ai engineer

machine learning engineer

senior software engineer

data engineer

software engineer

applied ai engineer europe

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

Station

<div>
 
 
 Python Engineer
 
 
 
 
 
 
 
 (AI / LLM Focus)
 
 
 
 
 
 
 London (Hybrid) | 🚀 Fintech Scale-Up
 
 
 
 
 
 We&rsquo;re partnering with a fast-growing fintech company in London that&rsquo;s redefining how financial data is leveraged through cutting-edge AI. They&rsquo;re now looking for a Python Engineer with strong experience in AI and Large Language Models (LLMs) to help build intelligent, scalable products used by thousands of customers.
 
 
 
 
 
 This is a high-impact role where you&rsquo;ll work closely with product, data, and engineering teams to bring AI-driven features into production.
 
 
 
 
 
 
 What you&rsquo;ll be doing:
 
 
 <ul>
 <li>
 Designing and building scalable backend systems using Python
 </li>
 <li>
 Developing and integrating LLM-powered features into core products
 </li>
 <li>
 Working with modern AI frameworks (e.g. OpenAI, LangChain, Hugging Face)
 </li>
 <li>
 Collaborating with data scientists to productionise models
 </li>
 <li>
 Improving system performance, reliability, and scalability
 </li>
 <li>
 Contributing to architectural decisions in a growing engineering team
 </li>
 </ul>
 
 
 
 
 
 What they&rsquo;re looking for:
 
 
 <ul>
 <li>
 Strong experience with Python in a production environment
 </li>
 <li>
 Hands-on experience with LLMs or applied AI (prompt engineering, fine-tuning, RAG, etc.)
 </li>
 <li>
 Experience building APIs and backend services
 </li>
 <li>
 Solid understanding of cloud platforms
 </li>
 <li>
 Ability to work in a fast-paced, product-driven environment
 </li>
 </ul>
 
 
 
 
 
 Why join?
 
 
 <ul>
 <li>
 Work on genuinely impactful AI products in fintech
 </li>
 <li>
 Strong engineering culture with a focus on innovation
 </li>
 <li>
 Backed by top-tier investors
 </li>
 <li>
 Competitive salary + meaningful equity
 </li>
 <li>
 Flexible hybrid working in London
 </li>
 </ul>
 
 
</div>

Python Developer

Accenture

<div><h2 class="jobSectionHeader">Description</h2>
You build the systems that actually make AI work in enterprise environments, not demos, not prototypes that stall after a pilot, but production agentic architectures running inside real client organizations. The difference between an AI Engineer and what we are looking for is straightforward: you have shipped a multi-agent system in production, you have owned the eval harness, and you know what happens when your agent fails at 2am because you have lived it.
As an AI Engineer (Agentic/Applied), you will design, build, and deploy production-grade agentic AI systems across the full enterprise technology stack. You will work directly with client engineering teams, lead technical design sessions, and build reusable patterns and accelerators that scale beyond individual engagements.
This sits at the heart of the AI engineering talent market, demand is growing faster than supply and will continue to do so. We offer what no single product company can: breadth across every industry, every enterprise technology stack, and every level of organizational complexity, combined with vendor fellowship access inside Anthropic, OpenAI, Microsoft, and Google engineering teams and a direct pathway to the Forward Deployed Engineer programme.<h2 class="jobSectionHeader">
Key Responsibilities</h2><ul><li>
Design and build production-grade agentic systems end-to-end: multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability</li><li>
Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window engineering and tuning against real quality targets</li><li>
Integrate and abstract across multiple LLM providers &mdash; OpenAI, Anthropic, Vertex AI, and open-source models &mdash; with fallback routing, token, cost, and latency management</li><li>
Implement LLMOps in production: eval harnesses with real quality metrics, prompt versioning, observability tooling (LangSmith, Braintrust, or equivalent), cost and safety monitoring</li><li>
Embed directly with client engineering teams to design, prototype, and deploy agentic solutions &mdash; workshops, proofs of concept, code-with sessions, and architecture walkthroughs</li><li>
Build reusable patterns, accelerators, and playbooks that scale beyond the individual client engagement and enable the next one to start faster</li><li>
Define and use metrics to measure agent accuracy, latency, safety, and cost-effectiveness; present findings and recommendations to client stakeholders in business terms</li></ul></div>
 
