Forward Deployed Engineer - Applied AI - Senior - Financial Services - Consulting
Ernst and Young3 months ago
Base Salary
$107k - $201k/yr
Responsibilities
- Design, develop, test, deploy, and support production-grade AI/ML, generative AI, and intelligent automation solutions.
- Build and integrate LLM, RAG, and agentic components into enterprise applications and platforms.
- Translate business and user requirements into technical designs, APIs, workflows, and implementation plans.
- Contribute to system design across service boundaries, orchestration layers, data flows, security controls, and external integrations.
- Debug, troubleshoot, and remediate production LLM and agentic systems across retrieval, orchestration, and generation layers.
- Improve the performance, resilience, maintainability, and cost efficiency of deployed AI systems.
- Support project delivery through estimation, documentation, status communication, risk identification, and design reviews.
- Partner with Development, Engineering, Product, Data, Architecture, project leadership, risk, legal, and audit teams.
- Use AI-assisted software engineering tools such as Claude Code, Codex, or equivalent agentic coding platforms.
Requirements
- Bachelor’s degree is preferred; an MBA or MS is also preferred.
- At least 3 years of applied engineering experience, including meaningful AI/ML engineering experience.
- Hands-on proficiency in Python and production-quality software engineering for LLM pipelines and agentic applications.
- Experience designing, building, and maintaining production-grade LLM applications and end-to-end data-to-output pipelines.
- Experience with retrieval-augmented systems, chunking, embedding pipelines, vector search, semantic retrieval, and retrieval optimization.
- Experience designing agentic systems with multi-agent orchestration, tool use, memory, retries, fallbacks, and context-window management.
- Experience implementing LLM evaluation frameworks for correctness, quality, safety, and business KPIs.
- Familiarity with structured and unstructured data preparation, API integration, containerization, cloud deployment, testing, and data quality validation.
- Ability to explain complex AI system behavior and trade-offs to technical and non-technical stakeholders in regulated environments.
- Preferred qualifications include consulting experience, model observability, LLM fine-tuning, inference optimization, AI security, responsible AI, data pipeline design, cloud ML platforms, and AI-assisted development tools.
Benefits
- Hybrid client-serving model, with an expectation that most external client-facing employees work in person 40–60% of the time over an engagement, project, or year.
- Medical and dental coverage, pension and 401(k) plans, and paid time off options.
- Flexible vacation policy plus EY-paid holidays, winter and summer breaks, personal/family care, and other leaves of absence.
- Flexible environment, professional development, future-focused skills training, and globally connected teams.
Tech Stack
Categories
Forward Deployed
About Ernst and Young
Ernst & Young (EY) provides audit/assurance, tax, consulting, strategy and transactions services to enterprises, financial institutions, and public‑sector clients. Structured as a global network of partner‑owned member firms, it sells professional services on a fee basis, including a dedicated Financial Services Organization for banking, insurance, and capital markets. Headquartered in London, EY was formed in 1989 from the merger of Ernst & Whinney and Arthur Young, and operates in 150+ countries.