machine learning engineer

software engineer

ai engineer

lead software engineer

platform engineer

mlops engineer

Hiscox

<div>
 <strong>
  Job Type
  <br/>
  <br/>
 </strong>
 Permanent
 <br/>
 <br/>
 <strong>
  Build a brilliant future with Hiscox
  <br/>
  <br/>
 </strong>
 <strong>
  Principal Machine Learning Engineer
  <br/>
  <br/>
 </strong>
 <strong>
  Location:
 </strong>
 London/York
 <br/>
 <br/>
 <strong>
  Why Hiscox London Market
  <br/>
  <br/>
 </strong>
 Hiscox London Market sits at the centre of global specialist insurance, tackling some of the most complex and unusual risks in the world. These are not commoditised problems, they demand deep expertise, strong judgement, and increasingly, sophisticated data and machine learning capabilities.
 <br/>
 <br/>
 We have a strong track record of putting AI into real production use, from augmenting underwriting decisions to shaping future market standards through partnerships and market‑first innovation. This is an environment where advanced ML systems are expected to operate reliably, safely, and at scale, not remain in experimentation.
 <br/>
 <br/>
 You&rsquo;ll join a culture that values technical excellence, ownership, and courage, where senior individual contributors are trusted to set direction, challenge thinking, and build platforms that matter. For a Principal Machine Learning Engineer, this is a chance to work on high‑impact ML systems, influence how AI is adopted across the London Market, and help shape the future of insurance.
 <br/>
 <br/>
 <strong>
  Role Purpose
  <br/>
  <br/>
 </strong>
 As a
 <strong>
  Principal Machine Learning Engineer (MLE)
 </strong>
 , you bring a wealth of experience in building, scaling, and operating production machine learning systems, and use that experience to provide deep technical leadership across machine learning engineering and MLOps.
 <br/>
 <br/>
 You play a key role in shaping the architectural strategy for production ML systems and the ML Platform, working closely with Data Science, Engineering, and Platform teams to define patterns, standards, and tooling that enable reliable, repeatable delivery at scale. Through hands‑on contribution, design leadership, and technical mentorship, you help teams navigate complex technical decisions and build robust, maintainable systems.
 <br/>
 <br/>
 A central focus of the role is enabling the organisation to move quickly without sacrificing quality, evolving the ML platform, supporting the transition from experimentation to production, and helping teams adopt modern engineering practices. This includes championing the effective and responsible use of AI‑assisted development tools as part of a broader approach to improving developer experience, system quality, and long‑term sustainability.
 <br/>
 <br/>
 Success in this role comes from the practical application of deep experience, strong architectural thinking, and the ability to help others build better systems that deliver real business value.
 <br/>
 <br/>
 <strong>
  Key Responsibilities
  <br/>
  <br/>
 </strong>
 <strong>
  Technical Leadership &amp; Ownership (Individual Contributor)
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Act as the technical lead for Machine Learning Engineering and MLOps across London Market.
  </li>
  <li>
   Technically lead the most complex and business‑critical ML systems, from architectural design through to production operation.
  </li>
  <li>
   Define and evolve production ML patterns and best practices, covering deployment, orchestration, monitoring, retraining, and decommissioning.
  </li>
  <li>
   Lead deep technical decision‑making, balancing scalability, reliability, security, and developer experience.
  </li>
  <li>
   Contribute hands‑on to critical systems, frameworks, and platform components where the complexity or impact demands it.
  </li>
  <li>
   Define best practices and guardrails for the use of AI‑assisted coding tools, ensuring they are used to improve productivity and code quality without compromising maintainability or operational safety.
  </li>
  <li>
   Lead by example in applying AI‑assisted development techniques (e.g. for prototyping, refactoring, and accelerating complex engineering work) with strong engineering judgement.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  ML Platform Strategy &amp; Build‑Out
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Partner closely with Group and Platform teams to design, build, and evolve the ML Platform, ensuring it supports:
  </li>
  <ul>
   <li>
    Reusable and scalable deployment patterns
   </li>
   <li>
    CI/CD for machine learning
   </li>
   <li>
    Full model lifecycle management
   </li>
   <li>
    Monitoring, observability, and alerting
   </li>
   <li>
    Secure and compliant operation
   </li>
  </ul>
  <li>
   Shape platform standards and interfaces that enable consistent ML delivery across squads and value streams.
  </li>
  <li>
   Lead technical spikes and proof‑of‑concepts to evaluate new tools, approaches, and architectural patterns.
  </li>
  <li>
   Influence developer tooling choices so that AI‑assisted coding tools integrate safely with CI/CD, testing, and governance workflows.
