><b>Your Role</b></h2>
</div><div><p>The GenAI Engineer / Data Scientist is responsible for designing, developing, deploying, and maintaining Generative AI (GenAI) and Predictive AI solutions that deliver business value. The role requires expertise in AI/ML, cloud platforms, APIs, data engineering, MLOps, and AI agent workflows, with a strong focus on production-grade AI implementation.</p><br>
<p><br>
<b>Key Responsibilities</b><br>
</p><p><br>
Develop and deploy Generative AI solutions (GPT, Claude, Stable Diffusion) and Predictive AI models (regression, classification, clustering).
<br>
Design and integrate AI applications using REST APIs, authentication mechanisms, JSON services, and cloud-based architectures.
<br>
Build and support AI agents, autonomous systems, and agentic workflows.
<br>
Deploy, monitor, and optimize AI models in production using MLOps practices.
<br>
Design and maintain data pipelines, data lakes, and data warehouses.
<br>
Monitor model performance, troubleshoot issues, and manage data/model drift.
<br>
Collaborate with business and technical teams to align AI solutions with organizational objectives.
<br>
Create reusable assets, demos, and proof-of-concepts to support innovation and business development.<br>
<br>
<br>
</p></div></div><div><div><h2 class="jobSectionHeader"><b>Required Skills and Experience
</b></h2></div><div><p>A hands-on AI professional with strong expertise in Generative AI, Machine Learning, Python, Cloud Technologies, APIs, MLOps, Data Engineering, and AI Agents, capable of building and scaling enterprise-grade AI solutions from development to production.
<br>
Bachelor's degree or higher in AI, Machine Learning, Computer Science, or related field.
<br>
3-5 years of experience in AI, Machine Learning, Data Science, or Application Development.
<br>
Experience with production AI deployments, MLOps, model monitoring, and cloud platforms.</p>
<p>GPT, Claude LLM, Stable Diffusion
<br>
LangChain
<br>
TensorFlow
<br>
PyTorch
<br>
Scikit-learn
<br>
RAG (Retrieval-Augmented Generation)
<br>
AI Agents &amp; Agentic Workflows
<br>
API Development
<br>
REST APIs
<br>
JSON
<br>
HTTP Requests
<br>
Authentication &amp; Tokens
<br>
Error Handling &amp; Debugging<br>
<br>
<br>
<br>
<br>
</p></div></div></div><p>The base compensation range for this role in the posted location is $90,000- $160,000</p>
<p>Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law.</p>
<p>The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction.</p>
<p>These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity.</p>
<p>It is not typical for candidates to be hired at or near the top of the posted compensation range.</p>
<p>In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws.</p>
<p><b>Capgemini offers a comprehensive, non-negotiable benefits package to all regular, full-time employees.</b> In the U.S. and Canada, available benefits are determined by local policy and eligibility and may include:</p>
<ul><li>Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave</li>
</ul><ul><li>Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)</li>
<li>Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)</li>
<li>Life and disability insurance</li>
<li>Employee assistance programs</li>
<li>Other benefits as provided by local policy and eligibility</li>
</ul><p><b>Important Notice:</b> Compensation (including bonuses, commissions, or other forms of incentive pay) is not considered earned, vested, or payable until it becomes due under the terms of applicable plans or agreements and is subject to Capgemini&rsquo;s discretion, consistent with applicable laws. The Company reserves the right to amend or withdraw compensation programs at any time, within the limits of applicable legislation.</p>
<p><b>Disclaimers</b></p>
<p>Capgemini is an Equal Opportunity Employer encouraging inclusion in the workplace. Capgemini also participates in the Partnership Accreditation in Indigenous Relations (PAIR) program which supports meaningful engagement with Indigenous communities across Canada by promoting fairness, accessibility, inclusion and respect. We value the rich cultural heritage and contributions of Indigenous Peoples and actively work to create a welcoming and respectful environment. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law.</p>
<p>This is a general description of the Duties, Responsibilities and Qualifications required for this position. Physical, mental, sensory or environmental demands may be referenced in an attempt to communicate the manner in which this position traditionally is performed. Whenever necessary to provide individuals with disabilities an equal employment opportunity, Capgemini will consider reasonable accommodations that might involve varying job requirements and/or changing the way this job is performed, provided that such accommodation does not pose an undue hardship. Capgemini is committed to providing reasonable accommodation during our recruitment process. If you need assistance or accommodation, please reach out to your recruiting contact.</p>
<p>Please be aware that Capgemini may capture your image (video or screenshot) during the interview process and that image may be used for verification, including during the hiring and onboarding process.</p>
<p>Click the following link for more information on your rights as an Applicant in the United States. http://www.capgemini.com/resources/equal-employment-opportunity-is-the-law</p>
<p>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.</p></div></div></div></div>

