machine learning platform engineer

BV Teck

<div>Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.<br>
<br>
<b>Job Title: </b>AI Platform Engineer<br>
<b>Location: </b>100% Remote (Continental United States)<br>
<b>Position Type: </b>Full-time, Direct W2<br>
<b>Salary Range:</b> $100,000 &ndash; $150,000 per annum<br>
<b>Experience:</b> 6+ years<br>
<br>
<b>Sponsorship: </b>U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.<p><b><br>
Job Summary:</b><br>
We are seeking an <b>AI Platform Engineer</b> to design, build, and operate scalable AI inference platforms for production ML workloads. The ideal candidate will have expertise in distributed systems, LLM serving, GPU optimization, autoscaling, and cloud-native infrastructure, with a strong focus on performance, reliability, and observability.</p><p><b>
Key Responsibilities:</b></p><ul><li>
Design and maintain scalable AI model serving platforms.</li><li>
Optimize inference performance, GPU utilization, and request routing.</li><li>
Build autoscaling, deployment, and monitoring solutions.</li><li>
Implement caching, security, and high-availability strategies.</li><li>
Collaborate with ML teams to deploy and support production AI models.</li></ul><p><b>
Required Qualifications:</b></p><ul><li>
6+ years of experience in distributed systems, infrastructure, or ML platform engineering.</li><li>
Strong proficiency in Python and Go, Rust, or C++.</li><li>
Experience with LLM inference frameworks (vLLM, TensorRT-LLM), Kubernetes, cloud platforms, and GPU optimization.</li></ul><p><b>
Preferred Qualifications:</b></p><p><b>
Bright Vision Technologies</b> is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.</p><h2 class="jobSectionHeader"><b>
Job Title</b></h2><p><b>
AI Platform Engineer</b></p><p><b>
Location:</b> 100% Remote (Continental United States)<br>
<b>Position Type:</b> Full-time, Direct W2<br>
<b>Salary Range:</b> $130,000&ndash;$180,000 Annually <i>(based on experience)</i><br>
<b>Experience Required:</b> <b>10+ Years</b></p><p><b>
Sponsorship:</b> U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.</p><h2 class="jobSectionHeader"><b>
Job Summary</b></h2><p>
Bright Vision Technologies is seeking a highly experienced <b>AI Platform Engineer</b> with <b>10+ years of experience</b> in distributed systems, cloud-native infrastructure, and AI platform engineering to design, build, and operate enterprise-scale AI inference and machine learning platforms. The ideal candidate will possess deep expertise in <b>LLM serving, GPU optimization, Kubernetes, cloud infrastructure, distributed systems, and MLOps</b>, with a proven ability to deliver highly scalable, reliable, secure, and cost-efficient AI platforms supporting production machine learning workloads.</p><h2 class="jobSectionHeader"><b>
Key Responsibilities</b></h2><ul><li>
Design, build, and maintain scalable AI inference and model-serving platforms for enterprise production environments.</li><li>
Architect highly available, cloud-native infrastructure supporting Large Language Models (LLMs), foundation models, and machine learning services.</li><li>
Optimize inference latency, throughput, GPU utilization, memory management, and request scheduling across distributed AI workloads.</li><li>
Design autoscaling, workload orchestration, traffic management, and intelligent request routing strategies for AI services.</li><li>
Implement model deployment, versioning, rollback, and lifecycle management using modern MLOps practices.</li><li>
Develop monitoring, observability, logging, distributed tracing, and alerting solutions to ensure platform reliability and performance.</li><li>
Implement caching strategies, API gateways, security controls, authentication, authorization, and high-availability architectures.</li><li>
