machine learning engineer

software engineer

ai engineer

senior software engineer

devops engineer

ai platform engineer

ClearRoute

<div>About Us
ClearRoute is an engineering consultancy bridging Quality Engineering, Cloud Platforms and Developer Experience. We help enterprises reliably bring high-impact digital products to market faster, cheaper, and safer, working with technology leaders facing complex business challenges.
We take as much pride in our people, culture and work-life balance as we do in making better software. We&rsquo;re not just making better software. We&rsquo;re making the making of software better. Collaborative, entrepreneurial and dedicated to problem solving, we bring the step change our customer need to sustain innovation. Our values challenge us to do the best we can for ClearRoute, our customers and most importantly our team. This is an opportunity for you to build the organisation from the ground up, use your voice to drive change and help transform organisations and problem domains.
The Role
We are looking for a Platform Engineer to join client-facing delivery teams and help design, build, and operate modern developer platforms. You will work across a range of industries and tech stacks, so adaptability matters as much as expertise.
On any given engagement you might be building a goldenpath CI/CD pipeline, hardening a Kubernetes cluster, migrating secrets management to Vault, or running a platform engineering workshop with a client's engineering teams. You will be expected to lead technical workstreams, pair with client engineers, and leave behind well-documented, production-ready infrastructure.
Increasingly, our clients are asking us to help them build the foundations for AI, from model-serving infrastructure and MLOps pipelines to safely integrating LLM powered tooling into existing developer workflows. You don't need to be an ML engineer, but you should be curious about this space and comfortable building the platform layer that makes AI workloads production ready.
What You'll Do
Platform &amp; Infrastructure<ul><li>Design and deliver internal developer platforms (IDPs) that improve developer experience and accelerate software delivery.</li><li>Build and maintain infrastructure-as-code using Terraform, Pulumi, or CDK and enforce code review and testing standards.</li><li>Manage and optimise Kubernetes clusters (EKS, GKE, AKS) including multi-tenancy, networking, RBAC, and cost controls.</li><li>Own CI/CD pipelines end-to-end: from source control policies through build, test, security scanning, artefact management, and deployment.</li><li>Implement secrets management and certificate lifecycle automation using HashiCorp Vault or equivalent.</li></ul>
Reliability &amp; Security<ul><li>Embed SRE practices: SLOs, error budgets, runbooks, on-call design, and blameless post-mortems.</li><li>Integrate security tooling (SAST, DAST, dependency scanning, policy-as-code) into delivery pipelines.</li><li>Design and test disaster-recovery strategies; automate them where possible.</li><li>Ensure compliance with client security standards and relevant regulatory frameworks.</li></ul>
AI &amp; Emerging Technology<ul><li>Design and operate infrastructure for AI/ML workloads: GPU node pools, model-serving runtimes (Triton, vLLM, BentoML), and vector database deployments (pgvector, Weaviate, Qdrant).</li><li>Build and maintain MLOps pipelines model training, versioning, evaluation, and promotion to production using platforms such as Kubeflow, MLflow, or cloud-native equivalents.</li><li>Integrate LLM APIs and AI agent frameworks into existing developer platforms, including prompt management, observability, cost controls, and rate-limit guardrails.</li><li>Advise clients on AI readiness: data infrastructure, governance, security controls (model access policies, output filtering), and the organisational changes that sit alongside the technical work.</li><li>Stay current with the fast-moving AI tooling landscape and bring relevant ideas back to the team and to clients.</li></ul>
Client Engagement<ul><li>Lead technical discovery sessions and platform assessments with client engineering and architecture teams.</li><li>Translate client requirements into clear technical plans and communicate trade-offs to both technical and non-technical stakeholders.</li><li>Coach and upskill client platform and application engineers through pairing, workshops, and code review.</li><li>Produce high-quality documentation, architecture decision records (ADRs), and runbooks that clients can own after the engagement.</li></ul>
What We're Looking For....
Essential<ul><li>Solid hands-on experience with at least one major cloud provider (AWS preferred; GCP or Azure accepted).</li><li>Production experience with Kubernetes and familiarity with the surrounding ecosystem (Helm, ArgoCD / Flux, Karpenter, Cilium, etc.).</li><li>Strong infrastructure-as-code skills, Terraform is the baseline; other tools are a bonus.</li><li>Practical experience designing or operating CI/CD systems (GitHub Actions, GitLab CI, Tekton, or similar).</li><li>Comfort working in Linux environments and writing automation in Python, Bash, or Go.</li><li>The ability to explain complex technical concepts clearly to a mixed audience.</li><li>A consulting mindset: you care about solving the client's actual problem, not just delivering a deliverable.</li></ul>
Ideal (not required)<ul><li>Hands-on experience with AI/ML infrastructure: GPU scheduling on Kubernetes, model-serving runtimes, or MLOps tooling (Kubeflow, MLflow, Ray, etc.).</li><li>Familiarity with LLM APIs (OpenAI, Anthropic, Bedrock, Vertex AI) and patterns for building reliable, observable AI-powered applications.</li><li>Experience with vector databases or semantic-search infrastructure.</li><li>Experience with event-streaming platforms such as Apache Kafka.</li><li>Familiarity with configuration management tools (Chef, Ansible, or similar).</li><li>Knowledge of mainframe environments or hybrid-cloud patterns.</li><li>Relevant certifications: CKA/CKAD, AWS Solutions Architect, HashiCorp Vault, etc.</li><li>Prior consultancy or client-facing delivery experience.</li></ul>
At ClearRoute, we believe diverse perspectives lead to better outcomes, and inclusion creates the conditions for everyone to thrive. We are proud to have built a family friendly working environment and have many employees who have caring responsibilities alongside work. We welcome applications from people who require flexibility and will be happy to discuss needs on an individual basis.
We are committed to fostering a culture where all team members feel respected, supported, and empowered to do their best work. We celebrate individuality and our differences and understand that some differences may mean that you require changes made to the interview process. We are happy to cater to your needs to make the interview accessible, if this is something you require please let us know by emailing us at join@clearroute.io</div>