  </li>
  <li>
   Ensure the platform enables fast, safe experimentation while supporting robust, long‑lived production systems.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Governance, Reliability &amp; Commercial Impact
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Ensure ML systems meet architecture, security, compliance, and operational standards.
  </li>
  <li>
   Define and implement robust frameworks for monitoring technical health, model performance, and commercial impact of ML systems in production.
  </li>
  <li>
   Champion operational excellence across ML services, including monitoring, alerting, incident management, and post‑incident learning.
  </li>
  <li>
   Ensure clear ownership and lifecycle management for models and ML‑backed services.
  </li>
  <li>
   Promote responsible use of automation and AI‑assisted tooling in safety‑critical or regulated contexts.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Collaboration &amp; Influence
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Work closely with the Data Science team to ensure a smooth and repeatable transition from experimentation to production.
  </li>
  <li>
   Collaborate with software engineers, product managers, and business stakeholders to deliver end‑to‑end ML‑driven solutions.
  </li>
  <li>
   Act as a senior technical voice in design reviews, architecture forums, and strategic discussions.
  </li>
  <li>
   Influence technical direction and standards through expertise, credibility, and collaboration rather than line management.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Technical Mentorship &amp; Capability Development
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Provide hands‑on technical mentorship to Machine Learning Engineers and Data Scientists.
  </li>
  <li>
   Raise engineering standards by sharing best practices, patterns, and lessons learned from real production systems.
  </li>
  <li>
   Coach teams on the effective and critical use of AI‑assisted coding tools, reinforcing the importance of code review discipline, testing, and long‑term maintainability.
  </li>
  <li>
   Contribute to technical hiring, assessment, and onboarding from a senior engineering perspective.
  </li>
  <li>
   Help shape long‑term capability by identifying gaps in tooling, skills, and platform maturity.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  What You&rsquo;ll Bring
  <br/>
  <br/>
 </strong>
 <strong>
  Experience &amp; Background
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Engineering, or a related quantitative field (or equivalent experience).
  </li>
  <li>
   Extensive experience as a senior or principal Machine Learning Engineer delivering production ML systems at scale.
  </li>
  <li>
   Proven track record of owning or shaping ML platforms, MLOps frameworks, or critical ML infrastructure.
  </li>
  <li>
   Experience operating in complex, cross‑functional environments (insurance or financial services experience is advantageous but not essential).
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Technical Expertise
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Exceptional Python skills in a machine learning engineering context, with strong software engineering fundamentals (OOP, testing, design patterns).
  </li>
  <li>
   Deep experience building, deploying, and operating production ML systems, including:
  </li>
  <ul>
   <li>
    Online and batch model serving
   </li>
   <li>
    Monitoring, alerting, and observability
   </li>
   <li>
    Retraining and lifecycle management
   </li>
  </ul>
  <li>
   Strong understanding of core data science concepts, sufficient to:
  </li>
  <ul>
   <li>
    Review and challenge modelling approaches
   </li>
   <li>
    Ensure models are production‑ready and correctly evaluated
   </li>
  </ul>
  <li>
   Hands‑on experience with a major cloud platform (AWS, GCP, or Azure), including containerised deployments.
  </li>
  <li>
   Expert knowledge of MLOps and CI/CD, including:
  </li>
  <ul>
   <li>
    Git‑based workflows
   </li>
   <li>
    Infrastructure as Code (e.g. Terraform)
   </li>
   <li>
    Automated testing and deployment pipelines
   </li>
  </ul>
  <li>
   Strong operational mindset, including APIs, logging, monitoring, reliability, and incident response.
  </li>
  <li>
   Working knowledge of SQL and integration of ML services into wider data and application ecosystems.
  </li>
  <li>
   Experience using AI‑assisted coding tools in a production engineering context, with a clear understanding of their benefits, limitations, and risks.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Why Join Us?
  <br/>
  <br/>
 </strong>
 This is an opportunity to shape the future of machine learning engineering at Hiscox, build a high-performing sub-chapter, and influence strategic decisions, while staying close to the craft you love. You&rsquo;ll have the autonomy to set standards, mentor talent, and explore emerging technologies, all within a collaborative and forward-thinking environment.
 <br/>
 <br/>
 <em>
  Work with amazing people and be part of a unique culture
 </em>
</div>