Collaborate with AI researchers, ML engineers, DevOps teams, and software engineers to deploy and support production AI models.</li><li>
Drive cloud infrastructure optimization, resource utilization, FinOps initiatives, and operational excellence.</li><li>
Mentor engineering teams, conduct architecture reviews, and establish best practices for AI platform engineering and cloud-native development.</li><li>
Evaluate emerging AI infrastructure technologies, model-serving frameworks, and GPU acceleration techniques to drive continuous innovation.</li></ul><h2 class="jobSectionHeader"><b>
Required Qualifications</b></h2><ul><li>
Bachelor's or Master's degree in Computer Science, Computer Engineering, Artificial Intelligence, or a related technical discipline.</li><li><b>
10+ years of professional experience</b> in distributed systems, infrastructure engineering, cloud platforms, or machine learning platform engineering.</li><li>
Strong programming skills in <b>Python</b> and at least one systems programming language such as <b>Go, Rust, or C++</b>.</li><li>
Extensive experience with <b>Large Language Model (LLM) serving</b>, model inference optimization, and production AI infrastructure.</li><li>
Hands-on experience with <b>vLLM, TensorRT-LLM, Triton Inference Server, Ray Serve, or similar AI serving frameworks</b>.</li><li>
Strong expertise in <b>Kubernetes</b>, container orchestration, Docker, and cloud-native application architectures.</li><li>
Experience optimizing GPU workloads using <b>CUDA</b>, NVIDIA GPU technologies, distributed inference, and high-performance AI infrastructure.</li><li>
Experience with cloud platforms including <b>AWS, Microsoft Azure, or Google Cloud Platform (GCP)</b>.</li><li>
Strong understanding of distributed systems, networking, scalability, observability, and security best practices.</li><li>
Excellent analytical, communication, collaboration, and technical leadership skills.</li></ul><h2 class="jobSectionHeader"><b>
Preferred Qualifications</b></h2><ul><li>
Experience designing and operating <b>multi-region AI platforms</b> and globally distributed inference services.</li><li>
Knowledge of model optimization techniques such as <b>quantization, pruning, compression, speculative decoding, KV cache optimization, and mixed-precision inference</b>.</li><li>
Experience with <b>MLOps</b>, GitOps, Infrastructure as Code (Terraform, Bicep, CloudFormation), and CI/CD automation.</li><li>
Familiarity with service mesh technologies such as <b>Istio</b> or <b>Linkerd</b>, API gateways, and event-driven architectures.</li><li>
Contributions to open-source AI infrastructure projects, technical publications, patents, or conference presentations.</li><li>
Experience implementing <b>FinOps</b> strategies, cloud cost optimization, and enterprise AI governance.</li></ul><ul><li>
Experience with multi-region AI deployments and AI infrastructure.</li><li>
Familiarity with model optimization techniques such as quantization or compression.</li><li>
Open-source contributions or experience supporting large-scale AI APIs.</li></ul><p><br>
Interested in this opportunity? <b>Apply today for immediate consideration!</b></p><p>
Email your updated resume to <b>jaya@bvteck.com</b><br>
Call or Text <b>(908) 505-3545</b> if you have any questions.<br>
Learn more about Bright Vision Technologies at <b>www.bvteck.com</b>.</p><p>
We look forward to connecting with talented professionals and helping you take the next step in your career.</p><p><b>
Bright Vision Technologies is an Equal Opportunity Employer.</b></p><p>
Equal Employment Opportunity (EEO) Statement</p>
<p>Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.</p>
<p>BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.</p><p>
</p><p>qoHe1qeyqy</p></div>