Data Scientist/ AI Engineer

Occam Solutions

<div><p>Primary Responsibility</p><p></p><p><br>
The <b>DevSecOps Engineer</b> shall serve as the lead technical engineer responsible for designing, implementing, securing, automating, and sustaining the cloud infrastructure supporting the Office of Naval Research (ONR) Data &amp; Analytics environment. The DevSecOps Engineer shall lead the implementation and execution of the technical requirements identified in <b>PWS Section 4.5 &ndash; Technical Infrastructure and Platform Support</b>, including infrastructure automation, Continuous Integration/Continuous Deployment (CI/CD), cloud security, container orchestration, Infrastructure-as-Code (IaC), and operational sustainment of the FedRAMP High and DoD Impact Level 5 (IL5) cloud environment.</p><p></p><p><br>
The DevSecOps Engineer shall architect and maintain secure cloud infrastructure, automate deployment pipelines, support AI/ML platform integration, implement cloud-native security controls, monitor system health and security events, and ensure compliance with DoD cybersecurity requirements. This position will work closely with software developers, data engineers, cloud architects, cybersecurity personnel, and Government stakeholders to deliver resilient, scalable, and secure cloud services.</p><p></p><p><br>
Duties and Responsibilities</p>
<p>
The DevSecOps Engineer shall:</p>
<ul>
<li>Design, deploy, automate, and maintain secure cloud infrastructure supporting mission applications and analytics platforms.</li>
<li>Develop, implement, and manage automated CI/CD pipelines using tools such as Jenkins, GitLab CI/CD, or equivalent technologies.</li>
<li>Build and maintain Infrastructure-as-Code (IaC) solutions utilizing Terraform, Ansible, or similar automation frameworks.</li>
<li>Implement and manage containerized application environments using Docker, Kubernetes, or comparable orchestration platforms.</li>
<li>Configure, monitor, and optimize cloud-native security services including Web Application Firewalls (WAF), Intrusion Detection/Prevention Systems (IDS/IPS), endpoint protection, and Security Information and Event Management (SIEM) platforms.</li>
<li>Analyze security logs, audit events, vulnerability reports, and SIEM alerts to identify threats, recommend mitigation strategies, and support incident response activities.</li>
<li>Support continuous Authority to Operate (ATO) compliance through configuration management, security patching, vulnerability remediation, and security documentation.</li>
<li>Implement secure API management, identity and access management (IAM), secrets management, encryption, and least-privilege access controls.</li>
<li>Develop automated monitoring, logging, alerting, and operational dashboards to improve system reliability and performance.</li>
<li>Support deployment and operationalization of AI/ML platforms and services, including cloud-native machine learning environments.</li>
<li>Collaborate with software developers, data engineers, and cloud architects to integrate security throughout the software development lifecycle (SDLC).</li>
<li>Ensure cloud infrastructure complies with DoD cybersecurity policies, FedRAMP High requirements, DoD IL5 controls, and applicable security frameworks.</li>
<li>Support system scalability, resilience, disaster recovery, and operational continuity.</li>
<li>Document system architectures, deployment procedures, security configurations, and operational processes.</li></ul><br>
<p></p>
<p>
Minimum Qualifications</p>
<p>
The DevSecOps Engineer shall meet the requirements for the <b>&quot;DevSecOps Engineer &ndash; Intermediate&quot;</b> labor category as defined in the <b>CHESS ITES-3S</b> contract vehicle and possess the following additional qualifications:</p>
<ul>
<li>Seven (7) years of combined experience in DevSecOps, DevOps, SecOps, or MLOps engineering and development.</li>
<li>Five (5) years of hands-on experience designing, securing, implementing, and maintaining cloud environments, preferably within <b>AWS GovCloud</b>, <b>Azure Government</b>, or similarly regulated cloud environments.</li>
<li>Demonstrated experience supporting cloud environments operating under <b>FedRAMP High</b>, <b>DoD Impact Level 5 (IL5)</b>, or comparable federal security requirements.</li>
<li>Verifiable experience on at least two (2) projects designing, implementing, and managing Continuous Integration/Continuous Deployment (CI/CD) pipelines using Jenkins, GitLab CI/CD, Azure DevOps, GitHub Actions, or equivalent technologies.</li>
<li>Demonstrated expertise implementing and managing containerization and orchestration technologies, including Docker, Kubernetes, OpenShift, or similar platforms.</li>
<li>Hands-on experience implementing Infrastructure-as-Code (IaC) using Terraform, Ansible, CloudFormation, or comparable automation tools.</li>
<li>Experience configuring and administering cloud-native security capabilities including Web Application Firewalls (WAF), Intrusion Detection/Prevention Systems (IDS/IPS), Security Information and Event Management (SIEM), vulnerability management, and cloud security monitoring solutions.</li>
<li>Verifiable experience analyzing security logs, SIEM data, audit records, and threat intelligence to identify vulnerabilities, investigate security events, and implement corrective actions.</li>
<li>Experience implementing secure Identity and Access Management (IAM), encryption, secrets management, and Zero Trust security principles.</li>
<li>Experience supporting automated monitoring, logging, system performance optimization, and operational sustainment of enterprise cloud platforms.</li>
<li>Strong understanding of DevSecOps best practices, secure software development lifecycle (SSDLC), and cloud security architectures.</li><br>
</ul>
<p>Qualifications</p>
<ul>
<li>Active Secret security clearance (required).</li>
<li>Current DoD 8140 (or legacy DoD 8570) cybersecurity certification applicable to privileged access functions (e.g., Security+, CySA+, CASP+, CISSP, CCSP).</li>
<li>AWS Certified DevOps Engineer &ndash; Professional, AWS Security Specialty, Azure DevOps Engineer Expert, Certified Kubernetes Administrator (CKA), or comparable cloud certifications.</li>
<li>Experience supporting AI/ML environments, including AWS SageMaker, Azure Machine Learning, or similar platforms.</li>
<li>Experience with streaming data technologies such as Apache Kafka, AWS Kinesis, or equivalent event-driven architectures.</li>
<li>Familiarity with NIST RMF, DISA STIGs, FedRAMP, Zero Trust Architecture, and DoD cybersecurity compliance requirements.</li></ul></div>

