Forward Deployed AI Engineer
Posted 8 Oct 2026
Advertised as “AI & Data - Forward Deployed AI Engineer, Senior Manager”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 7+ yrs
- Management level
- People management
- Employment
- Hybrid
- Contract
- Full-time
- Visa sponsorship
- Not offered
No salary listed for this role.
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AI & Data - Forward Deployed AI Engineer, Senior Manager
General Information
Location: Dublin
Available for Work Visa Sponsorship: NO
Business Area: UKI AI & Data
Contract Type: Full-Time – Permanent
**
The Team and the opportunity**
As part of our AI & Data team, this role is pivotal in enabling our clients to derive significant value from AI. Through collaboration and domain expertise, our team embeds innovative AI & Data solutions into existing business areas, transforming data into strategic assets. We are looking for a Foward Deployed AI Engineer who is passionate about leveraging AI & data to solve complex problems and drive business outcomes.
As a Forward Deployed AI Engineer at EY, you will be a client-embedded technical leader responsible for translating AI ambition into trusted, enterprise-grade solutions. You will work closely with EY Partners, engagement teams, and senior client stakeholders to design, build, and deploy AI systems that operate effectively within complex, regulated, and large-scale environments.
This role blends deep hands‑on engineering, consulting judgment, and client‑facing delivery ownership. You will carry solutions from concept through production and enable clients to sustainably operate and evolve their AI capabilities.
Your Key Responsibilities
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Turn ambiguous business problems into concrete AI solutions by partnering with clients to understand workflows, constraints, success metrics, and incentives.
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Translate business challenges into well-scoped AI use cases (e.g., prediction, automation, retrieval, agent workflows).
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Define MVP scope, data requirements, and evaluation criteria jointly with stakeholders.
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Build fast prototypes using - LLMs, RAG pipelines, fine-tuning, agents, traditional ML models where appropriate.
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Design and implement AI architectures, including data ingestion, feature pipelines, embeddings, vector stores, model selection, tuning, and evaluation. Prompt design, guardrails, and system instructions.
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Integrate AI components with existing client systems (APIs, data warehouses, SaaS platforms). Optimize for latency, cost, reliability, and scalability.