AI Platform Engineer

Sainsbury’s

<div><div><h1 class="jobSectionHeader"><b>ML / AI Platform Engineer
</b></h1></div><h4 class="jobSectionHeader"><b>Salary: </b><b>Competitive Plus Benefits</b><br>
<b>Location: </b><b>London Store Support Centre and Home, London, EC1M 6HA
</b><br>
<b>Contract type: </b><b>Permanent</b><br>
<b>Business area: </b><b>Sainsbury's Tech</b><br>
<b>Closing date: </b><b>02 September 2026</b><br>
<b>Requisition ID: </b><b>400078171</b></h4><p></p><div><div><p>We&rsquo;d all like amazing work to do, and real work-life balance. That&rsquo;s waiting for you at Sainsbury&rsquo;s. Think about the scale it takes for us to feed the nation. The level of data, transactions and variety it involves. Then you&rsquo;ll realise that ours is a modern software engineering environment because it has to be. We&rsquo;ve made serious investment into a Tech Academy and into setting standards and principles. We iterate, learn, experiment and push ways of working such as Agile, Scrum and XP. So you can look forward to awesome opportunities in everything from AI to reusable tech.</p>
</div></div><div><div><p><b>Why join us</b></p><p>
At Sainsbury's, we're investing in shared AI and machine learning platforms that enable teams across the business to build, deploy and operate intelligent solutions safely, securely and at scale.</p><p>
You'll help shape the engineering foundations that power machine learning, generative AI and agentic applications across the organisation. Working as part of a platform engineering team, you'll build reusable capabilities that make it easier for product teams, data scientists and engineers to deliver value while meeting our standards for security, reliability, governance and cost efficiency.</p><p>
Our platforms include:</p><ul><li>
Azure Machine Learning</li><li>
AI Gateway capabilities built on Azure API Management</li><li>
Shared patterns and tooling for agentic and generative AI solutions</li><li>
Core platform services covering identity, networking, observability, security, deployment and cost management</li></ul><p></p><p><br>
You don't need to be an AI or machine learning specialist. We're looking for strong engineers with cloud platform, infrastructure or DevOps experience who are excited to apply their skills to the next generation of AI-enabled products and services.</p><p></p><p><b>
What you'll do</b></p><ul><li>
Design, build and operate shared platform capabilities that enable teams to develop and deploy ML and AI solutions efficiently and securely</li><li>
Create reusable infrastructure, tooling and automation that improves the developer experience and reduces operational overhead</li><li>
Build and maintain cloud-native platform services covering deployment, networking, security, identity, monitoring and governance</li><li>
Collaborate with product teams, data scientists and ML/AI Ops engineers to understand requirements and provide scalable platform solutions</li><li>
Drive engineering excellence through automation, standardisation, observability and reliability practices</li><li>
Contribute to the evolution of our AI and ML platform strategy, ensuring solutions remain secure, resilient and cost-effective</li><li>
Troubleshoot complex technical issues across cloud services, infrastructure and applications</li><li>
Support and mentor engineers within the team, sharing knowledge and driving continuous improvement</li></ul><p></p><p><b>
Who you are</b></p><p><b>
Essential skills and experience</b></p><ul><li>
Experience building, operating or improving cloud platforms in <b>AWS, Azure or GCP</b></li><li>
Strong understanding of <b>infrastructure as code</b></li><li>
Strong programming skills, with experience developing production-quality solutions in <b>Python</b> or other modern programming languages</li><li>
Experience with <b>CI/CD pipelines</b>, Git-based workflows and modern software delivery practices</li><li>
Knowledge of cloud networking, identity management, security and access controls</li><li>
Experience managing containerised workloads and cloud-hosted application platforms</li><li>
Strong troubleshooting and problem-solving skills across infrastructure and application layers</li><li>
Experience building reusable platform capabilities and self-service engineering solutions</li><li>
Ability to lead technical initiatives and take ownership of delivery outcomes</li><li>
Strong collaboration and communication skills, with a willingness to coach and support others</li><li>
A growth mindset, with the ability to learn new technologies and apply sound engineering principles in unfamiliar domains</li></ul><p></p><p><b><br>
Desirable skills and experience</b></p><ul><li>
Experience with Microsoft Azure platform services</li><li>
Azure Machine Learning</li><li>
Azure API Management</li><li>
Azure OpenAI, Azure AI Foundry or related AI platforms</li><li>
Platform Engineering and Developer Experience practices</li><li>
MLOps, model deployment and model lifecycle management</li><li>
Experience operating platforms in large-scale enterprise environments</li><li>
Knowledge of generative AI architectures, AI gateways, agent frameworks or agentic delivery patterns</li></ul><p></p><p><b>
What success looks like</b></p><ul><li>
Engineers can quickly and safely build and deploy ML and AI solutions using shared platform capabilities</li><li>
Platform services are secure, reliable, observable and cost-effective</li><li>
Reusable patterns and automation reduce duplication across teams</li><li>
Platform adoption grows through a great developer experience and strong engineering partnerships</li><li>
AI and ML workloads can be operated confidently at enterprise scale</li></ul><p></p><p><b><br>
Benefits</b></p><ul><li>
Generous colleague discount across Sainsbury&rsquo;s, Argos, and Habitat</li><li>
Competitive holiday allowance</li><li>
Bonus scheme</li><li>
Pension plan</li><li>
Flexible / smarter working policy</li><li>
Access to special offers on gyms, restaurants, holidays, retail vouchers and more</li></ul>
</div></div><p></p><div><div><p>We are committed to being a truly inclusive retailer, so you&rsquo;ll be welcomed whoever you are and wherever you work. Around here, there&rsquo;s always the chance to try something new - whether that&rsquo;s as part of an evolving team or somewhere else across the business - and we take development seriously and promise to support you. We also recognise and celebrate colleagues when they go the extra mile and, where possible, offer flexible working. When you join our team, we&rsquo;ll also offer you an amazing range of benefits. Here are some of them:<br>
<br>
 Starting off with colleague discount, you'll be able to get 10% off at Sainsbury's, Argos, TU and Habitat after 4 weeks. This increases to 15% off at Sainsbury&rsquo;s every Friday and Saturday and 15% off at Argos every pay day. We've also got you covered for your future with our pensions scheme and life cover. You'll also be able to share in our success as you may be eligible for a performance-related bonus of up to 10% of salary, depending on how we perform.<br>
<br>
 Your wellbeing is important to us too. You'll receive an annual holiday allowance, and you can buy additional holiday. We also offer other benefits that will help your money go further such as season ticket loans, cycle to work scheme, health cash plans, pay advance (where you can access some of your pay before pay day) as well access to a great range of discounts from hundreds of other retailers. And if you ever need it there is also an employee assistance programme.<br>
<br>
 Moments that matter are as important to us as they are to you which is why we give up to 26 weeks&rsquo; pay for maternity or adoption leave and up to 4 weeks&rsquo; pay for paternity leave.<br>
<br>
 Please see www.sainsburys.jobs for a range of our benefits (note, length of service and eligibility criteria may apply).</p></div></div></div><p></p>

