software engineer

ai engineer

senior software engineer

senior ai engineer

analytics engineer

ai product engineer

Elliptic

<div>Do you want to build the AI systems that compliance teams rely on to investigate real financial crime? Are you excited about taking agentic workflows from prototype to production at scale? Do you want your work to directly help catch bad actors moving money across blockchains?
Elliptic is hiring a Senior AI Engineer to join our AI team. It is the team behind Elliptic's copilot, the AI product that helps compliance investigators trace fund flows, surface patterns, and respond to risk in real time. This role sits at the centre of that work, designing the agentic systems, LLM integrations, and backend services that turn complex blockchain data into useful answers.
You'll join a focused team that cares about the quality of what it builds and gives you the space to do your best work. There's real ownership here, breadth across AI and backend systems, and the freedom to turn good ideas into shipped products. The problems are technically challenging, and the work is visible.
The impact you will have:
As a Senior AI Engineer on the AI team, you'll own significant parts of the Agentic stack that power Elliptic's copilot. You'll lead the design of agentic workflows, drive technical decisions on how we build and operate LLM-based features, and help raise the engineering standard across the team. You'll partner with product, web engineers, and your engineering lead to take complex problems from ambiguity to shipped product, and you'll mentor more junior engineers as the team grows.
What you will do:<ul><li>
Design and build agentic workflows and AI-powered features that power Elliptic's copilot, using LLM frameworks such as LangChain or LangGraph.</li><li>
Lead the design of agents, tool integrations, and retrieval pipelines that turn complex blockchain data into useful answers for investigators.</li><li>
Build and own evaluation frameworks that measure and improve the quality, reliability, and latency of LLM outputs.</li><li>
Design and build the backend services, APIs, and event-driven systems that support these features, using TypeScript and Node.js.</li><li>
Drive technical design reviews and architecture decisions for AI workstreams.</li><li>
Mentor and coach more junior engineers through pair programming, code review, and design feedback.</li><li>
Raise the engineering bar across the team by promoting good practices in testing, observability, and AI system reliability.</li><li>
Influence cross-team decisions on how AI capabilities integrate with the rest of the Elliptic platform.</li></ul>
What you will achieve in the first 6 months:<ul><li>
Owned and shipped at least one significant AI capability end-to-end, from ambiguous problem statement through to production.</li><li>
Driven the technical direction of an AI workstream, including architecture, evaluation, and rollout.</li><li>
Established or improved at least one team practice for building AI systems (evals, observability patterns, prompt management, rollout safety).</li><li>
Mentored junior engineers on AI engineering practices and contributed to their growth.</li><li>
Built strong working relationships across product, web engineering, data engineering, and DevOps.</li><li>
Started to influence adjacent teams on how Elliptic builds with AI.</li></ul>
You will be a great fit here if you:<ul><li>
Care about building AI systems that work reliably in production, not just demos.</li><li>
Have strong instincts for trade-offs specific to LLM systems: cost vs latency vs quality, deterministic vs probabilistic behaviour, when to use an agent vs a workflow.</li><li>
Can take ambiguous AI problems and break them into shippable pieces with clear evaluation criteria.</li><li>
Communicate clearly with non-engineers (product, stakeholders, leadership) about AI capabilities and limitations.</li><li>
Have coached or mentored other engineers and enjoy raising the bar through reviews, pairing, and feedback.</li><li>
Are genuinely curious about the domain and find blockchain investigations and financial crime prevention interesting.</li></ul>
Our ideal candidate has:<ul><li>
5-8+ years of software engineering experience, with at least 1-2 years of meaningful production work building LLM-powered features.</li><li>
Direct experience with LLM frameworks such as LangChain, LangGraph, or similar.</li><li>
Hands-on experience building agentic systems: tool use, multi-step reasoning, planning, memory, and human-in-the-loop patterns.</li><li>
Practical understanding of prompt engineering, structured outputs, context management, and managing the trade-offs of working with LLMs.</li><li>
Experience building and running evaluations for LLM outputs (eval sets, LLM-as-judge, regression testing).</li><li>
Strong backend skills in TypeScript / Node.js, with solid API design.</li><li>
Cloud experience (AWS: Lambda, ECS, S3, or similar).</li><li>
Database proficiency across SQL (Postgres) and some NoSQL exposure.</li><li>
Demonstrated ability to mentor and coach other engineers.</li><li>
Uses AI coding assistants (Copilot, Cursor, Claude, etc.) critically and effectively as part of their day-to-day workflow.</li></ul>
Bonus points for:<ul><li>
Experience with retrieval-augmented generation (RAG) and vector databases (pgvector, Pinecone, or similar).</li><li>
Experience designing multi-agent systems and agent orchestration patterns.</li><li>
Familiarity with LLM observability tools (LangSmith, Langfuse, Arize, or similar).</li><li>
Experience working with multiple model providers (Anthropic, OpenAI, open-weight models) or with fine-tuning.</li><li>
Hands-on experience with Terraform, Kubernetes, or infrastructure-as-code tooling.</li><li>
Experience with observability platforms like Datadog.</li><li>
Distributed or event-driven architectures (SNS, SQS).</li><li>
Interest in cryptocurrency, blockchain, or compliance.</li></ul><h2 class="jobSectionHeader">
Job Benefits</h2>
&gt; How we work:<ul><li>
Hybrid working and the option to work from almost anywhere for up to 90 days per year</li><li>
&pound;500 Remote working budget to set up your home office space</li></ul>
&gt; Learning &amp; Development:<ul><li>
$1,000 Learning &amp; Development budget to use on anything (agreed with your manager) that contributes to your growth and development</li></ul>
&gt; Vacation/ Leave:<ul><li>
Holidays: 25 days of annual leave + bank holidays</li><li>
An extra day for your birthday</li><li>
Enhanced parental leave: we provide eligible employees, regardless of gender or whether they become a parent by birth or adoption, 16 weeks fully-paid leave and leave.</li></ul>
&gt; Benefits:<ul><li>
Private Health Insurance - we use Vitality!</li><li>
Full access to Spill Mental Health Support</li><li>
Life Assurance: we hope you will never need this - but our cover is for 4 times your salary to your beneficiaries</li><li>
Cycle to Work Scheme</li></ul>
We know Diversity and Inclusion is much deeper than just hiring, but it's important for us to mention it here. We welcome and embrace individuals of all backgrounds and identities at Elliptic, and this is an ongoing priority for us. We know incredible people don't all think in the same way. We want to be challenged every day. We believe our diverse team of individuals underpins this by bringing creative thinking and innovation to Elliptic every day. We are committed to creating a diverse, inclusive and equitable workplace, so we welcome applications from everyone, even if you may not think you fit all of the requirements of our roles. We foster an environment of psychological safety, where everyone feels comfortable to bring their whole self to work.</div>