AI Platform Engineer

Tata Consultancy Services

<div>
 
 
 
 If you need support in completing the application or if you require a different format of this document, please get in touch with
 
 
 
 at UKI.recruitment@tcs.com
 
 
 
 or call
 
 
 
 TCS London Office number 02031552100 / +44 204 520 2575
 
 
 
 with the subject line: &ldquo;Application Support Request&rdquo;.
 
 
 
 
 
 
 
 
 
 
 
 Job Title:
 
 GenAI Adoption &ndash; Platform Engineer
 
 
 
 Job Type:
 
 Permanent
 
 
 
 Location:
 
 London
 
 
 
 Hours:
 
 40 hours per week &ndash; Full time
 
 
 
 
 
 
 
 
 
 Ready to apply your
 
 data engineering expertise to build reliable and scalable pipelines for enterprise platforms?
 
 
 
 We have an exciting opportunity for you &mdash;
 
 GenAI Adoption &ndash; Platform Engineer!
 
 
 
 
 
 
 
 Careers at TCS: It means more
 
 
 
 TCS is a purpose-led transformation company, built on belief. We do not just help businesses to transform through technology. We support them in making a meaningful difference to the people and communities they serve - our clients include some of the biggest brands in the UK and worldwide. For you, it means more to make an impact that matters, through challenging projects which demand ambitious innovation and thought leadership.
 
 <ul>
 <li>
 Gain access to continuous learning opportunities in emerging AI technologies.
 </li>
 <li>
 Be part of a collaborative engineering team delivering cutting-edge solutions.
 </li>
 <li>
 Support major organisations as they transform their data and AI capabilities.
 </li>
 </ul>
 
 
 
 
 
 
 
 
 The Role
 
 
 
 As a
 
 GenAI Adoption &ndash; Platform Engineer
 
 , you will be responsible for enabling enterprise adoption of Generative AI by designing and building scalable, cloud-native platforms for AI workloads. You will focus on reliability, automation, and multi-tenant architectures, while integrating advanced AI capabilities into enterprise systems.
 
 
 
 
 
 
 
 
 
 Your responsibilities:
 
 
 <ul>
 <li>
 Design, build, and deploy AI-powered applications using large language models and generative AI technologies.
 </li>
 <li>
 Develop and integrate solutions such as chatbots, copilots, and content generation platforms.
 </li>
 <li>
 Implement Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge systems.
 </li>
 <li>
 Perform prompt engineering, optimisation, and evaluation of model outputs.
 </li>
 <li>
 Integrate AI APIs into enterprise applications and workflows.
 </li>
 <li>
 Deploy and manage AI applications across cloud platforms such as AWS, Azure, or GCP.
 </li>
 <li>
 Ensure platform scalability, reliability, and performance across multiple environments.
 </li>
 <li>
 Collaborate with engineering and business teams to deliver AI-driven solutions aligned with organisational needs.
 </li>
 </ul>
 
 
 
 
 
 
 
 
 Your Profile
 
 
 
 Essential skills/knowledge/experience:
 
 <ul>
 <li>
 Strong programming skills in Python (Karat assessment clearance required).
 </li>
 <li>
 Experience in Generative AI application development using frameworks such as LangChain or LangGraph.
 </li>
 <li>
 Hands-on experience building and deploying RAG-based solutions.
 </li>
 <li>
 Experience working with cloud platforms such as AWS, Azure, or GCP.
 </li>
 <li>
 Strong understanding of cloud-native architecture and distributed systems.
 </li>
 </ul>
 
 
 
 
 
 
 
 
 Desirable skills/knowledge/experience:
 
 
 <ul>
 <li>
 Broader exposure to multi-cloud environments across AWS, Azure, and GCP.
 </li>
 </ul>
 
 
 
 
 
 Rewards &amp; Benefits
 
 
 
 TCS is consistently voted a Top Employer in the UK and globally. Our competitive salary packages feature pension, health care, life assurance, laptop, phone, access to extensive training resources and discounts within the larger Tata network.
 