<div><h2 class="jobSectionHeader">Basic Qualifications</h2><ul><li>
5+ years of software engineering experience in production environments</li><li>
Minimum 1 year of hands-on experience designing and deploying agentic AI solutions in a production environment &mdash; non-negotiable</li><li>
Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent &mdash; at production depth, not tutorial level</li><li>
Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs</li><li>
RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering</li><li>
LLMOps fundamentals: eval harness design, prompt versioning, and production observability</li><li>
Cloud-native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, and IaC (Terraform or Helm)</li><li>
Strong Python; Java or equivalent backend language acceptable; production debugging and observability experience</li><li>
Quality of experience is weighted over years, a candidate who has shipped three production agentic systems in four years is preferred over a generalist with passive AI exposure</li></ul></div>

AI Native SW Engineering Specialist

Codurance

<div>Codurance is a global software consultancy, focused on raising the bar in the software industry through supporting tech communities and working with our clients. We help our clients build remarkable digital products, evolve strategic legacy systems, and equip them to continuously deliver value through the adoption of quality practices and expertise.
We are looking for a Contract Senior Data Engineer who shares our values of craftsmanship, pragmatism, and professionalism. You understand that data engineering is, at its core, great software engineering, applied at scale, with rigour, and with production in mind.
Databricks is our primary data engineering platform, and you will be expected to use it fluently, but the goal is always durable, well-crafted data systems, not tool advocacy.
This is an initial 6-month contract, inside IR35.
What You&rsquo;ll Work On
Our data engineering engagements fall into three primary shapes:<ul><li>
Data Foundations</li><li>
Data Modernisation, re-platforming legacy ETL, introducing automated testing and data contracts, and aligning data models with application domains</li><li>
AI-Ready Data Enablement, preparing data systems to safely support AI and ML use cases, including feature pipelines, scalable compute, and guardrails around data quality and lineage</li></ul>
These engagements sit within wider software modernisation and platform evolution work, not as standalone data projects. You&rsquo;ll work alongside software craftspeople, architects, and client engineers, with data as one part of a broader delivery picture. 
Requirements 
You consider yourself a data engineer with strong software engineering instincts. You care deeply about the quality, testability, and operability of what you build, not just whether it runs.
You are comfortable working in small increments, tightening feedback loops, and letting tests inform your pipeline design. You understand that data systems live in production long after they are first delivered, and you build with that in mind.
You are a team player who values frequent collaboration with software engineers, data scientists, business analysts and client stakeholders alike. You invest in your craft, stay curious about emerging tooling, and are enthusiastic about sharing what you learn.
Data Engineering Practices
You have solid experience applying software engineering disciplines to data: writing testable pipelines, using version control rigorously, and treating data models as first-class software artefacts. You are familiar with data contracts, schema evolution, and the principles of incremental delivery applied to data work.
Platform and Architecture
You have hands-on experience with Databricks and are comfortable across its core capabilities: Delta Lake, Unity Catalog, Databricks Workflows, and notebook-to-production pipeline patterns. You have a working understanding of lakehouse architecture and medallion design, and can articulate trade-offs clearly to clients and colleagues.
Beyond Databricks, experience with the broader data engineering landscape, including tools such as dbt, Apache Spark, and data quality frameworks, is a plus.
Data Engineering for ML and AI
You understand how machine learning and AI systems consume data, and you build the pipelines that feed them to production. You are comfortable with engineering reproducible feature pipelines, managing feature tables in Unity Catalog, and using MLflow for experiment tracking and model registry so that training data and model lineage stay traceable. You prepare and version the datasets that models depend on, and you treat that data with the same rigour you apply to any production system.
You are familiar with the data engineering behind modern AI use cases: building embedding and retrieval pipelines, populating and maintaining vector stores, and assembling the evaluation and ground-truth datasets that make model behaviour measurable.
Software Engineering Foundations
Your data engineering work is grounded in solid software fundamentals. You understand clean code principles, meaningful abstraction, and the difference between code that works and code that can be maintained. You are familiar with CI/CD applied to data pipelines and are comfortable contributing to infrastructure-as-code environments.
Observability and Production Readiness
You understand what it means to run data systems in production. This includes pipeline monitoring, alerting, data quality validation, lineage tracking, and the operational disciplines needed to maintain trust in data over time.
Cloud Platforms
You have experience working on at least one major cloud provider (AWS or Azure) and are comfortable with the infrastructure patterns that underpin modern data platforms. Familiarity with cloud-native services for storage, compute, and orchestration is expected.
Collaboration and Client Engagement
You can work effectively within a client&rsquo;s environment and communicate clearly with both technical and non-technical stakeholders. You understand how to manage expectations, navigate legacy constraints, and make pragmatic decisions under real-world delivery conditions.
Legal / Eligibility
You must be eligible to work in the UK. 
Benefits</div>

Senior Data Engineer (Contract)

Compare the Market

<div>
 
 Job Description
 
 
 
 
 Machine Learning Engineer
 
 
 
 
 Function:
 
 Data &amp; AI Solutions
 
 
 
 Location:
 
 Hybrid, London office
 
 
 
 Curious about what&rsquo;s next?
 