Senior AI Product Engineer

Metaview

<div><div><div><h2 class="jobSectionHeader">Location</h2>
London
</div><div><h2 class="jobSectionHeader">Employment Type</h2>
Full time
</div><div><h2 class="jobSectionHeader">Location Type</h2>
Hybrid
</div><div><h2 class="jobSectionHeader">Department</h2>
ProductEngineering
</div></div><div><div>Overview
</div><div><div>Coding was first. Recruiting is next &mdash; and we're building it.
We are an applied AI lab building end-to-end recruiting agents that feel like coworkers. Metaview helps organizations hire with radically more speed and precision. We automate toil and augment humans for teams like Brex, Affirm, Deel, ElevenLabs, and Airtable.
Founded by Siadhal Magos and Shahriar Tajbakhsh, who scaled Uber and Palantir, we have raised over $50M from top-tier investors. We are growing 5x year over year, our customers are raving fans of the product, and our story has been covered in Fortune, Forbes, TechCrunch, and The Times.
Now is the time to re-engineer how work gets done with AI at the core, and our toughest challenges still lie ahead. We are looking for people seeking the hardest, most fulfilling work of their lives.
We have one value: velocity.
 
Not motion for its own sake, but real progress. We ship every day, ship on Fridays, and skip the tickets, roadmaps, and design-doc theater. We barely write code by hand anymore: every engineer orchestrates a stack of agents, and we ship 8x what we did a year ago with the same small team. Process stays light, standards stay high, and feedback loops stay short.
What you'll build
 
Day to day, you'll work on:
<ul><li>new product experiences across the hiring workflow
</li><li>systems that turn conversational signal into structured actions and insights
</li><li>AI-assisted features that need to feel useful, transparent, and well-integrated into the product
</li><li>improvements to quality, speed, and reliability across both user-facing and agent-facing parts of the product
</li><li>product problems that cut across engineering, design, product, and go-to-market context
</li></ul>What you'll own
In your first 3-6 months, you should:
<ul><li>own and ship meaningful product areas from problem definition through rollout
</li><li>help shape how Metaview's AI products behave in real customer workflows
</li><li>identify and drive improvements that make the product faster, clearer, and more valuable
</li><li>contribute to the engineering bar across craft, execution, and product judgment
</li></ul>What we're after
We're hiring across all levels, but we are excited by people who:
<ul><li>have shipped product in fast-moving software environments
</li><li>are comfortable working across the stack, or moving outside their default lane when needed
</li><li>care deeply about users and can translate messy problems into clear product decisions
</li><li>combine technical strength with product sense and good judgment
</li><li>like ownership, ambiguity, and close collaboration across functions
</li><li>want to help build a category-defining product while the shape of the company is still being formed
</li></ul>What you bring
Helpful signals include experience with:
<ul><li>shipping products, workflow software, or AI-enabled products
</li><li>systems where frontend and backend decisions meaningfully shape each other
</li><li>building products used in consequential or trust-sensitive moments
</li><li>startup or scale-up environments where iteration speed and ownership are both high 
 