 
 We offer health &amp; wellness initiatives and sports events; we are the proud sponsor of the London Marathon.
 
 
 
 Diversity, Inclusion and Wellbeing
 
 
 
 Tata Consultancy Services UK&amp;I is committed to meeting the accessibility needs of all individuals in accordance with the UK Equality Act 2010 and the UK Human Rights Act 1998.
 
 
 We welcome and embrace diversity in race, nationality, ethnicity, disability, neurodiversity, gender identity, age, physical ability, gender reassignment, sexual orientation. We are a disability inclusive employer and encourage disabled people to apply for this role.
 
 
 As a Disability Confident Employer, we offer an interview to applicants with disabilities or long-term conditions who meet the minimum criteria for the role. Please email us at
 
 UKI.recruitment@tcs.com
 
 if you would like to opt in.
 
 
 If you are an applicant who needs any adjustments to the application process or interview, please contact us
 
 at UKI.recruitment@tcs.com
 
 with the subject line: &ldquo;Adjustment Request&rdquo; or call
 
 TCS London Office 02031552100 / +44 204 520 2575
 
 to request an adjustment. We welcome requests prior to you completing the application and at any stage of the recruitment process.
 
 
 
 
 
 
 Next Steps:
 
 
 
 
 
 
 
 Application Process
 
 
 <ol>
 <li>
 
 Online application:
 
 You can apply directly through LinkedIn/ by uploading your CV. In case you wish to submit your application via another format like audio/video, please, contact - UKI.recruitment@tcs.com
 </li>
 <li>
 
 Skill-Based discussion:
 
 This will be a level 1 interview with the project team, it can be via video or in-person. Details will be confirmed by your recruiter.
 </li>
 <li>
 
 Managerial discussion:
 
 This discussion will focus on behavioural aspects and person-organisation fit.
 </li>
 <li>
 
 HR Discussion
 
 : This will be with one of the members of the HR team and will cover your career journey, aspirations for growth, compensation and any other questions you may have.
 </li>
 </ol>
 
 
 
 
 
 Beware of Fraudulent offers
 
 
 
 This is to notify you that TCS does not ask for any sort of payment or security deposit from candidates at any stage of the recruitment process. The firm never sends out job offers from free internet email services like Gmail, Yahoo Mail, and so on. TCS has not authorised any third-party company to collect money on their behalf. As a vigilant job seeker, beware of fraudulent recruitment activity and protect your interests! You can write to
 
 UKI.recruitment@tcs.com
 
 to report any fraudulent activity.
 
 
 Due to the high volume of applications, we will be unable to contact each applicant individually on the status of their application. If you have not received a direct response within 30 days, then it should be deemed unsuccessful on this occasion.
 
 
 
 Join us and do more of what matters. Apply online now.
 
 
 
 
 
</div>

Gen AI - Platform Engineer

Goldman Sachs

<div>
 
 Job Description
 
 
 
 
 The Opportunity
 
 
 
 Join a team building the data foundations that support the firm&rsquo;s AI and analytics capabilities. This role sits within the engineering effort to develop a modern Lakehouse and AI data platform that enables reliable, well-governed and high-performing data use across the firm.
 
 
 At Goldman Sachs, engineering teams are positioned at the centre of the business, building scalable systems, solving complex technical problems and turning data into action. In data engineering roles, the emphasis is on designing, building and maintaining large-scale data platforms, delivering production pipelines, improving reliability and quality, and partnering closely with users of the platform.
 
 
 This is a delivery-focused role for engineers who want to build robust data assets in production, work with modern data technologies, and grow over time within the firm. You will contribute to the data models, pipelines and platform capabilities that underpin analytics, operational decision-making and emerging AI use cases , and may also help extend platform tooling where additional functionality is needed.
 
 
 
 Role Summary
 
 
 
 As a
 
 Data Engineer
 
 in the
 
 Lakehouse and AI Data Platform team,
 
 you will design, build, test and support data pipelines and curated datasets on the firm&rsquo;s modern data platform. You will work across ingestion, transformation, modelling, optimisation and data quality, helping to deliver data products that are reliable, scalable and fit for purpose. Where there are gaps in platform functionality, you may also contribute to shared tooling or framework components that improve how the platform is used and operated.
 
 
 The role is suited to engineers who are comfortable writing code, working with SQL and distributed data processing, and solving practical delivery problems in a team environment. More experienced candidates may also contribute to technical design, platform standards and the shaping of delivery approaches across a wider set of use cases.
 
 
 
 Key Responsibilities
 
 
 
 
 Pipeline Engineering
 
 
 
 <ul>
 <li>
 Build, enhance and support batch and streaming data pipelines on the Lakehouse and AI data platform.
 </li>
 <li>
 Refactor or modernise existing data flows where needed to improve reliability, performance and maintainability.
 </li>
 <li>
 Where needed, build reusable tooling to improve delivery, consistency and operational support.
 </li>
 <li>
 Ensure data pipelines are production-ready, well tested and operationally supportable.
 