Principal Machine Learning Engineer

Talan

<div><b>Company Description</b><p><br>
Talan Data x AI is a leading Data Management and Analytics consultancy, working closely with leading software vendors and top industry experts across a range of sectors, unlocking value and insight from their data. At Talan Data x AI, innovation is at the heart of our client offerings, and we help companies to further improve their efficiency with modern processes and technologies, such as Machine Learning (ML) and Artificial Intelligence (AI).</p><b><br>
Job Description</b><p><br>
As our AI practice scales, we are seeking an experienced AI Architect / Engineer to act as the senior technical authority shaping how we design and deliver next-generation AI solutions. This is a role for someone who has moved beyond building individual models to architecting entire systems: agentic platforms, LLM-powered applications, and the production infrastructure that makes them dependable at enterprise scale.</p><p>
You will sit at the intersection of strategy and delivery, operating across multiple client engagements simultaneously. In the early phases you will shape solutions, validate technical approaches, and translate ambiguous business problems into robust, deployable architectures. As engagements mature, you will provide the technical oversight that keeps delivery teams aligned to the original design intent. Your influence will be felt in pre-sales conversations, solution blueprints, and the technical standards that define how our practice builds.</p><p>
This is a hands on architectural role, not a purely advisory one. We expect you to remain close to the code, to prototype where it accelerates a decision, and to set a credible technical example for the engineers and data scientists you guide.</p><p><b>
Required skills &amp; experience:</b></p><ul><li>
Lead end-to-end solution architecture for AI and LLM-based systems, spanning data, integration, infrastructure, and deployment.</li><li>
Design agentic architectures and multi-agent orchestration patterns, defining how LLMs, tools, APIs, and retrieval layers interact reliably.</li><li>
Shape early-stage engagements (scoping, solutioning, estimation, and pre-sales) turning client ambition into deliverable technical plans.</li><li>
Provide technical validation and quality oversight across several concurrent projects, mitigating risk before it reaches delivery.</li><li>
Define LLM integration and prompting strategies, including RAG, fine-tuning, evaluation, and guardrailing approaches.</li><li>
Establish MLOps and LLMOps practices that ensure solutions are observable, testable, and production ready.</li><li>
Set architectural standards, reusable patterns, and reference implementations that raise the quality bar across the practice.</li><li>
Partner with Responsible AI colleagues to embed safety, security, and governance into solution designs from the outset.</li></ul><br>
<b>
Qualifications</b><p><b><br>
To qualify for this role, you must have:
</b></p><ul><li>Minimum 5 years in AI architecture or AI engineering, ideally 10+, with a track record of production deployments.</li><li>
Deep expertise in LLMs, agent frameworks (e.g. LangGraph, AutoGen, Semantic Kernel), API design, and orchestration.</li><li>
Strong cloud engineering experience.</li><li>
Proven MLOps / LLMOps capability &mdash; CI/CD for models, monitoring, evaluation, and cost optimisation.</li><li>
Hands-on software engineering background (Python and modern tooling) underpinning your architectural decisions.</li><li>
Comfort managing multiple engagements at once, with pragmatic judgement on trade-offs and delivery risk.</li></ul><p><b>
Ideally, you&rsquo;ll also have, but not essential:</b></p><ul><li>
Experience in consulting, professional services, or pre-sales environments.</li><li>
Exposure to enterprise-scale, regulated AI deployments (financial services, public sector, healthcare).</li><li>
Familiarity with vector databases, knowledge graphs, and advanced retrieval techniques.</li><li>
Relevant cloud or AI certifications.</li></ul><p><b>
You must be:</b></p><ul><li>
Willing to work on client sites, potentially for extended periods.</li><li>
Willing to travel for work purposes and be happy to stay away from home for extended periods.</li><li>
Eligible to work in the UK without restriction.</li></ul><p></p><p><br>
#TalanUK #LI-HB1</p><b><br>
Additional Information</b><p><b><br>
Compensation &amp; Benefits Package:</b></p><ul><li>
BDP Plus &ndash; A reward programme whereby you accrue points to trade against a 3-month paid sabbatical or cash equivalent.</li><li>
25 days holiday + bank holidays.</li><li>
5 days holiday buy/sell option.</li><li>
Private medical insurance.</li><li>
Life cover.</li><li>
Cycle to work scheme.</li><li>
Eligibility for company pension scheme (5% employer contribution, salary sacrifice option).</li><li>
Employee assistance programme.</li><li>
Bespoke online learning via Udemy for Business.</li></ul><p><b>
Why join:</b></p><p>
You will be a founding technical voice in a practice that is investing decisively in AI. The work is varied, intellectually demanding, and commercially significant, you will shape solutions that reach production, not slideware. You will have the autonomy to define how we build, the support of a collaborative multidisciplinary team, and a clear platform to grow into a practice-leading technical figure.</p></div>

AI Architect / Engineer (Agentic AI & LLM Architecture)