</li></ul>What you want to know
<ul><li>A small, sharp, in-person team in London. Best ideas win on merit, not ceremony. Want to know what it's actually like? Read our unfiltered Slack at metaview.ai/careers#slackbook.
</li><li>We pay in the top 10% of the market &mdash; salary and meaningful equity. Our process is fast and direct. It's not unusual to hear back the same day.
</li><li>We operate with a simplified leveling, early-career, mid-career, and staff and above. Your level sets your comp. No back-and-forth, no games. We track the market closely and adjust when it tells us to. Market refreshers, twice a year. We re-benchmark against the market two times a year and move comp up when it drifts &mdash; so &quot;top 10%&quot; stays true, not just true on your start date.
</li><li>Promotions and raises on a real cycle. We review level and scope roughly every six months. When your responsibilities grow, your comp follows. It's tied to a real change in the work rather than an automatic bump.
</li><li>Equity that's genuinely competitive. World-class equity option grants so we&rsquo;re all owners. We keep the structure simple: cash and equity.
</li><li>Visa sponsorship and immigration support. We sponsor work visas and handle the process with you, start to finish.
</li><li>Time off, unlimited. You should feel empowered to take as much time off as you need.</li></ul></div></div></div></div>

Member of Product Staff, Engineer (London)

Percepta

<div>
 
 Who We Are
 
 
 
 Percepta&rsquo;s mission is to
 
 transform critical institutions with applied AI.
 
 We care that industries that power the world (e.g. healthcare, manufacturing, energy) benefit from frontier technology.
 
 
 To make that happen, we embed with industry-leading customers to drive AI transformation. We bring together:
 
 
 <ul>
 <li>
 Forward-deployed expertise in engineering, product, and research
 </li>
 <li>
 Mosaic, our in-house toolkit for rapidly deploying agentic workflows
 </li>
 <li>
 Strategic partnerships with Anthropic, McKinsey, AWS, companies within the General Catalyst portfolio, and more
 
 
 </li>
 </ul>
 Our team is a quickly growing group of Applied AI Engineers, Embedded Product Managers and Researchers motivated by diffusing the promise of AI into improvements we can feel in our day to day lives.
 
 
 Percepta is a direct partnership with General Catalyst, a global transformation and investment company.
 
 
 
 About The Role
 
 
 
 We&rsquo;re hiring
 
 Applied AI Engineers
 
 who will work directly with customer executives, operators, and technical teams to define and deliver high-impact AI systems. We are excited to build out our European presence, and you will be on the front lines of helping to define our strategy and success in the region. We don't build prototypes or laboratory projects- you&rsquo;ll design, build, and ship production-grade AI agents and workflows that drive millions in business value for customers.
 
 
 
 Our Applied AI Engineers
 
 
 
 <ul>
 <li>
 Shape the AI vision: Collaborate with customer stakeholders to define their long-term AI strategy and transformation roadmap.
 </li>
 <li>
 Identify high-leverage use cases: Work backwards from the long-term vision to surface and prioritize specific AI applications that connect our ambition to measurable business value.
 </li>
 <li>
 Ship real products: Rapidly build and deploy AI solutions that work in the wild, with real users and real data.
 </li>
 <li>
 Advance our core product: Encode the lessons from our customer engagements in our Mosaic product, consistently contributing new features, patterns, and abstractions.
 
 
 </li>
 </ul>
 
 What We&rsquo;re Looking For
 
 
 
 <ul>
 <li>
 AI-nativeness: You're excited about the potential for AI to transform businesses and want to play a hands-on role in bringing frontier technology into critical institutions.
 </li>
 <li>
 Being generative and collaborative: You love constantly jamming on new &ldquo;what if&rdquo; ideas with teammates and partners.
 </li>
 <li>
 Extreme ownership: You&rsquo;re willing to jump in and love being the one on the hook. You aren&rsquo;t going to wait to be pointed at a task&mdash;you&rsquo;re going to identify what you think we should do next, and then do it.
 </li>
 <li>
 Execution excellence and speed: You can build stuff in messy environments and know how to get code written and shipped quickly. You can hold the balance of speed and quality and know when to push the pace vs. when to slow down.
 </li>
 <li>
 Customer-obsession and respect: You&rsquo;re motivated by understanding customer pain points and iterating directly with end users to deliver wins quickly.
 
 
 </li>
 </ul>
 
 Bonus if you have
 
 
 
 <ul>
 <li>
 Hands-on experience with LLM tooling (LangGraph, Mastra, Agents SDK, etc).
 </li>
 <li>
 Built and deployed production LLM-powered systems (tool use, RAG pipelines, multi-step agents, human-in-the-loop, etc).
 </li>
 <li>
 Strong technical foundations in Python/TypeScript, cloud deployment (AWS/GCP/Azure) and DevOps tooling.
 </li>
 <li>
 Prior startup or founding engineer experience, balancing craft, ownership, and speed.
 
 
 </li>
 </ul>
 We&rsquo;re working against an incredibly ambitious mission. It won&rsquo;t be easy, but it will likely be the most fulfilling work of your career. If this excites you, let's chat, even if you don't meet all of the qualifications above.
 
 
 
 Our Values
 
 
 
 
 Dream bigger:
 
 We have the unique privilege of taking on the most ambitious problems and we should chase them with optimism, responsibility, and genuine belief that we can make it happen. We have to embrace the hard things when no one else will.
 
 
 
 Heart in the game:
 
 What we're doing matters and we have to give a shit. Internally, that means fixing badness when you find it. Externally, it means honoring the trust our customers place in us with their most important problems. This isn&rsquo;t a 9-5, nor is it a job we&rsquo;re ever going to monitor your hours. We promise to put work in front of you that matters and in return, we ask you to promise to care.
 