 
 </li>
 </ul>
 
 Data Modelling and Curation
 
 
 
 <ul>
 <li>
 Develop raw, refined and curated datasets that support analytics, reporting and AI use cases.
 </li>
 <li>
 Apply sound data modelling principles to represent business entities, relationships and historical change accurately.
 </li>
 <li>
 Work with consumers to shape data products that are usable, well documented and aligned to business needs.
 
 
 </li>
 </ul>
 
 Data Quality and Reconciliation
 
 
 
 <ul>
 <li>
 Implement controls to validate completeness, accuracy and consistency of data across pipelines and datasets.
 </li>
 <li>
 Use reconciliation approaches to build confidence in production outputs and investigate breaks where they arise.
 </li>
 <li>
 Contribute to clear standards for testing, monitoring and issue resolution.
 </li>
 <li>
 Contribute to practical improvements in testing, monitoring or reconciliation tooling where these strengthen platform reliability and day-to-day delivery.
 
 
 </li>
 </ul>
 
 Delivery and Partnership
 
 
 
 <ul>
 <li>
 Work closely with engineers, platform teams and data consumers to deliver agreed outcomes to time and quality expectations.
 </li>
 <li>
 Communicate clearly on progress, risks, dependencies and design choices, including where delivery would benefit from improvements to shared platform tooling.
 </li>
 <li>
 For more senior candidates, take a broader role in technical leadership, task breakdown and support for junior engineers.
 
 
 </li>
 </ul>
 
 Required
 
 
 
 
 Skills and Experience
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or master&rsquo;s degree in a relevant discipline, or equivalent practical experience, with evidence of strong quantitative skills or data engineering expertise.
 </li>
 <li>
 Strong hands-on programming experience in Python or Java.
 </li>
 <li>
 Good working knowledge of SQL, including troubleshooting, optimisation and data analysis.
 </li>
 <li>
 Ability to learn new tools, internal platforms and delivery workflows quickly.
 </li>
 <li>
 Familiarity with software engineering fundamentals, including version control, testing, release discipline and CI/CD practices.
 
 
 </li>
 </ul>
 
 Data Engineering Capability
 
 
 
 <ul>
 <li>
 Understanding of temporal data modelling, including the handling of historical state and change over time.
 </li>
 <li>
 Knowledge of schema design, schema evolution and data compatibility considerations.
 </li>
 <li>
 Understanding of partitioning, clustering and other techniques used to improve data performance at scale.
 </li>
 <li>
 Ability to make sensible design choices across normalised and denormalised models, and between natural and surrogate keys.
 </li>
 <li>
 Practical approach to data quality, reconciliation and root-cause analysis.
 </li>
 <li>
 Experience building or supporting production data pipelines in a collaborative engineering environment.
 </li>
 <li>
 Experience working with distributed data processing frameworks such as Apache Spark.
 </li>
 <li>
 Working knowledge of common data formats such as JSON, Avro and Parquet.
 
 
 </li>
 </ul>
 
 For More Experienced Candidates
 
 
 
 <ul>
 <li>
 Stronger ownership of technical design across multiple datasets or pipeline domains.
 </li>
 <li>
 Experience guiding implementation standards, code quality and engineering practices within a team.
 </li>
 <li>
 Ability to lead delivery for a workstream, manage dependencies and support less experienced engineers.
 
 
 </li>
 </ul>
 
 Technology Environment
 
 
 
 The role will involve working with a modern and evolving data stack. Candidates are not expected to have deep expertise in every tool from day one but should bring relevant experience and the ability to work across comparable technologies.
 
 
 
 Examples Of Technologies In Scope Include
 
 
 
 <ul>
 <li>
 Data processing and logic: ANSI SQL, Apache Spark, Kafka
 </li>
 <li>
 Data formats: JSON, Avro, Parquet
 </li>
 <li>
 Platforms and storage: Snowflake, Apache Iceberg, Databricks, Hadoop ecosystem technologies, Sybase IQ
 </li>
 <li>
 Engineering and deployment: CI/CD tooling, containerised or Kubernetes-based deployment approaches where relevant
 
 
 </li>
 </ul>
 You will also work with internal data management and platform tooling, so a practical and adaptable engineering mindset is important.
 
 
 
 What We Are Looking For
 
 
 
 We are looking for engineers who can deliver well-structured, reliable solutions in production and who take ownership of the quality of what they build. The role suits candidates who are technically strong, pragmatic and comfortable working in a fast-paced environment where data platforms support important business outcomes. It will also suit candidates who are willing to contribute to shared tooling or platform components that make the wider engineering environment more effective.
 
