ml infrastructure engineer

Google

<div><h3 class="jobSectionHeader"><b>Minimum qualifications:</b></h3><ul>
<li>Bachelor&rsquo;s degree or equivalent practical experience.<br>
</li>
<li>5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.</li>
<li>5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.<br>
</li>
<li>5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).</li></ul><br>
<h3 class="jobSectionHeader"><b>
Preferred qualifications:</b></h3><ul>
<li>Master&rsquo;s degree or PhD in Engineering, Computer Science, or a related technical field.<br>
</li>
<li>5 years of experience with data structures and algorithms.</li>
<li>3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.</li>
</ul><div><h2 class="jobSectionHeader"><b>About the job</b></h2><p>
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google&rsquo;s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.</p>
<p>
In this role, you will be at the forefront of applying Google's Generative AI to solve real-world issues for our customers. You will act as a specialized software engineer embedded with key accounts, working to build, deploy, and optimize sophisticated AI agents on our Customer Engagement Suite (CES) platform. You will bridge the gap between our core product developing teams and customer needs, directly accelerating customer success and shaping the future of our AI products.</p><p>
The Cloud Applied AI (AAI) powers business growth with Gemini Enterprise. Our portfolio includes Gemini Enterprise for Customer Experience (Shopping Agent, CX Agent Studio, Agent Assist, Vertex AI Search - Commerce, Customer Experience Insights), along with other vertical and domain packaged solutions. We enable high adoption and speed to value by building solutions that are quickly deployed, delivering new 0-to-1 capabilities with startup agility. Team members operate at the forefront of AI, collaborating directly with model builders with unprecedented speed. Join us to work on cutting-edge projects and shape the future of AI in a fast-paced, collaborative, and impactful environment.</p></div><div><h2 class="jobSectionHeader"><b>
Responsibilities</b></h2><ul>
<li>Partner directly with customers to understand their business issues. Design, co-develop, debug, and deploy custom conversational AI agents and solutions to accelerate their time to value.</li>
<li>Systematize learnings from customer engagements by creating reusable tools, building documentation and accelerators, and establishing best practices across the organization.</li>
<li>Act as a high-level problem solver, empowered to write bespoke code, develop custom tooling, and even contribute directly to the core product codebase to resolve critical customer issues.<br>
</li>
<li>Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies.<br>
</li>
</ul></div>Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.</div>