 
 
 Win for the customer:
 
 Everyone is an engineer and the job of an engineer is to deliver outcomes, not outputs. Everything we do&mdash;the products we build, the partnerships we launch, the strategy we set&mdash;exists to make our customers successful. Delivery is the strategy.
 
 
 
 Make the call:
 
 Organizations are only as strong as the pace at which they make decisions. Everyone at Percepta should feel empowered to commit and shape the ambiguity in front of them. But "make the call" cuts both ways: make the decision
 
 and
 
 make the phone call. High-agency decision-making only works with high-bandwidth communication and we commit to never operate in silos.
 
 
 
 Intensity with kindness:
 
 We believe in excellence in execution, candor in feedback, ruthlessness in prioritization, and survivalist urgency. We also believe you don't need to be an asshole to deliver on any of this. The trust built through shared kindness and vulnerability is what makes the intensity sustainable.
</div>

Applied AI Engineer - Europe

Macrobond Financial AB

<div>About Us 
 
Macrobond is a leading provider of global economic and financial data and technology for investment professionals. Our customers include over 900 firms spanning the buyside, sell side, corporate and academic sectors.
Our platform, rich in intellectual property and supported by a rapidly expanding global team, ensures we remain at the forefront of our industry. With the backing of Francisco Partners, a prominent global tech investment firm, we operate as a truly international company. Our headquarters are in London, and we have key offices in Poland, Lisbon, Hong Kong, Gothenburg, Malmo and New York 
 
Job Overview 
 
 
Macrobond is building a dedicated engineering capability to accelerate the development of AI-native products and workflows. As a Senior Fullstack Engineer, you will play a key role in delivering scalable, high-quality software across the platform, contributing to areas such as semantic search, knowledge systems, and AI-assisted research workflows.
You will be responsible for designing, building, and maintaining end-to-end product capabilities, working across frontend, backend, and data layers. This role focuses on strong execution, technical ownership within defined areas, and close collaboration with Product, Design, and AI teams to deliver features that enable faster discovery, analysis, and decision-making. 
Job Responsibilities 
 
 
System Design and Development
<ul><li>Design and implement scalable, reliable fullstack solutions across frontend and backend systems
</li><li>Own and deliver components and services within a defined product area
</li><li>Contribute to architectural decisions within your domain, ensuring maintainability and performance
</li>Ensure code quality through testing, code reviews, and adherence to engineering standards 
<li>
</li></ul>Product Delivery
<ul><li>Translate product requirements into well-structured technical solutions
</li><li>Deliver features end-to-end, from design through to production deployment
</li><li>Balance delivery speed with long-term system quality and sustainability
</li>Identify and resolve technical risks, constraints, and dependencies 
<li>
</li></ul>AI-Enabled Product Development
<ul><li>Integrate AI capabilities into product workflows (e.g. search, insights, automation)
</li><li>Collaborate with AI Engineering to incorporate models, services, and pipelines into user-facing applications
</li><li>Ensure reliability, observability, and performance of AI-driven features in production
</li>Contribute to evaluation and iteration of AI-powered capabilities 
<li>
</li></ul>Cross-Functional Collaboration
<ul><li>Work closely with Product Managers and Designers to shape and refine solutions
</li><li>Collaborate with other engineers to ensure consistency across systems and services
</li><li>Contribute to team delivery processes, including sprint planning, reviews, and retrospectives
</li>Support alignment across teams when working on shared systems or dependencies 
<li>
</li></ul>System Quality and Operations
<ul><li>Build and maintain robust APIs, services, and frontend applications
</li><li>Ensure systems are secure, observable, and performant in production
</li><li>Monitor, debug, and resolve production issues
</li>Contribute to continuous improvement of development workflows, tooling, and infrastructure 
<li>
</li></ul>Required Qualifications and Experience 
 
 
<ul><li>Strong experience building and delivering full stack applications in a production environment
</li><li>Proven ability to design and implement scalable backend services and intuitive frontend interfaces
</li><li>Proficiency in modern frontend frameworks and backend technologies (languages/frameworks vary by stack)
</li><li>Experience working with APIs, distributed systems, and data-intensive applications
</li><li>Solid understanding of system design, performance optimization, and reliability
</li><li>Experience integrating external services, including AI/ML capabilities, into applications
</li><li>Strong collaboration skills, with experience working closely with Product and Design
</li><li>Ability to work through ambiguity and independently drive technical solutions
</li>Experience working in fast-paced, iterative development environments 
<li>
</li></ul> 
What do we offer
<ul><li>Private medical insurance, inclusive of dental, vision, and hearing
</li><li>Life insurance
</li><li>Company pension scheme, with a strong employer contribution
</li><li>Gym discounts
 
</li><li>25 days holiday per annum
</li>2 Macrobond days to use per annum for volunteering 
<li>
</li></ul> 
Our commitment to Diversity 
 
At Macrobond, diversity, equity, and inclusion are fundamental to our culture and success. We are committed to fostering an environment where all individuals are respected, valued, and empowered to contribute their unique perspectives.
We are an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity or expression, disability, protected veteran status, or any other characteristic protected by applicable law. 
 