Xantura

<div><h3 class="jobSectionHeader"><b>Description</b></h3><div><br>
In this role you will work in the Platform team &ndash; a function for the deployment and evolution of the backend platform that underpins the core of the Xantura business.</div><br>
<h3 class="jobSectionHeader"><b>
Key Responsibilities</b></h3><div><br>
As an MLOps Engineer, you'll own the infrastructure layer that all of Xantura's AI services depend on to operate and scale. You'll be responsible for how ML models and NLP systems are deployed, monitored, and maintained across a growing base of local authority clients, ensuring that what works for one client works reliably for a hundred.<br>
</div><div>
<br>
You'll work across every pillar of the AI function - predictive modelling, text analytics, knowledge representation, and agentic AI - building the deployment pipelines, orchestration, and observability that enable the rest of the engineering team to ship with confidence.<br>
</div><div>
<br>
<b>Key responsibilities</b><br>
</div><ul><li>Continuously evolve the platform infrastructure powering all AI services (predictive modelling, NLP, knowledge representation, and agentic AI), ensuring reliable, scalable operation across a growing base of local authority clients.</li>
<li>Deploy and manage ML models via Azure ML endpoints, batch endpoints , and AKS, enabling resilient, secure model hosting that accelerates client onboarding and ensures models remain performant and monitorable throughout their lifecycle.</li>
<li>Ensure all ML systems are transparent, explainable, and auditable, aligned with Responsible AI principles and UK GDPR; essential where AI outputs inform decisions about vulnerable people in health and social care.</li>
<li>Design, build, and maintain production-grade orchestration pipelines (Dagster) supporting model training, inference, and retraining, ensuring data from local authority systems is timely, accurate, and fit for purpose before it reaches ML services.</li>
<li>Contribute to organisation-wide AI capability building, sharing best practice with delivery and consulting teams, advising on technical feasibility, and shaping governance standards as the AI function scales.</li></ul><div><br>
</div><br>
<h3 class="jobSectionHeader"><b>What are we looking for?<br>
</b></h3><br>
<div>We&rsquo;d love to hear from you if you have:</div><ul>
<li>Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience.</li>
<li>4+ years of professional experience in an MLOps, Platform Engineering, or Infrastructure Engineering role supporting ML or data-intensive systems.</li>
<li>Strong programming skills and production experience in Python.</li>
<li>Expertise in Azure-native MLOps, including model endpoints, pipelines, registries, environments, and compute management.</li>
</ul><div>Clear evidence of practical experience across the following:</div><ul>
<li>Deploying, scaling, and troubleshooting containerised workload on Kubernetes in production</li>
<li>Building and maintaining CI/CD pipelines (Azure DevOps or equivalent) for automated testing, building, and deployment of ML services</li>
<li>Implementing infrastructure-as-code (Terraform, Bicep or Pulumi)</li>
<li>Implementing monitoring and observability for production systems, including metrics, altering, logging, and dashboarding (e.g. Prometheus, Grafana)</li>
<li>Pipeline orchestration using Dagster, Airflow, Prefect, or similar</li>
</ul><div>
<br>
Bonus points if you have:</div><ul>
<li>Practical experience with model serving infrastructure &ndash; batch and/or real-time inference at scale.</li>
<li>Experience operating multi-tenant systems, particularly scaling infrastructure across multiple clients or business units.</li>
<li>Practical experience building and serving production-ready, asynchronous APIs for embedding and/or other compute-intensive services.</li>
<li>Experience setting up, and optimising vector databases, e.g. Qdrant, and integrating with other services</li>
<li>Proficiency in Python for building high-performance data and model pipelines, with strong software engineering discipline (testing, versioning, CI/CD).</li>
<li>Deep familiarity with the Azure ecosystem (Azure Kubernetes Service, Azure Container Registry, Azure DevOps, Azure Blob Storage, Azure Monitor, Azure Key Vault).</li>
</ul><div>
<br>
<b>Location &ndash;</b> This is a hybrid role based in our office in London (Borough). You would be expected to be able to work from the office at least 1-2 days per week. Some travel is also required for on-site client engagements as needed.</div><h3 class="jobSectionHeader"><b><br>
What can we offer you?</b></h3><br>
<ul>
<li>
Competitive salary reviewed annually</li>
<li>Work for a passionate, mission-driven company solving society&rsquo;s big problems</li>
<li>Work flexible hours around life commitments with a focus on delivering company value rather than hours worked</li>
<li>Ability to work remotely (excluding face-to-face Team Meetings and client meetings)</li>
<li>Training and development opportunities</li>
<li>25 days annual leave (plus bank holidays)</li>
<li>Company pension</li>
<li>Private medical insurance</li>
<li>Generous enhanced parental leave policies</li>
<li>Cycle to work scheme</li>
<li>Flu Vaccinations,</li>
<li>Eye Test and contribution towards Glasses for VDU use</li>
<li>Employee Assistance Programme<ul>
<li>Mental health and wellbeing support</li>
<li>Remote GP access</li>
<li>Counselling/therapy</li>
<li>Physiotherapy</li>
<li>Medical second opinions</li></ul><br>
</li>
</ul><br>
<h3 class="jobSectionHeader"><b>About Xantura</b></h3><div>
At Xantura, we&rsquo;re on a mission to reduce societal inequality by helping local authorities use data more effectively. Our AI-driven platform empowers frontline workers with the insights they need to prevent complex issues like homelessness or children being taken into care &mdash; before they happen. We make this possible by connecting siloed datasets, applying advanced machine learning to enrich the data, and using predictive analytics to identify those most at risk. Our platform then distills this into clear, actionable insights that help frontline staff intervene early and make a real difference.<br>
<br>
It&rsquo;s an exciting time to join Xantura. We&rsquo;re scaling quickly, bringing on new clients, strengthening our platform, and expanding into new areas. While we&rsquo;re a technology company at heart, our true focus is on improving lives &mdash; and we&rsquo;re looking for people who share that vision.</div></div>