 
 
 So are we. Join Compare the Market and help to make financial decision making a breeze for millions.
 
 
 At Compare the Market, we&rsquo;re a purpose-driven business powered by tech and AI. We&rsquo;re building high-performing, results-driven teams with the skills, mindset, and ambition to deliver outcomes at pace. Every role here plays a part in driving our mission forward, and we create an environment where you can bring your authentic self, grow a truly characterful career, and see the direct impact of your work on the lives of our customers.
 
 
 We&rsquo;ve carved a meerkat-shaped niche and we&rsquo;re looking for ambitious, curious thinkers who thrive in a fast-moving, high-impact environment. If you love accountability, embrace challenge, and want to make a real difference, you&rsquo;ll fit right in.
 
 
 
 We&rsquo;d love you to be part of our journey.
 
 
 
 Compare the Market is building AI systems that connect directly to how millions of people in the UK find and buy financial products - and as a Machine Learning Engineer, you will play an important role in making those systems production-ready. That means contributing to the engineering that takes ML models and AI capabilities from experimentation into reliable, scalable production use: the pipelines, the deployment tooling, the monitoring, and the shared components that make the whole thing work.
 
 
 This is a hands-on role where you will grow your technical skills across a fast-moving and expanding set of AI capabilities and systems. You will work closely with data scientists, engineers, and product teams - delivering well-scoped ML and AI features, contributing to engineering standards, and building your understanding of what good looks like in a modern ML engineering function.
 
 
 
 Some Of The Great Things You'll Be Doing
 
 
 
 
 ML Engineering and AI Systems
 
 
 
 <ul>
 <li>
 Deliver well-scoped ML and AI system components end-to-end, from design through to testing, deployment, and production support
 </li>
 <li>
 Contribute hands-on code to model packaging, deployment, and lifecycle automation
 </li>
 <li>
 Build and improve systems that monitor model performance, drift, reliability, and operational health
 </li>
 <li>
 Support both batch and real-time ML workloads depending on use case requirements
 </li>
 <li>
 Work on emerging AI and LLM-powered capabilities, helping integrate modern AI techniques into production systems where they can deliver real user value
 
 
 </li>
 </ul>
 
 Platform and Standards
 
 
 
 <ul>
 <li>
 Contribute to the evolution of our internal ML and AI platform to support experimentation, governance, and collaboration
 </li>
 <li>
 Apply and help promote best practices for ML and AI system design, including reproducibility, testing, CI/CD, model and agent observability and evaluation
 </li>
 <li>
 Contribute to shared tools and libraries that accelerate safe, efficient, and scalable ML development
 
 
 </li>
 </ul>
 
 Collaboration
 
 
 
 <ul>
 <li>
 Work closely with data scientists to help productionise experimental models and turn prototypes into robust services
 </li>
 <li>
 Contribute to code reviews, providing and receiving constructive feedback
 </li>
 <li>
 Participate in architecture and design discussions, raising considerations around scalability, reliability, and system interactions
 
 
 </li>
 </ul>
 
 Culture and Innovation
 
 
 
 <ul>
 <li>
 Contribute to a culture of engineering excellence, collaboration, and continuous learning
 </li>
 <li>
 Stay curious about emerging tools and approaches in MLOps and applied AI, and bring ideas for improvement to the team
 </li>
 <li>
 Support responsible AI practices, contributing to explainability, auditability, and fairness in ML systems
 
 
 </li>
 </ul>
 
 The Essentials
 
 
 
 
 What we'd like to see from you:
 
 
 