By joining our team, you become part of an organization that believes diversity drives innovation and strengthens our collective performance. Apply today and be part of our ongoing journey of growth and inclusion.
By using this website and submitting your information, you acknowledge our Candidate Privacy Notice and understand that your personal information may be processed in accordance with applicable Candidate Privacy Notice in your country of application. 
 
Apply today and become part of our exciting journey!</div>

Senior Full Stack Engineer

Multiverse

<div>Multiverse is the upskilling platform for AI and Tech adoption.
We have partnered with 1,500+ companies to deliver a new kind of learning that's transforming today&rsquo;s workforce.
Our upskilling apprenticeships are designed for people of any age and career stage to build critical AI, data, and tech skills. Our learners have driven $2bn+ ROI for their employers, using the skills they&rsquo;ve learned to improve productivity and measurable performance.
In April 2026, we announced $70 million in strategic funding, led by Schroders Capital, with participation from StepStone Group, Lightspeed Venture Partners and General Catalyst. At an increased valuation of $2.1bn, the round makes us Europe&rsquo;s first EdTech double unicorn.
But we aren&rsquo;t stopping there. With a strong operational footprint and 800+ employees, we have ambitious plans to continue scaling. We&rsquo;re building a world where tech skills unlock people&rsquo;s potential and output. 
 
Join Multiverse and power our mission to equip the workforce to win in the AI era.<h2 class="jobSectionHeader">
The Role</h2>
Multiverse is the UK's largest apprenticeship provider and its first EdTech unicorn. The current state of AI presents a huge opportunity to reshape the future of education and workforce development. Multiverse is in a uniquely strong position to do that, and getting it right has implications beyond the company: for the UK tech sector and the broader economy.
The AI Transformation team exists to make that real, starting with Multiverse itself. This is not a team that bolts AI onto the edges of the business or ships a handful of internal productivity tools. The mandate is bigger: to rebuild how the company actually works, function by function, and to establish the practices that make Multiverse an AI-first company from the core out.
That work matters twice over. Get it right inside Multiverse and we move faster, serve learners better, and operate at a level few organisations can match. But Multiverse also exists to build the workforce that every other company is reaching for. The way we transform ourselves becomes the standard we set for everyone else. You are not just changing one company, you are building the blueprint others will follow.
The team is one small, focused squad, accountable for outcomes end to end. You work closely with the wider engineering org building Multiverse's customer-facing product, and alongside the teams whose work you are helping to reinvent. The structure is flat and fast. No shared queues, no bureaucratic overhead between having an idea and shipping it.
Whilst we are building something entirely new, Multiverse has an established product, existing infrastructure, and engineering teams in London and Berlin. You need to be as comfortable integrating existing systems and working across team boundaries as you are building new ones from scratch.<h2 class="jobSectionHeader">
What You Will Do</h2>
Own and deliver complete agent systems. You take a product problem and build the agent system that solves it. Architecture, implementation, evaluation, and production operation. You are responsible for the system working, not just for your code compiling.
Design context and retrieval strategies. What goes into the context window and what stays out is the most consequential design decision in an AI system. You design retrieval pipelines, conversation memory, summarisation strategies, and the chunking logic that makes context useful rather than noisy. You understand the cost and quality trade-offs at every layer.
Build evaluation frameworks. You define and implement the metrics that tell the team whether its AI systems are doing what they should. Accuracy, safety, helpfulness, domain-specific quality, latency. You build automated eval pipelines and human-in-the-loop review processes. You treat evaluation as an engineering discipline, not an afterthought.
Design tool integrations. Agents are only as capable as the systems they can reach. You design and build the tool layer: MCPs, APIs, data contracts, and the error handling that makes tool use reliable. You work closely with the wider engineering org building Multiverse's customer-facing product, whose systems your agents need to interact with.
Influence technical direction. You have opinions about how things should be built, and you back them up with evidence. You contribute to architectural decisions, push back when the team is heading in the wrong direction, and propose better approaches. You are not a team lead, but your technical judgement shapes what gets built and how.
Raise the bar through code review and pairing. You review code with rigour and give feedback that makes the team better. You pair with less experienced engineers on hard problems. You set a standard for what production-quality AI engineering looks like.
Use Claude Code as your primary development workflow. Claude Code is how this team builds. You set context, define constraints, review output critically, and augment the tool with skills and domain context. You are fluent in AI-assisted development and can mentor others in doing it well.<h2 class="jobSectionHeader">
What We Are Looking For</h2>
Production AI Agent Engineering
You have shipped AI systems that serve real users at meaningful scale. You understand the engineering challenges that make agent systems a different discipline from conventional software:<ul><li>
Context management. Designing what enters the context window and what stays out. Retrieval strategies, chunking approaches, conversation memory, summarisation. You know how context quality drives output quality and cost, and you have made these trade-offs in production.</li><li>
Model selection and routing. Choosing the right model for a task based on capability, latency, cost, and reliability. You have worked with multiple models and understand when a smaller, faster model is the right call.</li><li>
Cost engineering. Token economics, caching, prompt optimisation, batching. You know the difference between a prototype that works and a production system that works at a cost the business can sustain.</li><li>
Tool use and agent augmentation. Designing the tool surfaces that agents use to interact with external systems. Writing tool descriptions that models use reliably, handling failures gracefully, building integration layers that are composable rather than brittle.</li><li>
Evaluation. Building frameworks for assessing AI output quality: accuracy, safety, helpfulness, domain-specific criteria. You ship with eval, not after it.</li></ul>
Product Thinking
You do not wait for a spec. You understand the problem, figure out what needs to exist, and build it. On a small squad there is no gap between product thinking and engineering. You talk to users, understand their workflows, and identify the highest-value intervention.
This does not require product management experience. It requires the instinct to ask &ldquo;what problem are we solving and for whom?&rdquo; before &ldquo;what framework should we use?&rdquo;
Full-Stack Delivery
You work across the stack: LLM integration, backend services, data pipelines, and enough frontend to ship end to end. Agent systems do not fit neatly into service boundaries, and your ability to work across all of them is a practical requirement.
Communication
You explain technical decisions clearly to both engineers and the product and design people you work with day to day. You document your designs, write pull requests that tell a story, and give direct feedback without being abrasive.<h2 class="jobSectionHeader">
What Would Set You Apart</h2><ul><li>
Experience building AI systems in EdTech, regulated content, or domains where output quality has compliance or accreditation implications</li><li>
Background as a founding or early-stage engineer at a startup</li><li>
Published thinking or external contributions in AI engineering (talks, writing, open source)</li><li>
Experience with multi-agent coordination: task decomposition, handoff, shared state</li><li>
Practical experience with MCP (Model Context Protocol) or equivalent agent integration standards</li></ul><h2 class="jobSectionHeader">
What We Are Not Looking For</h2><ul><li>
Pure ML researchers without production engineering experience. We build products, not papers</li><li>
Narrow specialists. If you only do infrastructure, or only do model training, or only do frontend, this team needs broader range</li><li>
Engineers who need a detailed spec and a sprint plan before starting. We ship fast and iterate</li><li>
Candidates whose AI experience stops at wrapping LLM APIs. We need depth in context strategy, evaluation, tool design, and the systems engineering underneath</li><li>
Engineers who optimise for technical elegance over user outcomes. The architecture serves the product</li></ul>
Benefits<ul><li>
Time off - 27 days holiday, plus 5 additional days off: 1 life event day, 2 volunteer days, 2 company-wide wellbeing days (M-Powered Weekend) and 8 bank holidays per year</li><li>
Health &amp; Wellness- private medical Insurance with Bupa, a medical cashback scheme, life insurance, gym membership &amp; wellness resources through Wellhub and access to Spill - all in one mental health support</li><li>
Hybrid work offering - for most roles we collaborate in the office three days per week with the exception of Coaches and Instructors who collaborate in the office once a month</li><li>
Work-from-anywhere scheme - you'll have the opportunity to work from anywhere, up to 10 days per year</li><li>
Space to connect: Beyond the desk, we make time for weekly catch-ups, seasonal celebrations, and have a kitchen that&rsquo;s always stocked! 
</li></ul> 
Our Commitment to Diversity, Equity and Inclusion
We&rsquo;re an equal opportunities employer. And proud of it. Every applicant and employee is afforded the same opportunities regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. This will never change. Read our Equality, Diversity &amp; Inclusion policy here.
Our Commitment to Safeguarding
Multiverse is committed to safeguarding and promoting the welfare of our learners. We expect all employees to share this commitment and adhere to our Safeguarding Policy, our Prevent Policy and all other Multiverse company policies. Successful applicants will be required to undertake at least a Basic check via the Disclosure Barring Service (DBS).
For roles that will involve a Regulated Activity, successful applicants must also undergo an Enhanced DBS check, including a Children&rsquo;s Barred List check and a Prohibition Order check. Roles involving Regulated Activity may interact with vulnerable groups, therefore are exempt from the Rehabilitation of Offenders Act 1974 meaning applicants are required to declare any convictions, cautions, reprimands, and final warnings.
Providing false information is an offence and could result in the application being rejected or summary dismissal if the applicant has been selected, and possible referral to the police and the DBS.</div>