Senior ML Ops Engineer

Moonpig

<div><div>We&rsquo;re the Moonpig Group &ndash; home to Moonpig, Greetz, Red Letter Days and Buyagift &ndash; and we&rsquo;re on a mission to make people feel loved, celebrated and remembered. Whether it&rsquo;s a card that gets them laughing out loud or a gift that makes their day, we help people stay close, no matter the miles.</div><div></div><div><br>
We&rsquo;re proud to be leading the online gifting revolution, with brilliant products, clever tech and a whole lot of heart. Our platform makes it easy to create moments that matter &ndash; packed with personal touches and delivered with care.</div><div></div><div><br>
We&rsquo;re not just about selling cards or gifts &ndash; we&rsquo;re here to spread joy, spark smiles and make every celebration feel extra special. And with values that guide how we work and support one another, we&rsquo;ve built a place where people (and ideas) can truly thrive.</div><div></div><div><br>
If you&rsquo;re looking to make an impact, bring your spark and be part of something meaningful &ndash; we&rsquo;d love to have you on the team. \uD83C\uDF19\uD83D\uDC37</div><br>
<div></div>
<h3 class="jobSectionHeader"><b>
About the Role</b></h3><div>
<p>
We're looking for an MLOps Engineer to join Moonpig's Data Platform team. In this role, you'll help build and scale the infrastructure that powers machine learning across the business. Working closely with data scientists, data engineers, software engineers, and stakeholders, you'll streamline the end-to-end machine learning lifecycle&mdash;from experimentation and model development through to deployment, monitoring, and continuous improvement.</p>
<div>
<div><div><p>As part of the ML Ops team, you'll play a key role in enabling innovation, personalisation, and data-driven decision-making across Moonpig. This is an opportunity to work with modern cloud technologies, shape scalable ML platforms, and make a direct impact on how machine learning is delivered in production.</p>
</div>
</div></div></div><h3 class="jobSectionHeader"><b>Key Responsibilities</b></h3><div>
<ul><li>Evaluate, integrate, and implement MLOps tools and frameworks to improve the efficiency and reliability of machine learning operations.</li>
<li>Design, implement, and manage CI/CD pipelines for deploying machine learning models into production environments.</li>
<li>Build and maintain infrastructure supporting data pipelines, model training, and model serving using cloud-native technologies and infrastructure-as-code practices.</li>
<li>Optimise machine learning workflows for performance, scalability, resource utilisation, distributed processing, and GPU acceleration.</li>
<li>Implement monitoring solutions to track model performance, identify anomalies, and support automated retraining processes.</li>
<li>Develop automated workflows for model testing, validation, and deployment, integrating with CI/CD tooling.</li>
<li>Partner with data scientists, data engineers, and software engineers to streamline the journey from experimentation to production.</li>
<li>Ensure security best practices are followed, including access control, data privacy, and compliance requirements.</li>
<li>Contribute to the ongoing evolution of the data platform, identifying opportunities to improve productivity, reliability, and scalability.</li>
<li>Build strong relationships across teams and support the adoption of data and machine learning best practices.</li>
</ul></div><h3 class="jobSectionHeader"><b>About You</b></h3><div>
<div><div><ul><li>Strong experience writing clean, maintainable, and production-ready Python code.</li>
<li>Proven ability to build scalable applications, data workflows, and automated solutions.</li>
<li>Experience working with machine learning pipelines and platforms such as AWS SageMaker or similar technologies.</li>
<li>Strong understanding of cloud-native services and experience designing, deploying, and operating applications within AWS or comparable cloud environments.</li>
<li>Comfortable working in agile environments, balancing technical quality with pragmatic delivery.</li>
<li>Curiosity and enthusiasm for learning new technologies and improving engineering practices.</li>
<li>Ability to collaborate effectively with a range of technical and non-technical stakeholders.</li>
<li>Strong problem-solving skills and a focus on building reliable, scalable solutions.</li>