Software Engineer, Applied AI, Agent Building, Google Cloud Platform

Accenture

<div><p><b>YOU ARE</b></p><p>
As a hands-on Infrastructure architect, you are an early-career engineer who learns and grows while contributing hands-on to the AI and machine learning infrastructure that powers real-world applications. Under the guidance of senior architects and engineers, you'll develop practical skills in coding, testing, configuring, deploying, monitoring, and troubleshooting AI systems and the infrastructure they run on. Day to day, you'll write and test code and deployment scripts, help configure cloud and on-premises compute resources such as GPU clusters and distributed training environments, deploy AI systems and models into production, and support data pipelines that feed AI and ML workflows. You'll learn to monitor AI systems and infrastructure health across both InfraOps and MLOps disciplines, perform AI monitoring to track model and system performance, and troubleshoot issues across the computational stack with mentorship and support. This is a hands-on, learning-focused where you build expertise across modern tools and platforms &mdash; including container orchestration, model serving, CI/CD pipelines, InfraOps, MLOps, and AI monitoring &mdash; while making meaningful contributions to infrastructure that enables AI-driven busin</p><p>
ess outcomes.</p><p></p><p><b>
THE WORK</b></p><ul><li>
Write, test, and debug code and scripts for AI infrastructure tasks, including automation and tooling, under the guidance of senior engineers.</li><li>
Develop and maintain infrastructure and software deployment scripts to support reliable, repeatable releases of AI systems and models.</li><li>
Configure and provision compute resources across cloud and on-premises environments, including GPU clusters and distributed training setups.</li><li>
Deploy AI systems and machine learning models into production infrastructure, following established processes and best practices.</li><li>
Deploy data pipelines that feed AI and ML workflows, ensuring data is available, clean, and reliable.</li><li>
Assist with container orchestration and model serving, learning tools such as Docker, Kubernetes, and model deployment frameworks.</li><li>
Support and maintain CI/CD pipelines for automating the build, test, and deployment of AI infrastructure and applications.</li><li>
Monitor AI systems and infrastructure health across InfraOps and MLOps disciplines, tracking performance, reliability, and resource utilization.</li><li>
Perform AI monitoring to track model performance, detect drift or degradation, and surface issues for review.</li><li>
Troubleshoot and help resolve issues across the computational stack &mdash; hardware, networking, software, and models &mdash; with mentorship and support.</li><li>
Document configurations, processes, and procedures to maintain clear, repeatable, and shareable knowledge across the team.</li><li>
Collaborate with senior architects and engineers, participating in code reviews, team discussions, and knowledge-sharing sessions to grow technical skills.</li><li>
Apply security, cost-efficiency, and scalability best practices as you learn them, contributing to well-managed and responsible infrastructure.</li></ul></div>
<br>
<div><p><b>EDUCATION</b></p><ul><li>
Bachelor's Degree in Computer Science, Computer Engineering, related Engineering field</li></ul><p></p><p><b>
BASIC (REQUIRED) QUALIFICATION</b></p><ul><li>
Work or coursework experience with AI/ML or Computer engineering or Computer science.</li><li>
Coding, building, monitoring, troubleshooting applications of AI/ML models; selecting, designing and infrastructure for deploying and running them on premise or on public cloud.</li><li>
Strong understanding of AI and machine learning as a subject.</li><li>
Strong understanding of computing infrastructure a subject, preferred knowledge of AI infrastructure.</li><li>
Proficiency in programming languages such as Python, Java, or C++.</li></ul></div><p></p>

AI Infrastructure Architecture Analyst

<div><p><b>YOU ARE</b></p><p></p><p>
As a hands-on Infrastructure Architect, you are an experienced engineer with several years in infrastructure engineering who now takes on more complex, higher-impact work designing and optimizing the AI and machine learning infrastructure that powers real-world applications. Working alongside senior architects and engineers &mdash; and increasingly leading your own workstreams &mdash; you apply proven skills in coding, testing, configuring, deploying, monitoring, and troubleshooting AI systems and the infrastructure they run on. Day to day, you architect and optimize infrastructure components, write and review code and deployment scripts, design and tune cloud and on-premises compute resources such as GPU clusters and distributed training environments, deploy AI systems and models into production, and build and optimize data pipelines that feed AI and ML workflows. You optimize the computational stack for performance, cost, power, and scalability, monitor AI systems and infrastructure health across both InfraOps and MLOps disciplines, perform AI monitoring to track model and system performance, and independently troubleshoot and resolve complex issues across the stack. You also mentor junior engineers, contribute to architectural decisions, and help establish best practices. This is a hands-on, ownership-driven where you apply and deepen your expertise across modern tools and platforms &mdash; including container orchestration, model serving, CI/CD pipelines, InfraOps, MLOps, and AI monitoring &mdash; while making meaningful contributions to infrastructure that enables AI-driven business outcomes.</p><p></p><p><b>
THE WORK</b></p><ul><li>
Write, review, and debug code, scripts, and infrastructure-as-code for AI infrastructure, automation, and tooling, setting standards for quality across the team.</li><li>
Architect, configure, and provision compute resources across cloud and on-premises environments, including GPU clusters and distributed training setups, optimizing for performance and utilization.</li><li>
Design and maintain deployment automation and CI/CD pipelines to support reliable, repeatable releases of AI systems, models, and applications.</li><li>
Deploy AI systems, models, and data pipelines into production, defining and improving the processes and best practices others follow.</li><li>
Lead container orchestration and model serving using tools such as Docker, Kubernetes, and model deployment frameworks.</li><li>
Architect and optimize the computational stack for performance, power, cost, and scalability, balancing trade-offs against business goals.</li><li>
Evaluate and select tools, frameworks, and platforms, making recommendations that shape the infrastructure roadmap.</li><li>
Integrate AI models and systems into existing enterprise systems, ensuring interoperability, security, and regulatory compliance.</li><li>
Own AI monitoring and infrastructure health across InfraOps and MLOps, tracking performance, reliability, and utilization, and driving remediation.</li><li>
Independently troubleshoot and resolve complex issues across the computational stack &mdash; hardware, networking, software, and models &mdash; and lead root-cause analysis.</li><li>
Mentor junior engineers and lead code reviews, providing technical direction and supporting their growth.</li><li>
Define and document architecture standards, processes, and procedures, and apply security, cost-efficiency, and scalability best practices across the infrastructure.</li></ul></div>
<br>
<div><p><b>EDUCATION</b></p><ul><li>
Bachelor's Degree in Computer Science, Computer Engineering, related Engineering field</li></ul><p></p><p><b>
BASIC (REQUIRED) QUALIFICATION</b></p><ul><li>
Experienced with coding, building, monitoring, troubleshooting applications of AI/ML models; selecting, designing and infrastructure for deploying and running them on premise or on public cloud.</li><li>
Strong understanding of AI and machine learning as a subject.</li><li>
Strong understanding of computing infrastructure a subject, preferred knowledge of AI infrastructure.</li><li>
Proficient in programming languages such as Python, Java, or C++.</li><li>
Experience with data pipeline and workflow management tools (e.g., Apache Airflow, Kubeflow).</li><li>
Strong problem-solving skills and ability to work in a fast-paced environment.</li><li>
Excellent communication and collaboration skills.</li><li>
Experience in AI/ML infrastructure engineering or related roles on a hyperscaler platform for deploying large scale solutions.</li></ul></div>