ML / AI Platform Engineer

Sainsbury's

<div>
 We&rsquo;d all like amazing work to do, and real work-life balance. That&rsquo;s waiting for you at Sainsbury&rsquo;s. Think about the scale it takes for us to feed the nation. The level of data, transactions and variety it involves. Then you&rsquo;ll realise that ours is a modern software engineering environment because it has to be. We&rsquo;ve made serious investment into a Tech Academy and into setting standards and principles. We iterate, learn, experiment and push ways of working such as Agile, Scrum and XP. So you can look forward to awesome opportunities in everything from AI to reusable tech.
 <br/>
 <br/>
 <strong>
  Why join us
  <br/>
  <br/>
 </strong>
 At Sainsbury's, we're investing in shared AI and machine learning platforms that enable teams across the business to build, deploy and operate intelligent solutions safely, securely and at scale.
 <br/>
 <br/>
 You'll help shape the engineering foundations that power machine learning, generative AI and agentic applications across the organisation. Working as part of a platform engineering team, you'll build reusable capabilities that make it easier for product teams, data scientists and engineers to deliver value while meeting our standards for security, reliability, governance and cost efficiency.
 <br/>
 <br/>
 Our platforms include:
 <br/>
 <br/>
 <ul>
  <li>
   Azure Machine Learning
  </li>
  <li>
   AI Gateway capabilities built on Azure API Management
  </li>
  <li>
   Shared patterns and tooling for agentic and generative AI solutions
  </li>
  <li>
   Core platform services covering identity, networking, observability, security, deployment and cost management
   <br/>
   <br/>
  </li>
 </ul>
 You don't need to be an AI or machine learning specialist. We're looking for strong engineers with cloud platform, infrastructure or DevOps experience who are excited to apply their skills to the next generation of AI-enabled products and services.
 <br/>
 <br/>
 <strong>
  What You'll Do
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Design, build and operate shared platform capabilities that enable teams to develop and deploy ML and AI solutions efficiently and securely
  </li>
  <li>
   Create reusable infrastructure, tooling and automation that improves the developer experience and reduces operational overhead
  </li>
  <li>
   Build and maintain cloud-native platform services covering deployment, networking, security, identity, monitoring and governance
  </li>
  <li>
   Collaborate with product teams, data scientists and ML/AI Ops engineers to understand requirements and provide scalable platform solutions
  </li>
  <li>
   Drive engineering excellence through automation, standardisation, observability and reliability practices
  </li>
  <li>
   Contribute to the evolution of our AI and ML platform strategy, ensuring solutions remain secure, resilient and cost-effective
  </li>
  <li>
   Troubleshoot complex technical issues across cloud services, infrastructure and applications
  </li>
  <li>
   Support and mentor engineers within the team, sharing knowledge and driving continuous improvement
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Who You Are
  <br/>
  <br/>
 </strong>
 <strong>
  Essential skills and experience
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experience building, operating or improving cloud platforms in AWS, Azure or GCP
  </li>
  <li>
   Strong understanding of infrastructure as code
  </li>
  <li>
   Strong programming skills, with experience developing production-quality solutions in Python or other modern programming languages
  </li>
  <li>
   Experience with CI/CD pipelines, Git-based workflows and modern software delivery practices
  </li>
  <li>
   Knowledge of cloud networking, identity management, security and access controls
  </li>
  <li>
   Experience managing containerised workloads and cloud-hosted application platforms
  </li>
  <li>
   Strong troubleshooting and problem-solving skills across infrastructure and application layers
  </li>
  <li>
   Experience building reusable platform capabilities and self-service engineering solutions
  </li>
  <li>
   Ability to lead technical initiatives and take ownership of delivery outcomes
  </li>
  <li>
   Strong collaboration and communication skills, with a willingness to coach and support others
  </li>
  <li>
   A growth mindset, with the ability to learn new technologies and apply sound engineering principles in unfamiliar domains
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Desirable Skills And Experience
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experience with Microsoft Azure platform services
  </li>
  <li>
   Azure Machine Learning
  </li>
  <li>
   Azure API Management
  </li>
  <li>
   Azure OpenAI, Azure AI Foundry or related AI platforms
  </li>
  <li>
   Platform Engineering and Developer Experience practices
  </li>
  <li>
   MLOps, model deployment and model lifecycle management
  </li>
  <li>
   Experience operating platforms in large-scale enterprise environments
  </li>
  <li>
   Knowledge of generative AI architectures, AI gateways, agent frameworks or agentic delivery patterns
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  What Success Looks Like
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Engineers can quickly and safely build and deploy ML and AI solutions using shared platform capabilities
  </li>
  <li>
   Platform services are secure, reliable, observable and cost-effective
  </li>
  <li>
   Reusable patterns and automation reduce duplication across teams
  </li>
  <li>
   Platform adoption grows through a great developer experience and strong engineering partnerships
  </li>
  <li>
   AI and ML workloads can be operated confidently at enterprise scale
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Benefits
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Generous colleague discount across Sainsbury&rsquo;s, Argos, and Habitat
  </li>
  <li>
   Competitive holiday allowance
  </li>
  <li>
   Bonus scheme
  </li>
  <li>
   Pension plan
  </li>
  <li>
   Flexible / smarter working policy
  </li>
  <li>
   Access to special offers on gyms, restaurants, holidays, retail vouchers and more
   <br/>
   <br/>
  </li>
 </ul>
 We are committed to being a truly inclusive retailer, so you&rsquo;ll be welcomed whoever you are and wherever you work. Around here, there&rsquo;s always the chance to try something new - whether that&rsquo;s as part of an evolving team or somewhere else across the business - and we take development seriously and promise to support you. We also recognise and celebrate colleagues when they go the extra mile and, where possible, offer flexible working. When you join our team, we&rsquo;ll also offer you an amazing range of benefits. Here are some of them:
 <br/>
 <br/>
 Starting off with colleague discount, you'll be able to get 10% off at Sainsbury's, Argos, TU and Habitat after 4 weeks. This increases to 15% off at Sainsbury&rsquo;s every Friday and Saturday and 15% off at Argos every pay day. We've also got you covered for your future with our pensions scheme and life cover. You'll also be able to share in our success as you may be eligible for a performance-related bonus of up to 10% of salary, depending on how we perform.
 <br/>
 <br/>
 Your wellbeing is important to us too. You'll receive an annual holiday allowance, and you can buy additional holiday. We also offer other benefits that will help your money go further such as season ticket loans, cycle to work scheme, health cash plans, pay advance (where you can access some of your pay before pay day) as well access to a great range of discounts from hundreds of other retailers. And if you ever need it there is also an employee assistance programme.
 <br/>
 <br/>
 Moments that matter are as important to us as they are to you which is why we give up to 26 weeks&rsquo; pay for maternity or adoption leave and up to 4 weeks&rsquo; pay for paternity leave.
 <br/>
 <br/>
 Please see www.sainsburys.jobs for a range of our benefits (note, length of service and eligibility criteria may apply).
</div>


