(Senior) Forward-Deployed Data & AI Engineer

Description

Our Applied AI Engineering team designs and builds solutions across the spectrum — from data processing, through AI and ML workflows, to agentic AI — often in complex, regulated environments such as financial services. We work at the edge of delivery: embedded with the business, moving fast, and proving what's worth building. We're now looking for a Forward-Deployed Data & AI Engineer to sit at that edge — turning ambiguous business problems into working solutions, fast, from the raw data all the way to the AI. As a Forward-Deployed Data & AI Engineer within EY's Applied AI Engineering team, you will be embedded directly with our clients' business teams — helping to find high-value problems and turning them into working solutions in weeks rather than months. You work at the early discovery and prototyping as well as productization stage of delivery: hands-on, agile, and close to the business. This is a full-spectrum build role — you'll wrangle messy, real-world data into lightweight pipelines and data products, and then build the AI/ML workflows, GenAI and agentic solutions on top of them. Together with your Tech Lead and the wider pod, you'll prove (or disprove) value quickly, and help shape the requirements a validated idea needs before it's moved to production to scale. This is a hands-on engineering role for a versatile builder who is energised by ambiguity, comfortable across both the data and the AI, enjoys working directly with business users, and would rather ship a rough working demo than a polished slide. EY.AI – in this role, you will work in an environment where AI agents are an integral part of the team and daily project delivery. You will leverage role-specific AI solutions that help analyse data, generate recommendations, and design innovative solutions, enabling you to move faster from insight to action and create value for clients. Your scope of duties Work embedded with client business teams to understand problems in context and turn them into working solutions — end to end, from data to AI. Rapidly profile and wrangle source data, and stand up lightweight pipelines and integrations to get prototypes working — ingesting, cleaning, transforming and joining messy, real-world data. Design, build and demonstrate AI/ML workflows, GenAI and agentic-AI solutions on top of that data, using cloud-native services. Iterate fast on feedback — build, demo, learn, repeat — to prove or kill ideas quickly. Help distil clear data and solution requirements, source mappings and de-risked designs. Build responsibly from the start — mindful of data quality, lineage, privacy, guardrails and compliance (e.g. EU AI Act, GDPR, data residency). Collaborate closely with your Tech Lead, the architects and business stakeholders. Contribute to the team's reusable accelerators, connectors, assets and engineering practices.

What we offer

• Opportunity to work on transformation programmes for leading financial institutions across insurance and reinsurance sectors.

• Exposure to modern cloud technologies, advanced analytics, reporting transformation, and AI-driven solutions.

• Collaboration with multidisciplinary teams combining business expertise, engineering, analytics, and AI.

• Participation in projects for some of the largest market leaders.

• Best-in-class AI tools as a core part of your daily work, helping you maximise your impact and focus on the most challenging problems.

• Diverse and international projects offering continuous opportunities for learning and professional growth.

• Professional and financial support in obtaining recognised qualifications and certificates.

• EY Badges - global certification of your competencies and the opportunity to earn an MBA title from the prestigious Hult International School of Business.

• Career Counselor - professional, one-to-one guidance on career building and development.

• Psycho-educational platform - a package of free consultations with specialists in the broad field of mental health and personal development and access to educational activities.

• Benefit programme offering private healthcare with additional preventive examinations, life insurance, tickets, team sports, language learning platform, sports cards and much more (available online and offline).

Our requirements

• A bachelor’s degree in Computer Science, Engineering or a related field, and

• For senior position approximately 3+ years of hands-on data / AI / software engineering experience (or a master's degree and approximately 2 years).

• Strong Python and SQL, with experience both building data pipelines (ETL / ELT) and building AI/ML or GenAI solutions.

• Experience with LLM / agent frameworks (e.g. Semantic Kernel, Azure AI Agent Service, LangChain, LangGraph, Bedrock Agents) and retrieval-augmented generation (RAG) patterns.

• The ability to work with data end to end — profiling, wrangling, integrating and modelling messy, real-world data.

• Hands-on cloud experience — ideally Microsoft Azure (e.g. Azure OpenAI, Azure AI Foundry / Azure Machine Learning, Azure Functions, Azure Data Factory, Microsoft Fabric / Synapse), and/or AWS (Bedrock, SageMaker, Lambda, Glue) or Google Cloud (Vertex AI, BigQuery, Dataflow).

• Good communication skills and comfort working directly with non-technical stakeholders.

Ideally, you'll also have

• Financial-services or other regulated-industry experience, and awareness of relevant controls (AML/KYC, EU AI Act, GDPR, data residency).

• Experience preparing data for AI/ML or GenAI — features, embeddings and retrieval corpora.

• Rapid-prototyping, hackathon or startup experience.

• Exposure to MLOps / DataOps / LLMOps and to preparing prototypes for production handoff.

• Familiarity with modern data tooling (e.g. dbt, Spark, Kafka).

• Cloud certifications (e.g. Azure, AWS or Google Cloud).

• A consulting or product background.

Recruitment stages

• Apply. If this is your first time applying, create your profile and upload your CV.

• Let's talk. You will meet Talent Acquisition professionals and representatives of your future team.

Skills and attributes for success

• Comfort with ambiguity and messy data — you can make progress without clean sources or a complete specification.

• Full-spectrum, hands-on craft — SQL and Python, data pipelines, ML workflows, and LLM / agent frameworks — across both the data and the AI.

• Speed and a demo-first mindset — you build the simplest thing that proves the point, and you'd rather show than tell.

• Business and domain awareness — you keep the user's real problem in focus and understand what the data means, not just its shape.

• Collaboration and curiosity — you learn fast and work well in a small, fast-moving team.