</ul></div></div></div><h3 class="jobSectionHeader"><b>Our Tech Environment</b></h3><div>
<div><div><div><ul><li>MLOps: Snowflake, SQL, Python, FastAPI, Metaplane, Grafana, GitHub Workflows.</li>
<li>Infrastructure: AWS (SageMaker, ECS, Lambda, Glue, S3), Terraform, API Gateway.</li>
<li>Collaboration: GitHub, Jira, Confluence.</li>
</ul>
<p>We don't expect you to have experience with every technology listed above. We're interested in engineers who are excited to learn, collaborate, and help us build scalable machine learning platforms that support the future of Moonpig.</p>
</div>
</div></div></div><h3 class="jobSectionHeader"><b>How We Get There</b></h3><div>
<div>
<div><div><p>At Moonpig, we believe great products are built by great teams. You'll work in a collaborative environment where learning is encouraged, ideas are welcomed, and engineering excellence matters. We value people who are curious, supportive, and motivated to make a meaningful impact through technology.</p>
<p><b>
Interview Process</b></p>
<ul>
<li>Stage 1: Recruiter Screening Call</li>
<li>Stage 2: Hiring Manager Interview</li>
<li>Stage 3: Technical Assessment Interview with Two Team Members</li>
<li>Offer! \uD83C\uDF89</li>
</ul>
<p>Our process may vary depending on role and availability. We keep candidates informed of any changes.</p>
</div>
</div></div></div><div><b>What's in it for you?</b></div><div></div><div><br>
We believe in empowering our team to do their best work. Enjoy:</div><div>
\uD83D\uDCB0 Competitive Pay &amp; Bonuses: Plus, generous pension plans &amp; staff discounts.</div><div>
\uD83D\uDC86\uD83C\uDFFD Wellbeing First: Private healthcare (UK), mental health support &amp; dog-friendly offices (London &amp; NL).</div><div>
\uD83C\uDFD6️ Flexible Working &amp; Time Off: Generous holidays, hybrid working (1-3 days in office, depending on role/team) &amp; up to 20 days of international working.</div><div>
\uD83D\uDCC8 Career Growth: Learning allowances, coaching &amp; development programs.</div><div></div><div><b><br>
Want to know more?</b></div><div>
Explore our full benefits package: here</div><div>
Check out our podcast, tech blog and product blog to hear more about how we work and what we're building!</div><div></div><div><b><br>
Our Ways of Working:</b></div><div>
We trust our colleagues to do what&rsquo;s right and offer flexibility to support a balance between work and life. At the same time, face-to-face office time is an important and expected part of working at Moonpig Group. We believe regular in-person working supports collaboration, alignment, and effective decision-making. Candidates will have regular and ongoing time working from the office as part of their role, which will be discussed during the recruitment process.</div><div></div><div><b><br>
Moonpig Group's Commitment to Equality, Diversity, and Inclusivity:</b></div><div>
At Moonpig Group, we&rsquo;re all about creating a workplace where everyone feels they truly belong. We celebrate what makes each of us unique, whether that&rsquo;s our background, how we work best, or what matters most to us.</div><div></div><div><br>
From working parents who need flexible hours to neurodiverse colleagues with specific working styles, we&rsquo;re here to support our people in ways that work for them. Because when you feel valued and included, you can thrive, and so can we.</div><div></div><div><br>
We&rsquo;re proud to have a number of employee-led groups driving this forward, including our LGBTQ+, Gender Balance, Neurodiversity and EMBRACE (Educating Myself for Better Racial Awareness and Cultural Enrichment) communities, plus our Group-wide EDI committee. These teams help make sure every voice is heard and every idea has a place.</div><div></div><div><br>
We know that diversity fuels creativity, innovation and connection, and that&rsquo;s why we&rsquo;ll keep pushing for progress. Together, we&rsquo;re building a culture where everyone feels safe, supported, and free to be their brilliant, authentic selves.</div><div></div><div><br>
If you have a preferred name, please use it to apply and share your pronouns if you are comfortable to do so\uD83D\uDE0A - If you have any reasonable adjustment requests throughout the interview process please let us know on your application or speak to the Recruiter.</div><div></div><p><br>
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.</p></div>