AI Infrastructure Architecture Specialist

<div><p><b>YOU ARE</b></p><p>
As a Lead and Principal Infrastructure Architect, you own end-to-end responsibility for designing optimized compute infrastructure for large-scale AI and machine learning systems, including large-scale distributed training environments. You are the authority who translates business goals, SLAs, and client standards into infrastructure architectures that perform at scale while being deliberately engineered for cost-efficiency.</p><p></p><p>
Drawing on deep experience, you weigh multiple viable solutions for any given problem &mdash; across compute, networking, storage, orchestration, and model serving &mdash; and make rational, well-justified architectural decisions tailored to each client's situation, constraints, and standards. You architect and optimize the full computational stack for performance, power, cost, and scalability; design and tune large-scale GPU clusters and distributed training systems; and ensure infrastructure meets security, compliance, and regulatory requirements. As the recognized AI infrastructure expert in at least one hyperscaler cloud (such as AWS, Azure, or Google Cloud), you bring authoritative knowledge of that platform's AI/ML services, accelerators, networking, and cost levers, and apply it to deliver best-in-class solutions. Beyond design, you set technical direction and standards, lead and mentor engineers and architects, partner with clients and stakeholders to shape the infrastructure roadmap, and are ultimately accountable for delivering AI/ML infrastructure that meets business SLAs, controls cost, and scales to enterprise and frontier workloads.</p><p><b>
THE WORK</b></p><ul><li>
Own the end-to-end architecture and design of optimized compute infrastructure for large-scale AI/ML systems, including large-scale distributed training environments, from concept through delivery.</li><li>
Develop and evaluate architecture alternatives, weighing trade-offs across compute, networking, storage, orchestration, and model serving to make rational, well-justified decisions tailored to each client's situation and standards.</li><li>
Lead architecture assessments and reviews of existing and proposed environments, identifying gaps, risks, bottlenecks, and optimization opportunities, and recommending remediation.</li><li>
Drive architectural decision-making, documenting rationale, trade-offs, and assumptions so decisions are transparent, defensible, and aligned with business SLAs and standards.</li><li>
Define and maintain the AI infrastructure roadmap, planning capacity, scaling, and technology evolution in step with business and product goals.</li><li>
Architect and optimize the full computational stack for performance, power, cost, and scalability, ensuring infrastructure meets business SLAs while being deliberately engineered for cost-efficiency.</li><li>
Design and tune large-scale GPU clusters and distributed training systems, including accelerator selection, interconnect/networking, and storage for high-throughput training workloads.</li><li>
Serve as the authoritative AI infrastructure expert in at least one hyperscaler cloud (AWS, Azure, or GCP), applying deep knowledge of its AI/ML services, accelerators, networking, and cost levers.</li><li>
Design deployment, automation, and CI/CD strategies for reliable, repeatable, and scalable releases of AI systems, models, and data pipelines into production.</li><li>
Establish AI monitoring and observability strategy across InfraOps and MLOps, defining SLAs, SLOs, alerting, and performance/cost tracking, and driving continuous optimization.</li><li>
Integrate AI/ML systems into enterprise environments, ensuring interoperability, security, compliance, and adherence to regulatory and client standards.</li><li>
Lead capacity planning and cost modeling, forecasting compute needs and engineering cost-efficiency into the architecture without compromising performance.</li><li>
Collaborate with clients, stakeholders, and engineering teams to align infrastructure decisions with business outcomes, translating requirements into actionable architecture and standards.</li><li>
Set technical direction, standards, and best practices, mentoring engineers and architects and leading design and code reviews across the team.</li></ul></div>
<br>
<div><p><b>EDUCATION</b></p><ul><li>
Bachelor's Degree in Computer Science, Computer Engineering, related Engineering field</li></ul><p><b>
BASIC (REQUIRED) QUALIFICATION</b></p><ul><li>
Expertise in coding, building, monitoring, troubleshooting applications of AI/ML models; selecting, designing and infrastructure for deploying and running them on premise or on public cloud.</li><li>
Strong understanding of AI and machine learning as a subject.</li><li>
Strong understanding of computing infrastructure, preferred knowledge of AI infrastructure.</li><li>
Proficiency in programming languages such as Python, Java, or C++.</li><li>
Experience with data pipeline and workflow management tools (e.g., Apache Airflow, Kubeflow).</li><li>
Strong problem-solving skills and ability to work in a fast-paced environment.</li><li>
Excellent communication and collaboration skills.</li><li>
Experience in AI/ML infrastructure engineering or related roles on a hyperscaler platform for deploying large scale solutions.</li><li>
Proven experience in leading and managing AI projects and teams.</li><li>
Strong project management skills, with the ability to manage multiple projects simultaneously.</li><li>
Demonstrated experience in evaluating and selecting AI technologies and frameworks.</li><li>
Ability to work with cross-functional teams and drive project alignment.</li></ul></div>

