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

EY · Tampa, FL · Yesterday

On-siteEngineering$107k–$177k/yrFull-time

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. It involves contributing to the design, development, delivery, and maintenance of AI-enabled solutions or infrastructure while aligning to relevant engineering standards and project delivery expectations.

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

The ideal candidate should 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 (e.g. Docker, Kubernetes, Azure Container Apps, AWS ECS etc.).
Understanding of software engineering best practices as applied to ML systems, including modular code design, testing patterns for AI pipelines, and data quality validation.

Qualifications

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 (e.g. LangSmith, Arize, Datadog, Azure Monitor etc.).
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 (e.g. LoRA, QLoRA, PEFT, NeMo Framework etc.).
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 (e.g. Azure ML, AWS SageMaker, Google Vertex AI etc.).
Familiarity with AI-assisted software engineering tools for accelerating development, implementation, and code review practices (e.g. Claude Code, GitHub Copilot, Codex etc.).

Skills

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 (e.g. LangGraph, AutoGen, CrewAI, Semantic Kernel, NVIDIA NIM etc.).
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(e.g. DSPy, PromptFlow etc.).

Benefits

At EY, we offer 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

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.

Schedule

Our expectation is for most people in external, client serving roles to work together in person 40-60% of the time over the course of an engagement, project or year.