DevSecOps Engineer

Huron Consulting Group

<div><p>Huron is redefining what a global consulting organization can be. Advancing new ideas every day to build even stronger clients, individuals and communities. We&rsquo;re helping our clients find new ways to drive growth, enhance business performance and sustain leadership in the markets they serve. And, we&rsquo;re developing strategies and implementing solutions that enable the transformative change they need to own their future.<br>
<br>
As a member of the Huron corporate team, you&rsquo;ll help to evolve our business model to stay ahead of market forces, industry trends and client needs. Our accounting, finance, human resources, IT, legal, marketing and facilities management professionals work collaboratively to support Huron&rsquo;s collective strategies and enable real transformation to produce sustainable business results.<br>
<br>
Join our team and create your future.</p><p></p>
This person is a shared engineer who builds and runs the platform every pod reuses: the pipelines, testing, deployment and monitoring, plus reusable building blocks so each new pod starts further ahead.<p></p><h1 class="jobSectionHeader"><b>
Key Responsibilities</b></h1><ul><li><p><b>
Build and run the pipelines. </b>CI/CD with automated testing across the pods.</p></li><li><p><b>
Provision the environments. </b>Set up and run the environments the pods build in.</p></li><li><p><b>
Deploy and monitor. </b>The models and agents the pods put into production.</p></li><li><p><b>
Set the standards. </b>Technical and security standards every pod builds to.</p></li><li><p><b>
Build reusable parts. </b>So each new pod starts further ahead than the last.</p></li></ul><p></p><h1 class="jobSectionHeader"><b>
Preferred Qualifications</b></h1><ul><li><p>
Building internal developer platforms.</p></li><li><p>
AI testing and monitoring in production.</p></li><li><p>
Healthcare or higher education with data-security awareness (HIPAA, FERPA).</p></li><li><p>
AWS Machine Learning or DevOps, Azure AI-102, or Kubernetes (CKA).</p></li></ul><p></p><h1 class="jobSectionHeader"><b>
General Required Skills</b></h1><ul><li><p>
5 to 8 years in ML or AI platform work or MLOps, including 1 to 2 years leading.</p></li><li><p>
Hands-on running AI systems in production.</p></li><li><p>
AWS and Azure, Docker, Kubernetes, infrastructure as code.</p></li><li><p>
Strong Python.</p></li><li><p>
Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent experience.</p></li></ul><p></p><p>
The estimated base salary for this job is $140,000 - $170,000 USD. The range represents a good faith estimate of the range that Huron reasonably expects to pay for this job at the time of the job posting. The actual salary paid to an individual will vary based on multiple factors, including but not limited to specific skills or certifications, years of experience, market changes, and required travel. This job is also eligible to participate in Huron&rsquo;s annual incentive compensation program, which reflects Huron&rsquo;s pay for performance philosophy. Inclusive of annual incentive compensation opportunity, the total estimated compensation range for this job is $161,000 - $218,750 USD. The job is also eligible to participate in Huron&rsquo;s benefit plans which include medical, dental and vision coverage and other wellness programs. The salary range information provided is in accordance with applicable state and local laws regarding salary transparency that are currently in effect and may be implemented in the future.</p><p></p><p>
#LI-REMOTE</p><p>
#LI-CL1</p><p></p><h2 class="jobSectionHeader"><b>
Position Level</b></h2>
Manager<p></p><h2 class="jobSectionHeader"><b>
Country</b></h2>
United States of America</div>