AI Infrastructure Architecture Manager

C-Serv

<div><p>Most engineers get to use large language models. You will get to build the systems that run them in production, at scale, under real latency and reliability constraints, for one of the most recognised names in application security and traffic management.</p><p>
Our client is expanding its AI Core group and is looking for a Senior or Staff AI &amp; ML Engineer in the UK. This is hands-on, build-focused work at the centre of how the business turns generative AI from a prototype into a dependable product. You will design and ship multi-agent systems, retrieval-augmented generation pipelines, anomaly detection, fine-tuned models, and the real-time inference layer that serves them.</p><p>
If you want ownership over a serving stack rather than a single notebook, and you want your work measured by what holds up in production rather than what demos well, this is the seat.</p>
What you will own<ul><li>Building and shipping generative AI features end to end: from model selection and fine-tuning through to the inference path that serves them.</li><li>Designing multi-agent and RAG architectures, and anomaly detection, that are accurate, observable, and cost-aware at scale.</li><li>Owning the real-time inference layer on Triton and TensorRT, optimising for latency, throughput, and GPU efficiency.</li><li>Standing up the surrounding microservices in Python and FastAPI, containerised and orchestrated for reliability.</li><li>Setting the technical bar: making architecture decisions, raising code quality, and mentoring engineers around you.</li><li>Partnering with research and product teams to take ideas from experiment to a service customers depend on.</li></ul><b><br>
Requirements</b><ul><li><br>
5 to 10 years of relevant experience, including a proven track record as a technical lead who mentors others.</li><li>Strong, current Python engineering, with production services built and shipped (FastAPI or similar).</li><li>Genuine hands-on GenAI depth: LLMs, RAG, agentic or multi-agent workflows, anomaly detection, and fine-tuning (for example LoRA or PEFT).</li><li>Real-time inference experience with NVIDIA Triton and TensorRT, with real attention to latency, throughput, and cost.</li><li>A microservices mindset, with services built in Python and FastAPI, containerised and orchestrated for reliability.</li><li>Solid grounding in Docker and Kubernetes, and large-scale distributed systems on a major cloud.</li><li>Right to work in the UK. We welcome applications from all backgrounds and are committed to equal opportunity.</li></ul>
Nice to have<ul><li>Experience with vLLM or other serving frameworks alongside Triton and TensorRT.</li><li>Experience in security, networking, or other high-reliability domains.</li><li>Big-data tooling (Spark, Databricks, Snowflake) and modern MLOps practice.</li></ul>
What you will get<ul><li>A genuine build mandate inside an established AI Core team, not a proof-of-concept that never ships.</li><li>Fully remote working anywhere in the UK, built around delivery rather than presence.</li><li>Competitive salary, strong benefits, and clear scope to grow into staff and principal-level influence.</li><li>The backing of C-Serv throughout: a delivery partner that runs a real quality filter and looks after its people, end to end.</li></ul><b><br>
Benefits</b><ul><li><br>
Fully remote working anywhere in the UK, built around delivery rather than presence.</li><li>A clear path to grow into staff and principal-level technical influence.</li><li>Full support from C-Serv across the hiring process and beyond, with full-cycle accountability.</li><li>A values-led, woman-owned delivery partner built on empathy, integrity, collaboration, and growth.</li></ul></div>