ML Ops Engineer

Worldpay

<div><p><b>Ready to take your career global?</b></p><p>
Make your mark at one of the biggest names in payments.</p><p>
We are seeking an experienced <b>Senior ML Software Engineer</b> to join our Data Science Enablement team. You will be the primary software engineering expert for a product area, working as part of a cross-functional agile team and owning the successful launches and ongoing operations of high-profile products in production. Your focus will be on driving new feature delivery, maintaining operational excellence, and collaborating on enhancements to our shared ML platform. You will work closely with cross-functional collaborators&mdash;including ML research engineers, product managers, and platform engineers&mdash;to deliver scalable, reliable, and low-latency ML solutions.</p><p>
In addition to technical expertise, this job requires strong collaboration skills.</p><p></p><p><b>
What you&rsquo;ll own</b></p><p>
Feature Development &amp; Delivery:</p><ul><li>
Design, implement, and deploy new features and enhancements to ML products, collaborating with Product and ML Research teams to refine requirements.</li></ul><p>
Technical Ownership and Stewardship:</p><ul><li>
Technical ownership of existing and new production ML products in your area, ensuring alignment of technical investments with business goals (in collaboration with a product owner) and engineering best practices.</li><li>
End-to-end technical stewardship of ML products, ensuring ongoing reliability and performance.</li></ul><p>
Contribute to ML platform:</p><ul><li>
Contribute to the evolution of the shared ML platform alongside other engineers to drive best practices and shared tooling across all products.</li></ul><p>
Operational Excellence:</p><ul><li>
Maintain and improve automated CI/CD pipelines, testing frameworks, and monitoring/logging, ensuring high operational standards.</li><li>
Conduct comprehensive code reviews to enforce coding standards, improve code quality, and share knowledge.</li></ul><p>
Continuous Improvement:</p><ul><li>
Identify and implement opportunities for process, tooling, and system improvements, proactively addressing technical debt and scaling challenges.</li></ul><p>
Release Management:</p><ul><li>
Oversee pre-release testing, coordinate releases and ensure smooth enablement of new features.</li></ul><p>
Leadership:</p><ul><li>
Provide technical guidance and support to other engineers and data scientists to solve complex technical challenges.</li><li>
Mentor and coach other engineers, supporting their professional growth.</li><li>
Foster a culture of collaboration, continuous improvement, and knowledge sharing.</li></ul><p>
Act Like an Owner:</p><ul><li>
Proactively identify and resolve blockers, navigate processes, and independently seek out information and connect with relevant teams to drive solutions in the face of ambiguity.</li><li>
Operate with a strong sense of urgency, consistently prioritizing and executing tasks to meet timelines and deliver results.</li></ul><p></p><p><b>
What you&rsquo;ll bring</b></p><ul><li>
5+ years as an ML-focused software engineer, ML Engineer, MLOps Engineer, or similar, with hands-on production experience</li><li>
Proven expertise with ML model deployment, API design, and integration into production environments</li><li>
Strong Python programming and relevant ML/data libraries</li><li>
Experience with containerization, orchestration, and AWS cloud services</li><li>
Building and operating CI/CD pipelines</li><li>
Monitoring, troubleshooting, and optimizing production ML systems</li><li>
Pre-release testing and release management</li><li>
Demonstrated ability to work independently, navigate ambiguity, and deliver results</li><li>
Excellent communication skills and ability to collaborate both within engineering organizations and on cross-functional teams</li><li>
Experience with OpenAPI, FastAPI, or similar</li></ul><p></p><p><b>
It&rsquo;s a bonus if you have</b></p><ul><li>
Experience with MLFlow, model versioning, and storage</li><li>
Familiarity with Databricks or similar platforms</li><li>
Experience supporting high-volume, real-time data products</li><li>
Automated testing and validation frameworks</li><li>
Experience designing and configuring low-latency databases to serve real-time features, such as DynamoDB</li><li>
Experience with Terraform Cloud</li><li>
Experience in large companies, mature engineering teams, and/or highly regulated industries</li><li>
AI-assisted coding experience (e.g. GitHub Copilot)</li></ul><p></p><p><b>
Why Join Us?</b></p><ul><li>
Impact: Play a key role as the technical owner of high-profile ML products delivering meaningful business impact to merchants and advancing key pillars of the company&rsquo;s strategy. Your work will directly influence the reliability, scalability, and evolution of critical production systems.</li><li>
Autonomy: Take end-to-end technical ownership of your product area, with the freedom and responsibility to drive technical solutions, shape best practices, and deliver results in a fast-paced, supportive environment.</li><li>
Collaboration: Join a cross-functional, high-performing team where your expertise is valued and your contributions make a real difference.</li></ul><p></p><p><b>
About the team</b></p><p>
Our inclusive and global teams win together every day. We&rsquo;re proud to have the best minds in the industry, whom you can learn from as you grow your career. The people, the energy, and the connections are unmatched. Come and be part of an ever-evolving company and get dynamic opportunities that transcend borders.</p><p></p><p><b>
What makes a Globalpayer?</b></p><p>
Globalpayers think like a client, act like an owner, and win as one team. We&rsquo;re curious and innovative &ndash; always finding better ways to deliver impact. We empower each other to make decisions, and our passion drives excellence in everything we set out to do.</p><p></p><p>
Does this sound like you? Then you sound like a Globalpayer. Apply now to take your career global.</p><p></p><p>
#LI-AB1</p></div>