HCLTech

Bangalore, Karnataka
<br>Job Summary
<br>Data & MLOps Engineer (Transaction Monitoring)
<br>Role Purpose
<br><br>
Transaction Monitoring is evolving from traditional rule-based detection towards data-driven and AI-powered solutions that improve risk coverage, investigator effectiveness, and operational efficiency. We are looking for a Data & MLOps Engineer to help evolve our transaction monitoring analytics engineering and MLOps capabilities on Google Cloud Platform (GCP). You will design, build, and operate scalable data and machine learning solutions, while helping the team strengthen its GCP engineering expertise. The role combines Data Engineering, MLOps, and Cloud Engineering, ensuring that data products and machine learning models can be developed, deployed, monitored, and operated at scale.
<br><br>
Key Responsibilities
<br>Key Responsibilities
<br>Build and maintain data pipelines using SQL (on BigQuery, using dbt), Spark, Airflow.
<br>Develop cloud-native data solutions on GCP.
<br>Operationalise, monitor, and support machine learning models in production.
<br>Implement data quality, governance, lineage, and monitoring controls.
<br>Collaborate with Data Scientists and Engineers to deliver reliable, scalable solutions.
<br>Drive adoption of engineering best practices across Data Engineering and MLOps.
<br><br>
Act as a GCP champion and support the team's growth in cloud capabilities
<br><br>
Skill Requirements
<br>Required Skills
<br>Technical
<br>SQL
<br>dbt (Data Build Tool)
<br>BigQuery
<br>Apache Spark
<br>Apache Airflow
<br>Google Cloud Platform (GCP)
<br>Other Requirements
<br>Experience
<br>Data processing and large-scale data platforms
<br>Data governance and quality management
<br>Data quality monitoring and automated controls
<br>Machine learning and MLOps practices
<br>Operating and monitoring ML models in production
<br>Nice to Have
<br>Soda Data Quality
<br>Feature stores and model monitoring frameworks
<br>Financial Crime, AML, KYC, or Transaction Monitoring experience
<br>What Success Looks Like
<br>Reliable and scalable data pipelines running on GCP.
<br>Robust monitoring and data quality controls embedded in the platform.
<br>Machine learning models deployed and operated efficiently in production.
<br>Improved engineering standards and cloud adoption across the TM MLOps team(s)
<br>Increased GCP, Data Engineering, and MLOps maturity across the team through knowledge sharing, coaching, and establishment of best practices.
<br>What We're Looking For
<br>An engineer who combines strong Data Engineering and MLOps skills with hands-on GCP expertise. You enjoy building reliable, scalable solutions, improving data quality and governance, and helping the team adopt modern cloud and machine learning engineering practices.
<br><b>#body.unify div.unify-button-container .unify-apply-now:</b> focus, #body.unify div.unify-button-container .unify-apply-#body.unify div.unify-button-container .unify-apply-now: focus, #body.unify div.unify-button-container .unify-apply-