 
 
 Stronger Candidates Will Typically Demonstrate
 
 
 
 <ul>
 <li>
 sound judgement in technical trade-offs
 </li>
 <li>
 attention to detail in data correctness and testing
 </li>
 <li>
 a clear and structured approach to problem solving
 </li>
 <li>
 willingness to work closely with stakeholders and partner teams
 </li>
 <li>
 an ability to identify when delivery problems would be better solved through reusable tooling or platform improvements
 </li>
 <li>
 an interest in developing long-term expertise within the firm
 
 
 </li>
 </ul>
 
 About Goldman Sachs
 
 
 
 At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.
 
 
 We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers.
 
 
 We&rsquo;re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html
 
 
 &copy; The Goldman Sachs Group, Inc., 2026. All rights reserved.
 
 
 Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.
</div>

Software Engineer - Data, Lakehouse and AI Data Platform Engineer - Analyst/Associate - London

Monzo

<div><div>We're on a mission to make money work for everyone.

We're waving goodbye to the complicated and confusing ways of traditional banking.

After starting as a prepaid card, our product offering has grown a lot in the last 10 years in the UK. As well as personal and business bank accounts, we offer joint accounts, accounts for 16-17 year olds, a free kids account and credit cards in the UK, with more exciting things to come beyond. Our UK customers can also save, invest and combine their pensions with us.

With our hot coral cards and get-paid-early feature, combined with financial education on social media and our award winning customer service, we have a long history of creating magical moments for our customers!

We're not about selling products - we want to solve problems and change lives through Monzo ❤️</div> 
London / UK Remote | &pound;85,000&ndash;&pound;110,000 + Incentive Awards tied to your performance + Benefits 

About Machine Learning Platform Engineering at Monzo

The Machine Learning Platform team builds the systems that help teams across Monzo train, evaluate, deploy and serve ML models and AI features safely and reliably.

We work on backend services, Python libraries, model lifecycle tooling, evaluation workflows and low-latency serving systems. Our users are internal ML engineers, scientists and product teams building with ML and LLMs.

The work matters because machine learning powers many important decisions and experiences at Monzo, from fraud checks and credit decisions to customer operations. We help teams move faster while keeping production systems reliable, observable and safe.

This is a platform engineering role in the ML and AI space. We're looking for someone who combines strong software engineering foundations with ML or AI context, and who enjoys building tools and systems for other engineers.

 What you'll be working with...
<ul>
<li>Go for backend services, platform APIs, and production systems</li>
<li>Python for libraries, workflows, and tooling used by our ML engineers and scientists</li>
<li>Feature platforms and data workflows using Chronon, Feast, and DataHub</li>
<li>Model training pipelines and experiment tracking using Vertex AI and Comet</li>
<li>AI observability, evaluation, and tracing using Langfuse and Bifrost</li>
<li>AWS for real-time serving and online inference, GCP for batch compute and our BigQuery data warehouse.</li>
</ul>
(Please note direct experience with all of them is helpful but not required and our interview process can be completed in any language).

 We'd love to hear from you if...
<ul>
<li>You combine solid backend engineering with real ML or AI platform experience (ML pipelines, feature stores, model serving, experiment tracking or LLM tooling)</li>
<li>You've designed and operated distributed systems that handle scale, concurrency and failure</li>
<li>You think like a platform engineer, focused on developer experience and removing friction for internal teams</li>
<li>You're happy working across both Go and Python</li>
<li>You enjoy ambiguity and want to shape a platform as it grows</li>
<li>You have experience with strongly typed languages, writing and working on backend software</li>
<li>You're curious about how systems behave in production, including reliability, latency, quality, safety and operational risk</li>
</ul>
 This might NOT be the right fit if...
<ul>
<li>Your background is predominantly SRE, DevOps or infrastructure operations</li>
<li>You're focused on data science or modelling</li>
<li>You've shipped AI product features but haven't worked on the platform side (serving, evaluation, model lifecycle)</li>
</ul>
Levels

We're on the look out for Engineers of varying levels at the moment. You can read more in our Engineering Progression Framework - we will interview you across the entire framework, so if you are not sure what level you are aiming for please chat to your recruiters!

About our Engineering Teams

We have around 600 engineers out of roughly 5,000 people in total - and we have big ambitions. There are many interesting challenges ahead, and we're happy for people to move between teams or to specialise, whatever you prefer. As an engineer here you'd be able to work directly with anyone across the company, and we run regular knowledge-sharing sessions so you'll learn heaps about everything from how banks work to effective communication.

We contribute to open source software as much as possible. Our blog is a good place to learn even more about what we do.

 How we work

Locations &amp; Flexible Working

Our main tech hub is in London, but our engineers live everywhere in the UK, from Brighton to the Western Isles.

We value meeting in person but there's no pressure to come into the office, even if you're nearby. We believe you'll do your best work if you are where you want to be. If you live outside of London and we ask you to come into the office, Monzo will support you with the costs ✨

Our offices are naturally social, especially Tuesdays, Wednesdays and Thursdays, which happen to line up with our twice-weekly Monzo lunches &amp; treat Thursdays ️

Teams also often schedule time together for work and play, whether in or around the office, or online.

