Forward Deployed Engineer - Applied AI - Senior - Financial Services - Consulting

About the role

The opportunity supports the delivery of solution or infrastructure development services for AI/ML initiatives, applying strong technical capability and hands-on engineering experience. This role requires a deep understanding of business and user requirements, and the ability to translate them into sound technical designs and implementation plans.

Responsibilities

• Design, develop, test, deploy, and support production-grade AI/ML, generative AI, and intelligent automation solutions.
• Solve complex technical problems through coding, debugging, testing, troubleshooting, and structured design remediation.
• Translate business and user requirements into technical designs, APIs, workflows, and supportable implementation patterns.
• Build and integrate LLM, RAG, and agentic solution components into enterprise applications and platforms.
• Contribute to system design across service boundaries, orchestration layers, data flows, security controls, and external integrations.
• Support project delivery through disciplined execution, estimation, documentation, status communication, and risk identification.
• Partner with Development, Engineering, Product, Data, Architecture, and project leadership teams to deliver high-value AI capabilities.
• Improve performance, resilience, maintainability, and cost efficiency of deployed AI systems.
• Participate in architecture and design reviews, providing thoughtful trade-off analysis and implementation input.
• Use modern AI-assisted software engineering tools such as Claude Code, Codex, or equivalent agentic coding platforms as part of day-to-day engineering delivery.

Requirements

Requires a Bachelor’s degree preferred. 3+ years of applied engineering experience, including meaningful experience in AI/ML engineering roles. Demonstrated experience in the following areas will be a huge plus:

• Ability to build and maintain model observability pipelines including tracing of multi-step agentic reasoning chains, output degradation detection, and behavioral drift monitoring in production.
• Familiarity with LLM fine-tuning approaches including instruction tuning and preference optimization, with an understanding of when fine-tuning is appropriate versus prompt-based solutions.
• Familiarity with inference optimization principles — latency, throughput, and cost management — to support scalable and cost-effective LLM deployment.
• Familiarity with AI security considerations relevant to LLM systems, including prompt injection risks, adversarial input handling, and audit trail requirements.
• Familiarity with responsible AI principles including bias and fairness evaluation, human-in-the-loop design, and explainability approaches in the financial services contexts.
• Familiarity with data pipeline design for AI workloads including ingestion, transformation, and quality validation.
• Familiarity with cloud-based platforms for building, training, and deploying scalable LLM solutions.
• Familiarity with AI-assisted software engineering tools for accelerating development, implementation, and code review practices.

Qualifications

Must have:

• Hands-on software engineering proficiency in Python, with the ability to write clean, modular, production-quality code for LLM pipelines and agentic applications.
• Familiarity with RESTful and event-driven API patterns including asynchronous workflows, service boundaries, and integration of enterprise data sources to expose LLM and agentic capabilities.
• Familiarity with containerization and orchestration concepts for packaging and deploying LLM applications in cloud environments.
• Understanding of software engineering best practices as applied to ML systems, including modular code design, testing patterns for AI pipelines, and data quality validation.

Skills

Skills include:

• Gen AI Foundational: Ability to understand business challenges and translate them into value-add AI solutions leveraging large language models and intelligent automation.
• Agentic and LLM Ops: Experience designing and building agentic systems including multi-agent orchestration patterns, tool use, and memory design across single and multi-step workflows.
• Software Engineering: Proficiency in prompt engineering techniques including zero-shot, few-shot, chain-of-thought, and structured output design, with the ability to systematically evaluate and iterate on prompt performance.

Benefits

EY offers a comprehensive compensation and benefits package where you'll be rewarded based on your performance and recognized for the value you bring to the business. The base salary range for this job in all geographic locations in the US is $106,900 to $176,500. The base salary range for New York City Metro Area, Washington State and California (excluding Sacramento) is $128,400 to $200,600. Individual salaries within those ranges are determined through a wide variety of factors including but not limited to education, experience, knowledge, skills and geography.

Pay

$106,900 to $176,500

Schedule

Hybrid model with most people in external, client serving roles expected to work together in person 40-60% of the time over the course of an engagement, project or year. Flexible vacation policy allows you to decide how much vacation time you need based on your own personal circumstances. You'll also be granted time off for designated EY Paid Holidays, Winter/Summer breaks, Personal/Family Care, and other leaves of absence when needed to support your physical, financial, and emotional well-being.