 <ul>
 <li>
 Some experience delivering ML or AI components in a production or near-production environment
 </li>
 <li>
 Solid software engineering skills in Python, with an understanding of how to write maintainable, testable code
 </li>
 <li>
 Familiarity with ML tooling and platforms such as Databricks, MLflow, Airflow, or equivalent
 </li>
 <li>
 Familiarity with CI/CD pipelines and how they apply to ML systems
 </li>
 <li>
 An understanding of ML lifecycle challenges - versioning, testing, monitoring, governance
 </li>
 <li>
 Good collaboration and communication skills, with an ability to work effectively across data science, engineering, and product teams
 
 
 </li>
 </ul>
 
 Nice to Have
 
 
 
 <ul>
 <li>
 Exposure to LLM-based systems - for example prompt engineering, RAG, or orchestration frameworks such as LangGraph or LangChain
 </li>
 <li>
 Familiarity with multi-step AI patterns - systems where models plan, retrieve information, and take sequences of actions
 </li>
 <li>
 Active personal use of AI-assisted or agentic coding tools, and an interest in how similar patterns could be applied to automate and accelerate ML engineering workflows - candidates who are exploring this space, even at an early stage, are encouraged to talk about it
 </li>
 <li>
 Experience or interest in financial services, insurance, or another regulated sector
 </li>
 <li>
 Interest in responsible AI and ML model governance
 
 
 </li>
 </ul>
 
 Why Compare the Market?
 
 
 
 We&rsquo;re a business built for pace and performance. Here, you&rsquo;ll be encouraged to think differently, act boldly, and deliver brilliantly in a culture that values results and rewards progress.
 
 
 We believe diverse teams make better decisions, and we&rsquo;re committed to creating an inclusive workplace where everyone feels empowered to grow, contribute, and thrive.
 
 
 If you&rsquo;re ready to stretch yourself, raise the bar, and grow with a team that&rsquo;s serious about performance, innovation, and purpose, we&rsquo;d love to hear from you.
</div>

Machine Learning Engineer

Compare The Market

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

Senior Machine Learning Engineer - Customer AI

Futurice

<div>
 
 Join us!
 
 
 
 We&rsquo;re always happy to hear from people who are eager to learn and grow, and share our values. Read more about the role and apply.
 
 
 
 About The Role
 
 
 
 As a Senior AI Engineer at Futurice, you'll be part of our growing AI &amp; Data team, designing and building the intelligent applications and workflows that sit at the heart of our client work. You'll be shaping solutions, advising clients, and helping define how Futurice builds and delivers AI at scale.
 
 
 You'll work across the full lifecycle: from early-stage ideation and proposals through to building and deploying production-quality, cloud-native GenAI applications for clients across industries like media, retail, and automotive. You'll also mentor junior engineers and make meaningful contributions to our internal AI practices.
 
 
 
 Skills &amp; Requirements
 
 
 
 
 What we're looking for:
 
 
 
 <ul>
 <li>
 5+ years of hands-on experience &ndash; in AI or applied data-intensive software engineering, or equivalent experience. A track record of taking projects from idea to production is what matters most.
 </li>
 <li>
 Proficiency in Python &ndash; You write clean, maintainable, production-quality code. You have experience taking systems from idea to production and keeping them running.
 </li>
 <li>
 Modern GenAI tooling &ndash; You have hands-on experience with libraries and tools like Pinecone, HuggingFace, LangChain/LangSmith, or CrewAI, and you know how to pick the right tool for the job.
 </li>
 <li>
 Understanding of agentic architectures &ndash; You're comfortable designing systems involving tool use, orchestration, evaluation, and multi-agent patterns, and you understand the trade-offs involved.
 </li>
 <li>
 Experience with RAG systems &ndash; You have a solid understanding of retrieval-augmented generation systems and pipelines, and you've built them in real-world contexts.
 </li>
 <li>
 Cloud platform experience &ndash; You're experienced with at least one of Azure, AWS, or GCP, and comfortable deploying and maintaining systems in production.
 </li>
 <li>
 Interest in client-facing work &ndash; You enjoy the full picture. You're as happy in a client workshop or contributing to a proposal as you are building. You can explain complex ideas clearly and build trust with clients.
 </li>
 <li>
 Curiosity over ego &ndash; You approach disagreement with empathy, learn from feedback, and care more about getting things right than being right.
 
 
 </li>
 </ul>
 You don't
 
 need
 
 experience in everything in the world of GenAI, we know it moves fast. We're looking for people who are strong in a few areas and take the initiative to grow in others. Tell us what you've been exploring lately!
 