GCP Application Technical Specialist

Mastercard

<div><p><b>Our Purpose</b></p><p></p><p><i>
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we&rsquo;re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.</i></p><p></p><p><b>
Title and Summary</b></p><p></p>
Lead AI Engineer<p></p>
Who is Mastercard?<br>
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we're helping build a sustainable economy where everyone can prosper. We support a wide range of digital payment choices, making transactions secure, simple, smart, and accessible. Our technology and innovation, partnerships, and networks combine to deliver a unique set of products and services that help people, businesses, and governments realize their greatest potential.<br>
Our decency quotient (DQ) drives our culture and everything we do inside and outside our company. We cultivate an environment where individuals can thrive, collaborate, and contribute to innovations that power the global economy.<br>
Overview<br>
The AI Platform Engineering team is responsible for building, operating, and evolving Mastercard's enterprise AI platforms and capabilities. Our mission is to provide scalable, secure, and reliable AI infrastructure that enables teams across Mastercard to accelerate the development and deployment of AI-powered solutions.<br>
As a Lead AI Engineer, you will help design, implement, and operate the foundational platforms that support AI and machine learning workloads across the enterprise. You will work at the intersection of platform engineering, cloud infrastructure, MLOps, and AI operations to deliver enterprise-grade capabilities that enable teams to safely develop, deploy, observe, and scale AI solutions.<br>
This team functions as an enterprise AI Platform Engineering organization, delivering and operating shared AI capabilities that enable application teams to build AI-powered products at scale across public and private cloud environments.<br>
The Role<br>
As a Lead AI Engineer, AI Platform Engineering, you will contribute to the architecture, engineering, automation, and operational excellence of Mastercard's AI platforms. You will collaborate with software engineers, data scientists, infrastructure teams, security partners, governance organizations, and business stakeholders to build scalable AI services that support a growing portfolio of AI use cases.<br>
The ideal candidate combines strong software engineering and cloud platform expertise with experience supporting AI and machine learning systems in production environments. You are passionate about automation, reliability, customer enablement, operational excellence, and building platforms that empower others to innovate.<br>
Responsibilities<br>
Design, build, and operate enterprise AI platforms supporting machine learning, generative AI, and advanced analytics workloads.<br>
Engineer scalable solutions across public and private cloud environments, ensuring security, reliability, availability, and performance.<br>
Build and automate platform capabilities that simplify onboarding, deployment, operations, and lifecycle management for AI solutions.<br>
Develop and maintain infrastructure, tooling, and services that support model training, evaluation, deployment, monitoring, and governance.<br>
Implement and enhance MLOps capabilities that enable repeatable, scalable, and secure AI development workflows.<br>
Design and maintain observability solutions, including telemetry, performance monitoring, logging, alerting, operational analytics, and drift detection.<br>
Support production AI platforms and services, proactively identifying opportunities to improve reliability, scalability, efficiency, and customer experience.<br>
Partner with internal engineering teams to understand requirements, enable platform adoption, and accelerate delivery of AI-powered products.<br>
Collaborate with infrastructure, security, architecture, and governance teams to ensure alignment with enterprise standards, controls, and regulatory requirements.<br>
Evaluate emerging AI technologies, platform capabilities, and industry trends to help shape the future direction of Mastercard's AI ecosystem.<br>
Drive automation and engineering best practices through Infrastructure as Code, CI/CD, testing, and operational excellence initiatives.<br>
Participate in troubleshooting, root cause analysis, operational support, and incident response activities to maintain highly available platforms.<br>
Contribute to technical design discussions, architecture reviews, and long-term platform strategy.<br>
Mentor peers and share knowledge across engineering teams while contributing to a culture of continuous improvement.<br>
All About You<br>
Required Qualifications<br>
Experience designing, building, and operating cloud-native systems in enterprise environments.<br>
Strong experience working within both public and private cloud environments.<br>
Experience deploying and managing containerized workloads using Kubernetes or OpenShift.<br>
Strong software engineering and automation experience using Python.<br>
Experience implementing CI/CD pipelines and modern DevOps practices.<br>
Experience supporting production AI, machine learning, data platforms, or large-scale distributed systems.<br>
Strong understanding of MLOps principles and machine learning lifecycle management.<br>
Experience implementing monitoring, observability, telemetry, logging, and operational analytics solutions.<br>
Strong troubleshooting, analytical, and problem-solving skills.<br>
Ability to communicate effectively with technical and non-technical stakeholders.<br>
Experience working in highly collaborative, cross-functional engineering environments.<br>
Preferred Qualifications<br>
Experience supporting generative AI platforms and large language model (LLM) workloads.<br>
Experience implementing model evaluation, finetuning, guardrails, and model governance controls.<br>
Experience with model observability, drift detection, telemetry monitoring, and operational analytics.<br>
Experience with AI serving infrastructure and inference platforms.<br>
Experience supporting GPU-based workloads and accelerated computing environments.<br>
Experience with OpenShift, Kubernetes, Docker, Helm, GitOps, and Infrastructure as Code practices.<br>
Experience with enterprise-scale platform engineering, developer enablement, and self-service platform capabilities.<br>
Familiarity with vector databases, retrieval systems, AI gateways, agentic systems, or emerging AI platform technologies.<br>
Experience working within regulated environments requiring strong security, governance, and compliance controls.<br>
Key Capabilities<br>
The successful candidate will demonstrate proficiency in:<br>
Public and Private Cloud<br>
Model Building, Observability, and Scaling<br>
MLOps (Machine Learning Operations)<br>
Drift Detection and Telemetry Data Monitoring<br>
Model Evaluation, Finetuning, and Guardrails<br>
Deep Learning and Neural Networks<br>
Iterative Model Development<br>
Hyperparameter Tuning<br>
Reinforcement Learning and Feedback Loops<br>
Python<br>
What Makes This Role Unique<br>
This role provides the opportunity to influence and operate the foundational AI platforms that enable innovation across Mastercard. Rather than focusing solely on individual AI models or applications, you will help build and scale the enterprise platforms, tooling, operational practices, and cloud infrastructure that support the next generation of AI capabilities across the organization.<br>
You will work on challenging problems involving platform scalability, reliability, observability, governance, automation, and customer enablement while helping shape Mastercard's long-term AI platform strategy.<p></p><p><b>
Corporate Security Responsibility</b></p><p><br>
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:</p><ul><li><p>
Abide by Mastercard&rsquo;s security policies and practices;</p></li><li><p>
Ensure the confidentiality and integrity of the information being accessed;</p></li><li><p>
Report any suspected information security violation or breach, and</p></li><li><p>
Complete all periodic mandatory security trainings in accordance with Mastercard&rsquo;s guidelines.</p></li></ul></div><p></p>