Platform Engineer, AI Capability Center Hub

<div><div><div><div><div><div><div><h2 class="jobSectionHeader"><b>About Capgemini</b></h2>
</div><div><p>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. The Group reported 2024 global revenues of &euro;22.1 billion.</p>
</div></div><div><div><h2 class="jobSectionHeader"><b>Job Description:</b></h2>
</div><div><p>We're looking for a passionate and experienced AI Platform Engineering Senior Consultant to join our vibrant team! As an AI Platform Engineering Senior Consultant, you will lead the design, implementation, and modernization of enterprise AI platforms supporting Generative AI, Machine Learning, Advanced Analytics, and Intelligent Automation initiatives. You will help customers build secure, scalable, and production-ready AI platforms leveraging NVIDIA AI Enterprise, Kubernetes, OpenShift, VMware, cloud services, and automation technologies across hybrid and multi-cloud environments.</p>
</div></div><div><div><h2 class="jobSectionHeader"><b>Key Responsibilities</b></h2>
</div><div><ul><li>Lead the architecture, design, and deployment of enterprise AI and Generative AI platforms across cloud, on-premises, and hybrid environments.</li>
<li>Design AI-ready infrastructure leveraging NVIDIA AI Enterprise, GPU technologies, Kubernetes, OpenShift, VMware, and cloud-native services.</li>
<li>Develop AI platform strategies, target-state architectures, governance frameworks, and modernization roadmaps.</li>
<li>Enable enterprise AI use cases including LLMs, RAG, AI Agents, MLOps, LLMOps, and Intelligent Automation platforms.</li>
<li>Implement Infrastructure-as-Code, GitOps, Terraform, Ansible, CI/CD, and automation frameworks to drive platform standardization and operational excellence.</li>
<li>Integrate AI platforms with ServiceNow ITSM, ITOM, CMDB, Change Management, and operational workflows.</li>
<li>Establish monitoring, observability, governance, security, and lifecycle management capabilities for AI platforms.</li>
<li>Lead customer workshops, architecture reviews, executive briefings, solution development, and mentoring activities.</li>
</ul></div></div><div><div><h2 class="jobSectionHeader"><b>Qualifications:</b></h2>
</div><div><ul><li>Bachelor&rsquo;s degree in Computer Science, Engineering, Information Technology, or a related field.</li>
<li>Minimum 10 years of experience in Platform Engineering, Cloud Architecture, Infrastructure Engineering, AI Platforms, or Technology Consulting.</li>
<li>Minimum 5 years of experience designing and operating enterprise AI, Machine Learning, Data Science, or Advanced Analytics platforms.</li>
<li>Outstanding English communication, both verbal and non-verbal.</li>
<li>Strong expertise in NVIDIA AI Enterprise, GPU-accelerated infrastructure, AI platform architecture, MLOps, and LLMOps.</li>
<li>Deep knowledge of Kubernetes and container platforms including Red Hat OpenShift, AKS, Amazon EKS, GKE, and VMware Tanzu.</li>
<li>Hands-on experience with Terraform, Ansible, GitOps, CI/CD pipelines, and platform automation.</li>
<li>Experience with AI technologies such as PyTorch, TensorFlow, Hugging Face, LangChain, Vector Databases, and Generative AI ecosystems.</li>
<li>Strong understanding of Azure, AWS, and Google Cloud AI services and infrastructure platforms.</li>
<li>Experience integrating enterprise platforms with ServiceNow and implementing governance, security, and operational best practices.</li>
<li>Excellent consulting, stakeholder management, leadership, communication, and presentation skills.</li></ul></div></div></div></div></div></div></div>