 
 What you'll do
 
 
 
 In This Role, You Will
 
 
 
 <ul>
 <li>
 Design and build cutting-edge AI applications &ndash; from MCP servers and voice-to-voice real-time apps to RAG bots and multi-agent systems. You'll work on technology that's genuinely new territory.
 </li>
 <li>
 Advise clients on AI strategy &ndash; join workshops, contribute to proposals, and help clients understand what's possible and what's right for them. You'll be a trusted voice in conversations with clients, shaping direction alongside them.
 </li>
 <li>
 Build demos and PoCs &ndash; fast, purposeful, and client-ready. You know how to show something compelling without over-engineering it.
 </li>
 <li>
 Mentor junior engineers &ndash; share knowledge, review code thoughtfully, and help grow the team around you.
 </li>
 <li>
 Shape our AI practice &ndash; contribute to shared tools, frameworks, and ways of working. How Futurice delivers AI is something we build together.
 
 
 </li>
 </ul>
 
 Next steps - who you&rsquo;ll meet and for what
 
 
 
 We aim to make the process clear, human, and respectful of your time:
 
 
 <ul>
 <li>
 Talent screening call (30m) &ndash; with someone from our People Team to learn about you, your ambitions, and answer your questions about the role.
 </li>
 <li>
 Skills-based interview (45m) &ndash; a Skills-based interview with our technical team.
 </li>
 <li>
 Coding Task (1 hour) - you can either bring your own code/architecture to walk us through, or complete a task provided by Futurice.
 </li>
 <li>
 Values interview (45m) &ndash; with a cross-functional duo to explore how you work, what drives you, and how you align with our culture of care, trust, transparency, and continuous improvement.
 
 
 </li>
 </ul>
 
 What Futurice Offers You
 
 
 
 We use a transparent salary model based on your skills, responsibilities, and impact. The salary band for this role is
 
 &pound;60,000 &ndash; &pound;90,000
 
 depending on experience.
 
 
 We also believe in care, trust, transparency, and continuous improvement. Here&rsquo;s what that looks like in practice:
 
 
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 A supportive, values-driven team culture where individuality is celebrated.
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 A personal learning budget (&pound;1,200/year), mentoring, and knowledge-sharing sessions.
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 Flexible, remote-first working with colleagues across the UK and EU.
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 Private health insurance (WPA), pension contributions (6%+), and wellbeing support (&pound;50/month via Juno).
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 25 days holiday + bank holidays + your birthday off &mdash; increasing by 1 day per year after 3 years&rsquo; service (capped at 30).
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 A tiered parental leave policy (16 weeks maternity, 4 weeks partner, full pay depending on tenure).
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 EV salary sacrifice scheme (depending on tenure).
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 A clear career framework with review cycles every 6 months and a genuine path to a permanent role.
 
 
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 What's It Like To Work Here
 
 
 
 Ever since Futurice was founded, we&rsquo;ve focused on creating a workplace people genuinely enjoy, Mondays included. We&rsquo;ve built a welcoming culture where trust, kindness, and well-being matter as much as great work. Our atmosphere is open and warm, whether you&rsquo;re in the office or working remotely. You&rsquo;ll quickly feel part of the community where coffee chats, virtual fikas, or after-work hangouts happen naturally.
 
 
 Because our culture is shaped by all of us, every day, you&rsquo;ll have an opportunity to influence how we work, connect, and evolve as a community. Across countries, teams, and time zones, everyone brings their own character, helping shape how we work, connect, and evolve together.
 
 
 At Futurice, new joiners aren&rsquo;t expected to fit into a mold. Come as you are, whatever your background. We believe in the power of different perspectives, and diversity is a key part of our success at delivering customer value when they trust us with some of their most challenging and inspiring challenges. You can read more about our DE&amp;I commitment here.
 
 
 
 Please note: Unfortunately we won&rsquo;t be able to offer visa sponsorship for this role.
 
 
 
 We help organizations unlock new value by combining data, AI, design, and strategy. Our promise is simple: faster, smarter delivery; AI-powered decisions; and exceptional, data-driven experiences. We deliver clarity, and clarity delivers results.
 
 
 Our vision is to redefine organizations with data and AI. Large enterprises work with us to build smarter services, accelerate decision-making, and strengthen their competitive edge.
 
 
 Founded in 2000, Futurice has grown into an 800+ person community across Europe. Our culture is built on trust, transparency, care, and continuous improvement. We offer a collaborative, international environment where ambitious people learn, grow, and make a real impact.
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Senior AI Engineer

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