Senior AI Engineer - AI Transformation

<div>Multiverse is the upskilling platform for AI and Tech adoption.
We have partnered with 1,500+ companies to deliver a new kind of learning that's transforming today&rsquo;s workforce.
Our upskilling apprenticeships are designed for people of any age and career stage to build critical AI, data, and tech skills. Our learners have driven $2bn+ ROI for their employers, using the skills they&rsquo;ve learned to improve productivity and measurable performance.
In April 2026, we announced $70 million in strategic funding, led by Schroders Capital, with participation from StepStone Group, Lightspeed Venture Partners and General Catalyst. At an increased valuation of $2.1bn, the round makes us Europe&rsquo;s first EdTech double unicorn.
But we aren&rsquo;t stopping there. With a strong operational footprint and 800+ employees, we have ambitious plans to continue scaling. We&rsquo;re building a world where tech skills unlock people&rsquo;s potential and output. 
 
Join Multiverse and power our mission to equip the workforce to win in the AI era.<h2 class="jobSectionHeader">
The Role</h2>
Multiverse is the UK's largest apprenticeship provider and its first EdTech unicorn. The current state of AI presents a huge opportunity to reshape the future of education and workforce development. Multiverse is in a uniquely strong position to do that, and getting it right has implications beyond the company: for the UK tech sector and the broader economy.
The AI Transformation team exists to make that real, starting with Multiverse itself. This is not a team that bolts AI onto the edges of the business or ships a handful of internal productivity tools. The mandate is bigger: to rebuild how the company actually works, function by function, and to establish the engineering practices that make Multiverse an AI-first company from the core out.
That work matters twice over. Get it right inside Multiverse and we move faster, serve learners better, and operate at a level few organisations can match. But Multiverse also exists to build the workforce that every other company is reaching for. The way we transform ourselves becomes the standard we set for everyone else. You are not just changing one company, you are building the blueprint others will follow.
The team is one small, focused squad, accountable for outcomes end to end. You work closely with the wider engineering org building Multiverse's customer-facing product, and alongside the teams whose work you are helping to reinvent. The structure is flat and fast. No shared queues, no bureaucratic overhead between having an idea and shipping it.
Whilst we are building something entirely new, Multiverse has an established product, existing infrastructure, and engineering teams in London and Berlin. You need to be as comfortable integrating existing systems and working across team boundaries as you are building new ones from scratch.<h2 class="jobSectionHeader">
What You Will Do</h2>
Own the architecture of our internal agentic operating system. The team's work spans the full surface of how Multiverse operates. You own the technical architecture of our agentic operating system: the agent orchestration, context strategy, tool integrations, evaluation framework, and production operation. Your design decisions shape what is possible for human and AI teams at Multiverse
Ship production AI agent systems. This is a building role. You write code, review code, and own the quality of what goes to production. You will personally build and deliver significant agent systems. On a squad this size, nobody leads from a whiteboard.
Design multi-agent coordination. Task decomposition across agents, handoff protocols, shared state management, orchestration logic. You know the difference between agents that genuinely coordinate and agents that run sequentially and hope for the best. You design the patterns that make multi-agent systems reliable.
Build the evaluation and quality infrastructure. Automated eval pipelines, human-in-the-loop review systems, regression testing for prompt changes, domain-specific quality metrics. You treat evaluation as a first-class engineering concern and build the systems that make it possible at scale.
Drive cost engineering. Token economics, caching strategies, model routing, prompt optimisation. The cost profile of production AI systems requires active engineering attention, and you build the cost awareness and tooling into the architecture rather than bolting it on later.
Build the integration layer that makes existing Multiverse systems agent-accessible. APIs, MCPs, shared data contracts, and the tooling that connects agents to the platform, content systems, and the tools the company runs on. This means building real working relationships with engineering teams across London and designing interfaces that serve both sides well.
Set the standard. You define patterns for prompt management, retrieval, guardrails, and testing that the wider team and eventually the whole organisation adopts &mdash; and that, in time, shape how the companies who learn from Multiverse do this too. You do this through code, documentation, and architectural decisions, not through mandates.
Mentor the team. Code review, architectural guidance, pairing on the hardest problems. You are not a line manager, but your technical leadership directly shapes the growth of the engineers around you.<h2 class="jobSectionHeader">
What We Are Looking For</h2>
Production AI Agent Engineering
You have shipped multi-agent systems or complex AI products to real users. You understand the engineering challenges that make agent systems a distinct discipline:<ul><li>
Context management. Designing what enters the context window and what stays out. Retrieval strategies, chunking, conversation memory, summarisation, and the cost/quality trade-offs of each. You have made these decisions in production and seen the consequences.</li><li>
Model selection and routing. Choosing the right model for each task based on capability, latency, cost, and reliability. Building routing logic that matches work to the appropriate model rather than defaulting to one.</li><li>