AI/ML Engineer

Ciklum

<p></p><div><div><div><div><div><div><div><div><div><div><div><b>Ciklum</b> is looking for a <b>Expert Agentic AI Engineer</b> to join our team full-time in the UK .<br>
We are a custom product engineering company that supports both multinational organizations and scaling startups to solve their most complex business challenges. With a global team of over 4,000 highly skilled developers, consultants, analysts and product owners, we engineer technology that redefines industries and shapes the way people live.</div><p><b>
About the role:</b></p><p>
As a Expert Agentic AI Engineer, become a part of a cross-functional development team engineering experiences of tomorrow.</p><p><b>
Responsibilities:</b></p><ul><li>
Design, build, train, and deploy production-grade Machine Learning, Deep Learning, or Natural Language Processing (NLP) models</li><li>
Optimize ML models for enterprise-scale latency, throughput, and performance, ensuring flawless integration with data warehouses and automated workflow bots</li><li>
Partner with Data Engineers to engineer high-quality data pipelines, automated feature stores, and secure data preprocessing routines</li><li>
Establish and scale MLOps workflows to handle model lineage tracking, code versioning, automated validation gates, and continuous deployment pipelines</li><li>
Set the core technical direction for AI engineering across squads, evaluate emerging AI methodologies, and provide high-level mentorship to senior team members</li></ul><p><b>
Requirements:</b></p><ul><li>
5+ years (Senior) or 8+ years (Expert) of professional experience developing, shipping, and maintaining machine learning models in live production environments</li><li>
Agentic Frameworks: Hands-on experience building autonomous workflows, custom plugins, and orchestration logic using Microsoft Copilot Studio or semantic kernels</li><li>
Cloud AI Infrastructure: Proven experience deploying and fine-tuning generative AI models, prompt flow, and vector databases within Azure AI Foundry (formerly Azure AI Studio)</li><li>
Tool Integration: Strong background in connecting LLMs to external APIs, enterprise data sources, and robotic process automation (RPA) tools to enable autonomous action execution</li><li>
Elite proficiency in Python paired with extensive experience using core ML libraries and frameworks (such as PyTorch, TensorFlow, Scikit-Learn, or Hugging Face)</li><li>
Hands-on experience working with MLOps tracking tools and cloud-based AI infrastructure ecosystems (such as MLflow, Kubeflow, AWS SageMaker, or Azure ML)</li><li>
Strong computer science fundamentals, including advanced knowledge of data structures, algorithmic complexity, and clean code principles. a formal degree in Computer Science, Data Science, Mathematics, or a highly quantitative field is strongly preferred</li></ul><p><b>
What`s in it for you?</b></p><ul><li>
Strong community: Work alongside top professionals in a friendly, open-door environment</li><li>
Growth focus: Take on large-scale projects with a global impact and expand your expertise</li><li>
Tailored learning: Boost your skills with internal events (meetups, conferences, workshops), Udemy access, language courses, and company-paid certifications</li><li>
Endless opportunities: Explore diverse domains through internal mobility, finding the best fit to gain hands-on experience with cutting-edge technologies</li><li>
Care: Healthcare, Basic Life Insurance, Short and Long-term disability insurance according to the Company&rsquo;s Benefit Plans</li></ul><p><b>
About us:</b></p><p>
At Ciklum, we are always exploring innovations, empowering each other to achieve more, and engineering solutions that matter. With us, you&rsquo;ll work with cutting-edge technologies, contribute to impactful projects, and be part of a One Team culture that values collaboration and progress. In the UK, Ciklum is growing fast&mdash;inviting experienced professionals to lead digital transformation alongside Fortune 500 clients. Be part of a company where innovation and impact go hand in hand.</p><p><b>
Explore, empower, engineer with Ciklum!</b></p><p>
Interested already? We would love to get to know you! Submit your application. We can&rsquo;t wait to see you at Ciklum.</p><p>
#LI-VH1</p></div>
</div></div></div></div></div></div><div></div><div><div><div><div><div><div><p>REQUIRED SKILLS</p>
<div><ul><p>Agentic AI</p>
</ul></div></div></div></div></div></div></div><div></div><div><div><div><div><div><div><div>
</div></div></div></div></div></div></div><div><div><div><div><div><div><p>JOB INFO</p>
<div><div><div><ul><div><div>Job Identification</div>
<div>3619</div>
</div><div><div>Job Category</div>
<div>Intelligent Automation</div>
</div><div><div>Posting Date</div>
<div>07/03/2026, 02:34 AM</div>
</div><div><div>Locations</div>
<div> <div></div><div><div>London
</div></div>(Hybrid)</div></div></ul></div></div></div></div></div></div></div></div></div></div></div></div><p></p>