Set up a work schedule that delivers impact and fits your life

At Monzo, we value connections, flexibility, and wellbeing. We keep our meetings during core hours to stay connected and believe in maintaining work/life balance.

You'll be empowered to manage your work in a way that suits you and your team, giving you the freedom for children drop-offs and pick-ups, walking your dog or adventurous cat, avoiding peak commuting times or gym slots, appointments, or supporting your family in an emergency ✨

If you prefer to work part-time, we'll make this happen whenever we can - whether this is to help you meet other commitments or strike a great work-life balance.

The Interview Process

Our interview process involves three main stages:
<div>
<ul><li>Initial Call</li></ul>
<ul><li>Take home task or pair coding exercise</li></ul>
<ul><li>Final interview: including a system design and a behavioural interview</li></ul>
</div>
Our average process takes around 4 weeks but we will always work around your availability.

One of our engineers has written a detailed blog on their experience through this process, for extra details, hints and tips please see here.

What's in it for you
<div>
 &pound;85,000&ndash;&pound;110,000 + Incentive Awards tied to your performance 
✈️ We can help you relocate to the UK 
✅ We can sponsor visas. 
 This role can be based in our London office, but we're open to distributed working within the UK (with ad hoc meetings in London). 
<ul><li>We offer flexible working hours and trust you to work enough hours to do your job well, at times that suit you and your team.</li></ul>
 Learning budget of &pound;1,000 a year for books, training courses and conferences 
➕ And much more, see our full list of benefits here</div><div> 

Equal opportunities for everyone

Diversity and inclusion are a priority for us and we're making sure we have lots of support for all of our people to grow at Monzo. At Monzo, we're embracing diversity by fostering an inclusive environment for all people to do the best work of their lives with us. This is integral to our mission of making money work for everyone. You can read more in our blog, 2026 Diversity and Inclusion Report and 2025 Gender Pay Gap Report.

We're an equal opportunity employer. All applicants will be considered for employment without attention to age, ethnicity, religion, sex, sexual orientation, gender identity, family or parental status, national origin, or veteran, neurodiversity or disability status.

If you have a preferred name, please use it to apply. We don't need full or birth names at application stage</div></div>

Machine Learning Platform Engineer

Playtech

<div>
 
 
 About Playtech
 
 
 
 
 
 
 Founded in 1999, the company has a premium listing on the Main Market of the London Stock Exchange and is focused on regulated and regulating markets across its B2B business. By leveraging its proprietary technology, Playtech delivers innovative products and services to ensure a safe, engaging and entertaining gaming experience.
 
 
 
 
 
 As the gaming industry's leading technology company, it combines business intelligence-driven software, services, content, and platform technology to drive excellence and innovation across the sector. Read more about who we are and what we do here: www.playtechpeople.com and www.playtech.com
 
 
 
 
 
 Here at Playtech, we genuinely believe that people are our biggest asset. Diverse thoughts, experiences, and individual characteristics enrich our work environment and lead to better business decisions. Embracing differences and maintaining transparency in our processes is the core of Playtech's overall commitment to responsible business practices.
 
 
 
 
 
 
 Ready to level up your career?
 
 
 
 
 
 
 We&rsquo;re hiring AI Solutions Engineers who enjoy turning ideas into practical solutions, experimenting with the latest tools, and solving real business problems. We are currently filling three roles.
 
 
 
 
 
 This is not about theory - it&rsquo;s about building AI that works in the real world!
 
 
 
 
 
 
 Job Description
 
 
 
 
 
 
 
 Your Influential Mission: You Will&hellip;
 
 
 
 
 
 <ul>
 <li>
 Build practical AI tools, prototypes, and applications that improve workflows, products, and productivity.
 </li>
 <li>
 Create reusable patterns, templates, and best practices for AI adoption across Playtech.
 </li>
 <li>
 Design agentic workflows that automate tasks, use APIs, and support complex processes.
 </li>
 <li>
 Stay current with AI trends and turn insights into demos, workshops, and recommendations.
 </li>
 <li>
 Partner with teams to turn ideas into real solutions and support AI adoption hands-on.
 </li>
 <li>
 Drive efficiency and productivity through AI across multiple business units.
 </li>
 <li>
 Improve solution quality, cost, and performance through better design and optimisation.
 </li>
 <li>
 Identify and mitigate AI risks, ensuring safe and responsible use.
 </li>
 <li>
 Contribute to AI standards, governance, and best practices.
 </li>
 <li>
 Share knowledge, mentor others, and raise the overall AI engineering bar.
 </li>
 </ul>
 
 
 
 
 
 Qualifications
 
 
 
 
 
 
 
 Components for Success: You&hellip;
 
 
 
 
 