Cost engineering. Token economics, caching, prompt optimisation, batching. You know the difference between a prototype that works and a production system that works at sustainable cost. You have built systems where cost was an engineering constraint, not someone else's problem.</li><li>
Tool use and agent augmentation. Designing what capabilities agents can reach: tool descriptions that models use reliably, failure handling, MCPs or equivalent interfaces. You understand that the quality of the tool layer determines whether agents are useful or fragile.</li><li>
Multi-agent coordination. Task decomposition across agents, handoff protocols, shared state, orchestration logic. You have built systems where multiple agents work together within a product domain and understand the architectural patterns that make coordination reliable.</li><li>
Evaluation and quality. Building eval frameworks for AI output: accuracy, helpfulness, safety, domain-specific criteria. Automated pipelines and human-in-the-loop review. You would not ship an agent system without a quality baseline.</li></ul>
Product Thinking and Entrepreneurial Instinct
On a small squad there is no gap between product thinking and engineering. You own the problem from user need to production system. You can sit with the people whose work you are transforming, understand their workflow, identify the highest-value intervention, and build it without waiting for a product manager to write a spec.
You have either built something yourself (a product, a startup, a project with real users) or operated with that founder mindset inside a larger organisation. You understand that speed matters and that shipping something useful beats polishing something theoretical.
AI-Native Engineering
You build with Claude Code daily. You set context and constraints before generating code. You review AI output critically. You augment the tool with skills, system prompts, and domain context to make it effective. This is how the team works, and you help define what good looks like.
Full-Stack Delivery
You work across the stack: LLM integration, backend services, data pipelines, and enough frontend to ship end to end. The boundaries between these layers dissolve in agent systems, and so should your willingness to work across them.
Communication
You can explain technical strategy to a CPO, walk a product manager through a cost trade-off, and give direct feedback in code review. You represent the team's technical approach in cross-functional forums with product, design, learning design, compliance, and other engineering teams. You document decisions, not just code.<h2 class="jobSectionHeader">
What Would Set You Apart</h2><ul><li>
Experience in EdTech, regulated content, or domains where AI output quality has compliance or accreditation implications</li><li>
Background as a founding engineer or technical co-founder</li><li>
Published thinking or external contributions in AI engineering (talks, writing, open source)</li><li>
Experience designing platform layers that other teams build on</li><li>
Practical experience with MCP (Model Context Protocol) or equivalent agent integration standards</li></ul><h2 class="jobSectionHeader">
What We Are Not Looking For</h2><ul><li>
Pure ML research without production engineering experience. We need builders</li><li>
Narrow specialism. This team works across the full stack of an AI product. If you only do infrastructure, or only do model training, or only do frontend, this is the wrong fit</li><li>
People who need a detailed spec, a sprint plan, and a standup before they can write a line of code. We ship fast and iterate</li><li>
Candidates whose experience is limited to wrapping LLM APIs in thin application layers. We need depth in agent architecture, context strategy, tool design, and multi-agent coordination</li><li>
Engineers who optimise for technical elegance over user outcomes. The architecture serves the product</li></ul>
Benefits<ul><li>
Time off - 27 days holiday, plus 5 additional days off: 1 life event day, 2 volunteer days, 2 company-wide wellbeing days (M-Powered Weekend) and 8 bank holidays per year</li><li>
Health &amp; Wellness- private medical Insurance with Bupa, a medical cashback scheme, life insurance, gym membership &amp; wellness resources through Wellhub and access to Spill - all in one mental health support</li><li>
Hybrid work offering - for most roles we collaborate in the office three days per week with the exception of Coaches and Instructors who collaborate in the office once a month</li><li>
Work-from-anywhere scheme - you'll have the opportunity to work from anywhere, up to 10 days per year</li><li>
Space to connect: Beyond the desk, we make time for weekly catch-ups, seasonal celebrations, and have a kitchen that&rsquo;s always stocked! 
</li></ul> 
Our Commitment to Diversity, Equity and Inclusion
We&rsquo;re an equal opportunities employer. And proud of it. Every applicant and employee is afforded the same opportunities regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. This will never change. Read our Equality, Diversity &amp; Inclusion policy here.
Our Commitment to Safeguarding
Multiverse is committed to safeguarding and promoting the welfare of our learners. We expect all employees to share this commitment and adhere to our Safeguarding Policy, our Prevent Policy and all other Multiverse company policies. Successful applicants will be required to undertake at least a Basic check via the Disclosure Barring Service (DBS).
For roles that will involve a Regulated Activity, successful applicants must also undergo an Enhanced DBS check, including a Children&rsquo;s Barred List check and a Prohibition Order check. Roles involving Regulated Activity may interact with vulnerable groups, therefore are exempt from the Rehabilitation of Offenders Act 1974 meaning applicants are required to declare any convictions, cautions, reprimands, and final warnings.
Providing false information is an offence and could result in the application being rejected or summary dismissal if the applicant has been selected, and possible referral to the police and the DBS.</div>