Expert Agentic AI Engineer

Few&Far

<div>
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  🚨
  <strong>
   Senior Data Engineer | Outside IR35 Contract | Up to &pound;525/day | London (1 day onsite)
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  I'm working with a growing business that's looking for a Senior Data Engineer to join on an initial 6-12 month contract.
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  💰
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   Up to &pound;525 per day
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  📍
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   London - 1 day per week onsite
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   Outside IR35
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   1-stage interview process
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  This is a fantastic opportunity to play a key role in modernising a cloud-based data platform while helping to future-proof the company's infrastructure and mentoring junior engineers as they continue to develop.
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  You'll be working on:
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  ✅ Modernising data lake &amp; data warehouse architecture
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  ✅ Designing, building and optimising ETL/ELT pipelines
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  ✅ Deploying and supporting production ML systems, including forecasting models, LLM-powered and agentic workflows
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  ✅ Enhancing an AWS-native data platform (S3, Redshift, RDS, Athena, SageMaker &amp; Lambda)
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  ✅ Writing high-performance SQL and production-quality Python
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  ✅ Improving platform reliability, monitoring and CI/CD
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  ✅ Collaborating with stakeholders to deliver scalable data solutions
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  ✅ Mentoring junior engineers through code reviews and best practices
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  <br/>
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  <strong>
   Tech Stack:
  </strong>
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  ☁️ AWS
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  🐍 Python
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  🗄️ SQL
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  🔄 Airflow (AWS)
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  📊 Looker (nice to have)
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  I'm keen to speak with Data Engineers who have:
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  ✔️ Strong experience building production-grade data pipelines
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  ✔️ Advanced SQL and Python skills
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  ✔️ Experience deploying and supporting ML models in production
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  ✔️ AWS cloud experience and a solid understanding of DevOps/platform engineering
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  <br/>
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  If you're available soon or know someone who could be a great fit, please apply below or share this job post!
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Senior Data Engineer

Explore 278+ Ml infrastructure engineer in London this July. Find roles across top companies and apply now to boost your career.