Senior ML Software Engineer

Compare The Market

<div><p><b>Senior Machine Learning Engineer &ndash; Customer AI
</b></p><p><b>Function: </b>Data &amp; AI Solutions
<br>
<b>Location: </b>Hybrid, London office<br>
</p><p></p><p><b>Curious about what&rsquo;s next?
</b><br>
So are we. Join Compare the Market and help to make financial decision making a breeze for millions.
</p><p>At Compare the Market, we&rsquo;re a purpose-driven business powered by tech and AI. We&rsquo;re building high-performing, results-driven teams with the skills, mindset, and ambition to deliver outcomes at pace. Every role here plays a part in driving our mission forward, and we create an environment where you can bring your authentic self, grow a truly characterful career, and see the direct impact of your work on the lives of our customers.
</p><p>We&rsquo;ve carved a meerkat-shaped niche and we&rsquo;re looking for ambitious, curious thinkers who thrive in a fast-moving, high-impact environment. If you love accountability, embrace challenge, and want to make a real difference, you&rsquo;ll fit right in.<br>
</p><p></p><p><b>We&rsquo;d love you to be part of our journey.
</b></p><p>Compare the Market is building AI systems that connect directly to how millions of people in the UK find and buy financial products - and as a Senior Machine Learning Engineer, you will play a central role in making those systems production-ready. That means owning the engineering that takes ML models and AI capabilities from experimentation into reliable, scalable production use: the pipelines, the deployment tooling, the monitoring, and the shared components that make the whole thing work.
</p><p>This is a hands-on role with real technical scope. You will work closely with data scientists, engineers, and product teams - contributing to architecture decisions, raising engineering standards, and building the reusable tooling that raises the pace and quality of delivery across a fast-moving and expanding set of AI capabilities and systems.<br>
</p><p></p><p><b>Some of the great things you'll be doing:
</b></p><p><b>ML Engineering and AI Systems
</b></p><ul><li>Own the end-to-end delivery of production machine learning and AI solutions in collaboration with data scientists and product teams
</li><li>Design and build model pipelines for training, validation, and deployment using modern tooling (e.g. MLflow, Kubernetes)
</li><li>Contribute hands-on code to model packaging, deployment, and lifecycle automation
</li><li>Build systems that monitor model performance, drift, reliability and operational health in real time
</li><li>Support both batch and real-time ML workloads depending on use case requirements
</li>Work on emerging AI and LLM-powered capabilities, helping integrate modern AI techniques into production systems where they can deliver real user value<br>
<li>
</li></ul><p></p><p><b>Platform and Standards
</b></p><ul><li>Help evolve our internal ML and AI platform to support experimentation, governance, and collaboration
</li><li>Define and promote best practices for ML and AI system design, including reproducibility, testing, CI/CD, model and agent observability and evaluation
</li>Develop shared tools and libraries that accelerate safe, efficient, and scalable ML development<br>
<li>
</li></ul><p></p><p><b>Collaboration and Technical Leadership
</b></p><ul><li>Work closely with data scientists to productionise experimental models and turn prototypes into robust services
</li><li>Act as a technical mentor and code reviewer for other engineers and contributors
</li>Provide technical leadership across ML and AI initiatives, contributing to architecture discussions and design reviews<br>
<li>
</li></ul><p></p><p><b>Culture and Innovation
</b></p><ul><li>Contribute to a culture of engineering excellence, collaboration, and continuous learning
</li><li>Stay up to date on emerging tools and approaches in MLOps and applied AI, helping evaluate and adopt technologies where appropriate
</li>Support responsible AI practices, contributing to explainability, auditability, and fairness in ML systems<br>
<li>
</li></ul><p></p><p><b>What we'd like to see from you:
</b></p><p><b>The Essentials
</b></p><ul><li>Hands-on experience with LLM-based systems: prompt engineering, RAG, tool use, or orchestration frameworks such as LangGraph or LangChain
</li><li>Familiarity with multi-step AI patterns - building systems where models plan, retrieve information and take sequences of actions
</li><li>Strong experience deploying ML models into production in cloud-native environments
</li><li>Solid software engineering skills in Python, with experience building scalable services, APIs, and production-quality code
</li><li>Experience with modern ML tooling and platforms (e.g. Databricks, MLflow, Airflow, Kubeflow, SageMaker, Vertex AI)
</li><li>Familiarity with CI/CD pipelines and infrastructure-as-code (e.g. Terraform, CloudFormation)
</li><li>Experience building robust, maintainable, and testable ML pipelines and APIs, including batch or real-time model delivery
</li><li>Strong understanding of ML lifecycle challenges - versioning, testing, monitoring, governance
</li>Excellent collaboration and communication skills, with experience working across data science, engineering, and product teams<br>
<li>
</li></ul><p></p><p><b>Nice to Have
</b></p><ul><li>Active personal use of AI-assisted or agentic coding tools, and an interest in how similar patterns could be applied to automate and accelerate ML engineering workflows - candidates who are exploring this space, even at an early stage, are encouraged to talk about it
</li><li>Experience in financial services, insurance or another regulated sector
</li><li>Experience deploying real-time or streaming ML models (e.g. Kafka, Flink, Spark Structured Streaming)
</li><li>Passion for automation, tooling, and building reusable systems
</li>Interest in responsible AI and ML model governance<br>
<li>
</li></ul><p></p><p><b>Why Compare the Market?
</b></p><p>We&rsquo;re a business built for pace and performance. Here, you&rsquo;ll be encouraged to think differently, act boldly, and deliver brilliantly in a culture that values results and rewards progress.
</p><p>We believe diverse teams make better decisions, and we&rsquo;re committed to creating an inclusive workplace where everyone feels empowered to grow, contribute, and thrive.
</p><p>If you&rsquo;re ready to stretch yourself, raise the bar, and grow with a team that&rsquo;s serious about performance, innovation, and purpose, we&rsquo;d love to hear from you.</p></div><p></p>

Senior Machine Learning Engineer - Customer AI

Ventula Consulting

<div>
 <p>
  <strong>
   AI / ML Engineer - AI Startup - London - &pound;80k - &pound;120k
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  A newly established Voice AI startup with multi-million funding, is looking to hire an AI / ML Engineer to focus on their ML pipelines which process very high-volume events at scale.
 </p>
 <p>
  <br/>
 </p>
 <p>
  Our client is looking for an individual with significant experience as either an AI Engineer or Machine Learning Engineer with clear evidence of shipping LLM powered ML products
 </p>
 <p>
  <br/>
 </p>
 <p>
  To be suitable for this role you will require the below:
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Key Requirements
  </strong>
 </p>
 <ul>
  <li>
   Bachelor&rsquo;s degree in Computer Science, Machine Learning, or a related field
  </li>
  <li>
   Excellent experience as a, ML, or AI Engineer, with evidence of shipping production systems
  </li>
  <li>
   Strong Python skills and experience with FastAPI
  </li>
  <li>
   Experience working modern MLOps tools, ideally Kubeflow
  </li>
  <li>
   Strong experience working closely with LLMs (including fine-tuning, RAG, prompt engineering)
  </li>
  <li>
   Strong problem-solving skills and ability to communicate technical trade-offs clearly
  </li>
  <li>
   Collaborative mindset with experience working across technical and non-technical teams
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <br/>
 </p>
 <p>
  This position is a permanent role, based in London, with 3-4 days a week in the company's office
 </p>
 <p>
  <br/>
 </p>
 <p>
  The role offers a salary between &pound;80,000 - &pound;120,000
 </p>
</div>


AI / ML Engineer

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