Staff AI Engineer - AI Transformation

Oho Group

<div>
 
 
 Founding AI Engineer | Early Stage AI Start-Up | London
 
 
 
 
 
 
 Founding AI Engineer required to join an extremely well funded, rapidly growing early-stage AI start-up with a world class founding team using AI to solve a real world problem. You&rsquo;ll be one of the first technical hires, working directly with the founders to design, build, and deploy LLM-powered systems that meaningfully shape the product and the company.
 
 
 
 
 
 
 You are:
 
 
 <ul>
 <li>
 2-6 years of professional experience, with strong software engineering fundamentals
 </li>
 <li>
 Proven hands-on experience building AI systems from scratch and deploying LLMs in production
 </li>
 <li>
 Proficiency in Python
 </li>
 <li>
 Academic Excellence: BSc, MSc or PhD in Computer Science, AI, Machine Learning, or related stem field from a top university
 </li>
 <li>
 Comfortable in chaos and thrive in fast paced environments
 </li>
 </ul>
 
 
 
 
 
 Bonus points:
 
 
 <ul>
 <li>
 Experience as an early employee at a start-up
 </li>
 <li>
 Experience working closely with product or founders
 </li>
 </ul>
 
 
 
 
 
 Why join?
 
 
 <ul>
 <li>
 Extremely competitive salary, meaningful equity, and benefits
 </li>
 <li>
 A genuine founding role with significant technical and product ownership
 </li>
 <li>
 Close collaboration with experienced, second-time founders who deeply understand the problem space
 </li>
 <li>
 Strong financial backing from top-tier investors
 </li>
 <li>
 The chance to help shape both the technology and culture from the ground up
 </li>
 </ul>
 
 
 
 
 Looking to join early, play a key role, and build AI systems that solve real-world problems? Let&rsquo;s chat.
 
 
 
 
 
 
 Founding AI Engineer | Early Stage AI Start-Up | London
 
 
</div>

Founding AI Engineer | Early Stage AI Start-Up | London

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