At a Glance
- Tasks: Build AI-powered solutions and collaborate with diverse teams to solve complex problems.
- Company: Join a leading tech firm committed to innovation and inclusivity.
- Benefits: Competitive salary, flexible hybrid work, and opportunities for learning and certification.
- Other info: Participate in hackathons and innovation challenges for hands-on experience.
- Why this job: Make a real impact in AI while developing your skills in a supportive environment.
- Qualifications: Basic software engineering skills and a passion for AI and continuous learning.
The predicted salary is between 50000 - 65000 £ per year.
Location: London
Salary: Competitive
Date: 9 Apr 2026
Requisition ID: 1700051
Contract: Permanent, full‑time
The opportunity
Organisations are moving rapidly from AI experimentation to operational adoption. However, many struggle to translate ideas into secure, scalable and reliable production solutions. As a Forward Deployed Engineer, you will sit at the intersection of software engineering, product thinking, and client delivery, working within a squad to build real AI‑powered solutions. You will collaborate with engineers, designers and client teams to turn complex problems into reliable systems. In this consultant role within the Forward Deployed Engineering team, you will develop foundational AI engineering and product skills within a delivery squad. You will learn how to design, build and integrate LLM/RAG features, contribute to discovery and user stories, and follow EY standards for quality, security and documentation while supporting demos and adoption.
What you’ll do
- Client‑facing engineering & delivery: Support discovery workshops by capturing requirements and helping translate them into user stories and acceptance criteria. Support demos, show‑and‑tells and feedback cycles; prioritise fixes and enhancements with the squad. Work effectively in diverse, multidisciplinary teams and produce client‑ready artefacts to agreed timelines.
- Solution design & implementation: Build LLM/RAG features and integrations under guidance, contributing clean, tested, maintainable code. Follow EY reference architectures, security patterns and evaluation baselines, contributing to documentation and reusable accelerators.
- Product mindset & continuous improvement: Contribute to documentation and reusable components/accelerators that help teams deliver consistently. Learn field lessons (what works in client environments) and feed that back through the squad’s delivery routines and artefacts.
What we’re looking for
- Essential skills & experience: Software engineering fundamentals (algorithms, data structures, APIs, microservice basics). Programming in Python and/or TypeScript; exposure to async patterns, testing, and version control (Git). LLM/RAG basics: embeddings, prompt design, retrieval patterns; willingness to learn evaluation and guardrails. Intro experience with data wrangling for structured and unstructured data; feature engineering fundamentals. Cloud fundamentals (Azure preferred); containers (Docker) and CI/CD exposure. Understanding of responsible‑AI principles, privacy and basic model‑risk concepts; eagerness to learn UK regulatory context (FCA, PRA, GDPR). Clear written and verbal communication; ability to operate in client environments and collaborate with security, risk and architecture teams. Growth mindset: curiosity, continuous learning, and ability to adapt quickly to new techniques and tools.
- Nice to have: Familiarity with ML concepts (regression, classification, clustering) and deep‑learning frameworks (PyTorch/TensorFlow). Foundational knowledge of data platforms (Spark/Databricks) and event/streaming patterns (e.g., Event Hub/Kafka).
Technical Certifications (preferred): Microsoft Azure AI Engineer Associate (AI‑102) — in progress or planned. Azure Data Scientist Associate — in progress or planned. AWS Machine Learning Specialty or Google Professional ML Engineer — welcome. Databricks and Kubernetes (CKA/CKAD) — welcome for future development.
How you work
You’re comfortable being hands‑on: coding, debugging, deploying, and iterating with users. You care about quality—but you’re pragmatic about what’s “good enough” to ship and improve safely, and you’re eager to learn from feedback. You can communicate clearly with both technical and non‑technical stakeholders as you build confidence in client settings.
What we offer
High‑impact work with leading organisations across sectors, within a collaborative engineering‑led AI team. You will benefit from:
- A structured FDE Academy and cohort learning experience
- Opportunities to participate in hackathons, innovation challenges and engineering showcases
- Learning and certification support across cloud and AI technologies
- Competitive compensation and benefits
- Flexible hybrid working arrangements depending on client needs
Travel & Working Model
Hybrid working and periodic travel to client sites across the UK (and occasionally internationally), discussed based on projects and location.
Inclusion and accessibility
EY is committed to building an inclusive culture where everyone can thrive. If you require adjustments or support during the recruitment process, we encourage you to let us know.
Consultant, AI Engineer TC FS 1 in London employer: 慨正橡扯
At EY, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters collaboration and innovation. As a Consultant in our AI Engineering team based in London, you'll engage in high-impact projects with leading organisations while benefiting from structured learning opportunities, competitive compensation, and flexible hybrid working arrangements. Our commitment to inclusion and continuous growth ensures that every employee can thrive and develop their skills in a supportive environment.
StudySmarter Expert Advice🤫
We think this is how you could land Consultant, AI Engineer TC FS 1 in London
✨Tip Number 1
Network like a pro! Reach out to people in your field on LinkedIn or at industry events. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Prepare for interviews by practising common questions and scenarios related to AI engineering. We recommend doing mock interviews with friends or using online platforms to get comfortable.
✨Tip Number 3
Show off your skills! Create a portfolio showcasing your projects, especially those involving Python, TypeScript, or AI solutions. This gives potential employers a taste of what you can do.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive!
We think you need these skills to ace Consultant, AI Engineer TC FS 1 in London
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the Consultant, AI Engineer role. Highlight your relevant skills in software engineering and AI, and show us how your experience aligns with what we're looking for.
Showcase Your Projects:If you've worked on any AI or software engineering projects, don’t hold back! Include links or descriptions of your work that demonstrate your coding skills and problem-solving abilities. We love seeing real examples!
Be Clear and Concise:When writing your application, keep it straightforward. Use clear language and avoid jargon where possible. We want to understand your experience and skills without having to decode complex terms.
Apply Through Our Website:We encourage you to submit your application through our website. It’s the best way to ensure your application gets into the right hands and shows us you're serious about joining our team!
How to prepare for a job interview at 慨正橡扯
✨Know Your Tech
Brush up on your software engineering fundamentals, especially algorithms, data structures, and APIs. Be ready to discuss your experience with Python or TypeScript, and don’t forget to mention any exposure to async patterns and version control with Git.
✨Show Your Problem-Solving Skills
Prepare to talk about how you've tackled complex problems in the past. Think of examples where you’ve built or integrated features, particularly LLM/RAG ones, and be ready to explain your thought process and the impact of your solutions.
✨Communicate Clearly
Since this role involves client-facing work, practice explaining technical concepts in simple terms. You might be asked to demonstrate how you would capture requirements or translate them into user stories, so clarity is key!
✨Embrace a Growth Mindset
Demonstrate your eagerness to learn and adapt. Share experiences where you’ve picked up new skills or tools quickly, and express your interest in continuous improvement, especially in the context of AI and cloud technologies.