Lead AI Engineer

GTR Railway

<ul><li><div>Salary:</div>
<div>Competitive</div>
</li><li><div>Location:</div>
<div>Monument Place</div>
</li><li><div>Contract Type:</div>
<div>Permanent</div>
</li><li><div>Closing Date:</div>
<div>11 September 2026</div></li></ul>
<br><div></div><div><div><div><div><div><div><div><div><div><h1 class="jobSectionHeader"><b>Data Engineer</b></h1>
<p><b>Location:</b> Hybrid, Monument Place
<br>
<b>Reporting to:</b> Platform Lead</p>
<p>Are you passionate about creating scalable data solutions that drive business insight, support operational decision making and enable the next generation of AI capabilities?</p>
<p>At GTR, we're looking for a talented <b>Data Engineer</b> to join our growing Data &amp; AI function and help shape the future of our enterprise DataHub platform.</p>
<b>What You'll Do</b>
<p>As a Data Engineer, you will design, build and maintain high-quality data models and pipelines within GTR's DataHub platform to enable trusted reporting, analytics and advanced machine learning use cases. You will work closely with analysts across the business to translate requirements into performant, well-governed data products that support both operational insight and future predictive work.</p>
<p>While the role is primarily technical, you will be expected to demonstrate sufficient business understanding to work effectively with subject matter experts, understand end-to-end use cases, and deliver data solutions that genuinely meet operational and decision-making needs.</p>
<h3 class="jobSectionHeader"><b>Key Responsibilities</b></h3>
<ul><li>Design, build and maintain curated data models within the DataHub platform, including Lakehouse, Warehouse and semantic models.</li>
<li>Develop, maintain and monitor ETL/ELT pipelines using Microsoft Fabric pipelines and associated tooling.</li>
<li>Work closely with analysts and subject matter experts to understand business requirements and translate them into robust data solutions.</li>
<li>Ensure data models are scalable, performant, well documented and aligned to agreed governance and architectural standards.</li>
<li>Contribute to data quality, continuous improvement initiatives and the development of data structures that support analytics, machine learning and AI workloads.</li><br>
</ul><p></p><p><b>What You'll Bring</b></p>
</div><div><ul><li>Experience delivering data engineering solutions within an enterprise or large-scale environment.</li>
<li>Strong understanding of data engineering principles, including data modelling, ETL/ELT development and pipeline orchestration.</li>
<li>Experience working with Microsoft Fabric, including Lakehouse, Warehouse, semantic models, notebooks and pipelines.</li>
<li>Strong SQL and data transformation skills.</li>
<li>Degree in Data Science, Computer Science, Statistics, Mathematics or a related discipline, or equivalent practical experience.</li>
</ul><h2 class="jobSectionHeader"><b>Desirable</b></h2>
<ul><li>Experience supporting reporting and analytics platforms such as Power BI.</li>
<li>Knowledge of Medallion Architecture (Bronze, Silver and Gold) and analytical data modelling patterns.</li>
<li>Understanding of how data platforms support advanced analytics, machine learning and AI use cases.</li>
<li>Experience working within formal governance, deployment and change management processes.</li>
<li>Strong stakeholder engagement skills with the ability to work effectively with both technical and non-technical audiences.</li><br>
</ul></div><p></p><div><br>
<h2 class="jobSectionHeader"><b>Why Join Us?</b></h2>
<p>This is an exciting opportunity to join GTR's growing Data &amp; AI function and help build the trusted data foundations that support reporting, analytics, machine learning and AI across the organisation.</p>
<p>You'll work with modern Microsoft technologies, collaborate with Data Architects, Data Scientists and AI Engineers, and play a key role in enabling data-driven decision making across one of the UK's largest train operating companies.</p>
<p>Our culture is built around being <b>Fair, Accountable, Collaborative, Transparent and Open-Minded</b>, while ensuring everyone feels <b>Valued, Respected and Safe</b>.</p>
<h2 class="jobSectionHeader"><b>Ready to Build the Future of Data at GTR?</b></h2>
<p>If you're passionate about data engineering, modern cloud technologies and delivering solutions that make a real impact, we'd love to hear from you. Join us and help power the data-driven future of GTR.</p><br>
</div><p></p><p><b>How to Apply</b></p>
<p>Please complete the application form and submit your CV.</p>
<p>We look forward to hearing from you and exploring whether GTR could be your next career step.</p>
<p><b>Please note that we reserve the right to close this vacancy earlier than the advertised closing date if we receive a high volume of suitable applications.</b></p></div></div></div><div></div><div><ul><br>
 </ul></div></div></div></div><div></div><div><div><div><h1 class="jobSectionHeader"><b>Benefits</b></h1>
</div></div><div><div><h3 class="jobSectionHeader"><b>Pension</b></h3>
</div><div><h3 class="jobSectionHeader"><b>Staff Travel</b></h3>
</div><div><h3 class="jobSectionHeader"><b>Childcare Vouchers</b></h3>
</div><div><h3 class="jobSectionHeader"><b>Extra Holidays</b></h3>
</div><div><h3 class="jobSectionHeader"><b>Discount Scheme</b></h3>
</div><div><h3 class="jobSectionHeader"><b>Long Service Awards</b></h3>
</div><div><h3 class="jobSectionHeader"><b>Employee Assistance Programme</b></h3>
</div><div><h3 class="jobSectionHeader"><b>The Transport Benevolent Fund</b></h3>
</div><div><h3 class="jobSectionHeader"><b>In-House Occupational Health Department</b></h3>
</div><div><h3 class="jobSectionHeader"><b>Cycle to Work Scheme</b></h3></div></div></div></div></div>