AI Platform Engineering Senior Consultant

Envision Employment Solutions

<div><p><b>Envision Employment Solutions</b> is currently looking for a<b> Senior Data Scientist - AI &amp; ML Engineer </b>for one of our partners, a global leader in <b>consulting, digital transformation, technology and engineering services!</b></p><p></p><p><b>
About:</b></p><p>
The <b>Senior Data Scientist - AI &amp; ML Engineer </b>will be responsible for delivering end-to-end machine learning solutions, designing scalable AI architectures, and developing Agentic AI applications that drive intelligent automation and business value.</p><p></p><p><b>
Responsibilities:</b></p><p><b>
AI &amp; Machine Learning Development</b></p><ul><li>
Design, develop, and deploy AI and machine learning solutions that address business needs.
</li><li>Build and implement Agentic AI applications using modern AI frameworks.
</li><li>Lead the full machine learning lifecycle, including data preparation, feature engineering, model development, evaluation, deployment, and monitoring.
</li><li>Optimize models to improve accuracy, scalability, and performance in production environments.
</li><li>Implement model monitoring, drift detection, and retraining strategies.
</li></ul><p><b>Data Engineering &amp; MLOps</b></p><ul><li>
Design scalable data pipelines to support machine learning workloads.
</li><li>Collaborate with data engineers to ensure reliable data ingestion, transformation, and storage.
</li><li>Develop and maintain ML-focused CI/CD pipelines.
</li><li>Implement model versioning, automated testing, and deployment best practices.
</li><li>Support containerized and cloud-native ML deployments.
</li></ul><p><b>Cloud &amp; Solution Architecture</b></p><ul><li>
Design AI and ML solutions using cloud platforms such as Azure, AWS, or GCP.
</li><li>Architect scalable infrastructure to support enterprise AI applications.
</li><li>Recommend technology solutions aligned with business and technical requirements.
</li><li>Ensure AI solutions meet security, reliability, and performance standards.
</li></ul><p><b>Collaboration &amp; Leadership</b></p><ul><li>
Partner with business stakeholders to understand objectives and translate them into technical solutions.
</li><li>Collaborate with cross-functional teams throughout the solution lifecycle.
</li><li>Mentor junior data scientists and ML engineers.
</li><li>Promote best practices in machine learning, software engineering, and MLOps.
</li><li>Stay informed about emerging AI technologies and industry trends.</li></ul><br>
<b>
Requirements</b><br>
<ul><li>
6-10 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 and delivering AI solution architecture.</li><li>
Strong proficiency in Python, data science and machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) and agentic application development libraries (e.g. LangChain, LangGraph).</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 (e.g. Docker), and orchestration (Kubernetes).</li><li>
Strong command of Git and collaborative development workflows.</li><li>
Excellent written and verbal communication skills, capable of conveying complex concepts to a variety of audiences.</li><li>
Fluent in Arabic &amp; English, both spoken and written (German is a plus!)</li><li>
Able to listen to and understand customer needs and articulate them as technical requirements to the team.</li><li>
Strong problem-solving abilities and attention to detail.</li><li>
Strategic thinking with the ability to align data science initiatives with business objectives.</li><li>
Strong leadership and team management skills with the ability to inspire and motivate.</li><li>
Ability to work collaboratively in a team environment.</li><li>
Self-motivated with a strong growth mindset.</li></ul></div>

Senior Data Scientist - AI & ML Engineer

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Senior Data Scientist - AI & ML Engineer