Overview
Forward Deployed Engineer combining software engineering, product thinking, and client delivery to build real AI-powered solutions within a delivery squad. You will collaborate with engineers, designers and client teams to translate complex problems into reliable systems and develop foundational AI engineering and product skills, including design, build and integration of LLM/RAG features, supporting discovery and user stories, and adhering to EY standards for quality, security and documentation.
Responsibilities
- Client-facing engineering and delivery: support discovery workshops by capturing requirements and translating 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 and implementation: build LLM/RAG features and integrations under guidance, contributing clean, tested, maintainable code.
- Follow EY reference architectures, security patterns and evaluation baselines; contribute to documentation and reusable accelerators.
- Product mindset and continuous improvement: contribute to documentation and reusable components/accelerators that help teams deliver consistently. Learn field lessons from client environments and feed back through the squad’s delivery routines and artefacts.
Qualifications and Skills
- 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).
Travel and Location
Hybrid working and periodic travel to client sites across the UK (and occasionally internationally), discussed based on projects and location.
What we offer
High-impact work with leading organisations within a collaborative engineering-led AI team. Includes a structured FDE Academy and cohort learning, opportunities to participate in hackathons and innovation challenges, learning and certification support across cloud and AI technologies, competitive compensation and benefits, and flexible hybrid working arrangements depending on client needs.
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