Data Engineer

Inception42

<div>
 <p>
  <strong>
   MLOps Engineer
  </strong>
 </p>
 <p>
  <strong>
   Location:
  </strong>
  Abu Dhabi, UAE
 </p>
 <p>
  <strong>
   Employment type:
  </strong>
  Full-time
 </p>
 <p>
  <br/>
 </p>
 <p>
  Inception42, a G42 company, is the region&rsquo;s leading innovator of AI-powered domain-specific as well as industry-agnostic products, built on a rich heritage of research and development. Within the G42 ecosystem, Inception42 functions as the core intelligence layer &ndash; transforming data and compute infrastructure into real-world, applied AI solutions. Beyond its commercial endeavors, Inception42 is committed to creating positive societal impact. For more information, please visit www.inceptionai.ai
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Overview
  </strong>
 </p>
 <p>
  We are looking for an MLOps Engineer to build and operate the systems that move machine learning from development into reliable production use. You will work across model training, validation, packaging, deployment, observability, and infrastructure automation, creating repeatable workflows that improve delivery speed without compromising security or control. The role works closely with data scientists, ML engineers, software engineers, architects, product teams, and platform teams in an environment where production quality, scale, and practical deployment matter.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   What You&rsquo;ll Own
  </strong>
 </p>
 <p>
  Design, build, and operate end-to-end ML pipelines covering data preparation, training, validation, packaging, release, deployment, and production monitoring.
 </p>
 <p>
  Build CI/CD workflows for machine learning systems, with automated testing, quality gates, environment promotion, release controls, rollback, and reproducible builds.
 </p>
 <p>
  Own model lifecycle controls across experiment tracking, model and dataset versioning, registries, approvals, deployment governance, lineage, and production-readiness checks.
 </p>
 <p>
  Deploy and operate model-serving and inference workloads across production and non-production environments using containerised, cloud-native platforms.
 </p>
 <p>
  Create reusable platform components, templates, standards, and paved paths that reduce manual work and help teams move models from research to production consistently.
 </p>
 <p>
  Instrument ML systems for model quality, data and concept drift, latency, throughput, availability, infrastructure health, logs, traces, alerts, and service-level performance.
 </p>
 <p>
  Automate infrastructure and environment provisioning through infrastructure as code, configuration management, and disciplined controls for access, secrets, networking, storage, and compute.
 </p>
 <p>
  Debug and optimise pipelines, model-serving paths, and platform services at component and system level to improve reliability, scalability, performance, and cost efficiency.
 </p>
 <p>
  Embed security, compliance, access control, auditability, and data-governance requirements into ML platforms, automated workflows, and deployment processes.
 </p>
 <p>
  Collaborate closely with data science, engineering, product, and platform teams to resolve production issues, improve developer experience, document operating practices, and evolve the MLOps capability.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   What We&rsquo;re Looking For
  </strong>
 </p>
 <p>
  Strong engineering fundamentals across software delivery, machine learning systems, cloud infrastructure, automation, reliability, and production operations.
 </p>
 <p>
  A track record of building and operating production ML or data platforms, with clear ownership of deployment quality, reliability, security, and operational outcomes.
 </p>
 <p>
  Hands-on experience building CI/CD pipelines and automation using Python and shell scripting or comparable languages and tooling.
 </p>
 <p>
  Practical experience with containers and orchestration, including Docker and Kubernetes, and the ability to diagnose issues across applications, clusters, networks, storage, and compute.
 </p>
 <p>
  Working knowledge of Azure services for ML and application workloads, such as Azure Machine Learning, Azure AI Foundry, AKS, Azure DevOps, storage, networking, and managed data services, or equivalent cloud platforms.
 </p>
 <p>
  Experience with ML lifecycle and workflow capabilities such as experiment tracking, model registries, feature and dataset versioning, orchestration, and model serving using MLflow, Kubeflow, Airflow, DVC, or equivalent tools.
 </p>
 <p>
  Strong observability and debugging skills across model behaviour, data quality, drift, inference performance, distributed services, and infrastructure health.
 </p>
 <p>
  Clear communication and product judgement, with the ability to work through ambiguity, make pragmatic trade-offs, and collaborate effectively across research, engineering, platform, and product teams.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Nice to Have
  </strong>
 </p>
 <p>
  Experience deploying generative AI or LLM workloads, including prompt and model versioning, evaluation, guardrails, GPU environments, or high-performance inference.
 </p>
 <p>
  Exposure to distributed data platforms, search systems, SQL or NoSQL databases, time-series storage, feature stores, or large-scale training environments.
 </p>
 <p>
  Experience with Terraform or Bicep and observability platforms such as Prometheus, Grafana, ELK, Datadog, or Langfuse.
 </p>
</div>


MLOps Engineer

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