 <ul>
 <li>
 Have a strong interest in AI, especially enterprise AI solutions, and genuine enthusiasm to learn and keep pace with the latest developments and trends.
 </li>
 <li>
 Bring a strong engineering background, with hands-on experience building and deploying AI, ML, or LLM-based systems.
 </li>
 <li>
 Have experience designing platform-level capabilities (APIs, SDKs, shared services) that other engineering teams consume.
 </li>
 <li>
 Are comfortable working across teams, explaining trade-offs, mentoring others, and partnering with various stakeholders.
 </li>
 <li>
 Have a practical understanding of modern AI tooling: LLM APIs, vector stores, RAG, evals, agent frameworks, and developer assistants such as Cursor.
 </li>
 <li>
 Understand software architecture and version control (Git), whether from a development background or equivalent hands-on AI experience.
 </li>
 <li>
 Bring a pragmatic, delivery-focused mindset; comfortable in a large, regulated, multi-product engineering environment.
 </li>
 </ul>
 
 
 
 
 
 You&rsquo;ll Get Extra Points for&hellip;
 
 
 
 
 
 <ul>
 <li>
 Familiarity with Google Cloud AI services (Gemini Enterprise Agent Platform, ADK)
 </li>
 <li>
 AI evaluation or AI security experience.
 </li>
 <li>
 Agentic development experience (MCP, multi-agent systems).
 </li>
 <li>
 RAG implementation experience.
 </li>
 <li>
 Cloud DevOps or Terraform experience.
 </li>
 <li>
 Prior experience in iGaming, fintech, or another regulated industry.
 </li>
 <li>
 AI governance experience or interest.
 </li>
 <li>
 Coding experience, especially in Python
 </li>
 </ul>
 
 
 
 
 
 Thrive in a culture that values&hellip;
 
 
 
 
 
 <ul>
 <li>
 Ownership and accountability where you take responsibility for delivery, operations, and outcomes.
 </li>
 <li>
 Proactive collaboration across the business units and teams globally.
 </li>
 <li>
 Innovation with responsibility, encouraging improvement while maintaining strong governance and compliance.
 </li>
 <li>
 Versatile and exciting challenges that keep you going and growing.
 </li>
 <li>
 Hybrid working style &ndash; work in our modern and comfortable office as well as work from home office.
 </li>
 <li>
 Neat benefits &amp; bonus package.
 </li>
 </ul>
 
 
 
 
 
 Additional Information
 
 
 
 
 
 
 
 CENTRAL AI TEAM
 
 
 
 
 
 
 The team is Playtech&rsquo;s AI Centre of Excellence, driving AI adoption across the business. Operating at the intersection of platform engineering, solution delivery, and governance, the team builds shared capabilities while enabling business units to apply AI safely and effectively. With 80+ use cases already in progress, this is a unique opportunity to work on real enterprise challenges using cutting-edge technologies. Joining now means shaping how AI scales across a global organisation and influencing solutions used by thousands of colleagues and millions of players. Backed by strong executive support, AI is a key strategic priority at Playtech, offering high visibility and long-term impact.
 
 
 
 
 
 Playtech is an equal opportunities employer. Our mission is to welcome everyone and create inclusive teams. We celebrate differences and encourage everyone to be themselves at work.
 
</div>

AI Solution Engineer

PLAYTECH

<div>Company Description 
About Playtech
Founded in 1999, the company has a premium listing on the Main Market of the London Stock Exchange and is focused on regulated and regulating markets across its B2B business. By leveraging its proprietary technology, Playtech delivers innovative products and services to ensure a safe, engaging and entertaining gaming experience.
As the gaming industry's leading technology company, it combines business intelligence-driven software, services, content, and platform technology to drive excellence and innovation across the sector. Read more about who we are and what we do here: www.playtechpeople.com and www.playtech.com
Here at Playtech, we genuinely believe that people are our biggest asset. Diverse thoughts, experiences, and individual characteristics enrich our work environment and lead to better business decisions. Embracing differences and maintaining transparency in our processes is the core of Playtech's overall commitment to responsible business practices.
Ready to level up your career?
We&rsquo;re hiring AI Solutions Engineers who enjoy turning ideas into practical solutions, experimenting with the latest tools, and solving real business problems. We are currently filling three roles. 
This is not about theory - it&rsquo;s about building AI that works in the real world! 
Job Description 
Your Influential Mission: You Will&hellip;<ul><li>
Build practical AI tools, prototypes, and applications that improve workflows, products, and productivity.</li><li>
Create reusable patterns, templates, and best practices for AI adoption across Playtech.</li><li>
Design agentic workflows that automate tasks, use APIs, and support complex processes.</li><li>
Stay current with AI trends and turn insights into demos, workshops, and recommendations.</li><li>
Partner with teams to turn ideas into real solutions and support AI adoption hands-on.</li><li>
Drive efficiency and productivity through AI across multiple business units.</li><li>
Improve solution quality, cost, and performance through better design and optimisation.</li><li>
Identify and mitigate AI risks, ensuring safe and responsible use.</li><li>
Contribute to AI standards, governance, and best practices.</li><li>
Share knowledge, mentor others, and raise the overall AI engineering bar.</li></ul> 

Qualifications 
Components for Success: You&hellip;<ul><li>
Have a strong interest in AI, especially enterprise AI solutions, and genuine enthusiasm to learn and keep pace with the latest developments and trends.</li><li>
Bring a strong engineering background, with hands-on experience building and deploying AI, ML, or LLM-based systems.</li><li>
Have experience designing platform-level capabilities (APIs, SDKs, shared services) that other engineering teams consume.</li><li>
Are comfortable working across teams, explaining trade-offs, mentoring others, and partnering with various stakeholders.</li><li>
Have a practical understanding of modern AI tooling: LLM APIs, vector stores, RAG, evals, agent frameworks, and developer assistants such as Cursor.</li><li>
Understand software architecture and version control (Git), whether from a development background or equivalent hands-on AI experience.</li><li>
Bring a pragmatic, delivery-focused mindset; comfortable in a large, regulated, multi-product engineering environment.</li></ul>
You&rsquo;ll Get Extra Points for&hellip;<ul><li>
Familiarity with Google Cloud AI services (Gemini Enterprise Agent Platform, ADK)</li><li>
AI evaluation or AI security experience.</li><li>
Agentic development experience (MCP, multi-agent systems).</li><li>
RAG implementation experience.</li><li>
Cloud DevOps or Terraform experience.</li><li>
Prior experience in iGaming, fintech, or another regulated industry.</li><li>
AI governance experience or interest.</li><li>
Coding experience, especially in Python</li></ul>
Thrive in a culture that values&hellip;<ul><li>
Ownership and accountability where you take responsibility for delivery, operations, and outcomes.</li><li>
Proactive collaboration across the business units and teams globally.</li><li>
Innovation with responsibility, encouraging improvement while maintaining strong governance and compliance.</li><li>
Versatile and exciting challenges that keep you going and growing.</li><li>
Hybrid working style &ndash; work in our modern and comfortable office as well as work from home office.</li>
Neat benefits &amp; bonus package. 
<li>
</li></ul> 
 
Additional Information 
CENTRAL AI TEAM
The team is Playtech&rsquo;s AI Centre of Excellence, driving AI adoption across the business. Operating at the intersection of platform engineering, solution delivery, and governance, the team builds shared capabilities while enabling business units to apply AI safely and effectively. With 80+ use cases already in progress, this is a unique opportunity to work on real enterprise challenges using cutting-edge technologies. Joining now means shaping how AI scales across a global organisation and influencing solutions used by thousands of colleagues and millions of players. Backed by strong executive support, AI is a key strategic priority at Playtech, offering high visibility and long-term impact.
Playtech is an equal opportunities employer. Our mission is to welcome everyone and create inclusive teams. We celebrate differences and encourage everyone to be themselves at work.</div>

HCLTech

City Of London, England
 Job Summary
 
Job Title: Forward Deployed Engineer (FDE), Gemini Enterprise
 The FDE is a hybrid of a Staff Software Engineer and a Strategic Consultant. You will be the technical lead responsible for deploying Gemini Enterprise App and Gemini Enterprise Agent Platform within client environments. You will architect and code the "connective tissue" that allows Gemini agents to reason across private enterprise silos, execute long-running workflows, and adhere to strict corporate governance.
 
Key Responsibilities
 
Key Responsibilities
 <ul>
<li>Agentic Architecture: Design multi-agent systems using the Agent Development Kit (ADK), implementing patterns like hierarchical delegation and self-reflection.</li>
<li>Production Implementation: Move beyond prototypes. You will write the production code that integrates Gemini agents with legacy APIs, SQL/NoSQL databases, and ERP systems (SAP, Salesforce, etc.).</li>
<li>The "Grounding" Expert: Build and optimize RAG (Retrieval-Augmented Generation) pipelines using Vertex AI Search and BigQuery, ensuring agents have "zero-hallucination" access to enterprise truths.</li>
<li>Governance & Security: Deploy Agent Identity and Agent Gateway protocols to ensure every AI action is auditable, secure, and compliant with regional data residency laws.</li>
<li>Field-to-Product Loop: Act as the "eyes and ears" for Google’s core engineering. Identify common friction points in the Agent Platform and collaborate with product teams to build reusable "Skills" and modules.</li>
</ul>
Skill Requirements
 
Technical Requirements
 Domain Stack Expertise
 AI/ML Platform Gemini 3.0, Gemini Enterprise Agent Platform, Agent Studio, Vertex AI
 Engineering Python (Advanced), Go or Java, TypeScript, REST/gRPC APIs
 Data Architecture BigQuery, Vector Search, AlloyDB, Apache Beam / Dataflow
 Orchestration Agent Development Kit (ADK), LangGraph-native patterns, Kubernetes (GKE)
 Ops & Eval Agent Evaluation (Auto-SxS), Agent Observability, CI/CD for LLMs
 
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AI Solution Architect I

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