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

Location:

Charlotte, Dallas, New York, Tampa

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** Forward Deployed Engineer - Applied AI - Manager - Financial Services*
* EY is the only professional services firm with a separate business unit ("FSO") that is dedicated to the financial services marketplace. Our FSO teams have been at the forefront of every event that has reshaped and redefined the financial services industry.  If you have a passion for rallying together to solve the most complex challenges in the financial services industry, come join our dynamic FSO team!

The business problems our clients are facing today are not the same problems they have faced in the past. The rapid pace of development in Artificial Intelligence and the technology that enables it has created an urgent need to innovate and adapt to the new global business paradigm. Financial institutions are looking to build smarter and more efficient ways to operate their business, create new revenue streams, and better manage risk, through new opportunities uncovered by their data.

We believe that to fully unlock the potential of Artificial Intelligence, we need to look not only at its application, but also at the strategy level for how best to transform the enterprise into one that is technology and data focused and ready for the new age. Our clients' problems are becoming increasingly complex while at the same time the need to automate and streamline is rising.

** The Opportunity*
* Leads the delivery of solution or infrastructure development services for large or complex AI/ML initiatives, applying strong technical capability and hands-on engineering experience. Takes accountability for the design, development, delivery, and maintenance of AI-enabled solutions or infrastructure, while ensuring compliance with and contribution to relevant engineering standards. Understands business and user requirements and translates them into design specifications that are effective from both business and technical perspectives.

Owns the implementation and integration of AI/ML capabilities into broader enterprise solutions, with a focus on reliability, scalability, user impact, and successful project delivery.

** Your key 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 sound 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.

+ Lead work streams or project delivery responsibilities through planning, coordination, execution oversight, issue management, and stakeholder communication.

+ Drive engineering quality through strong coding standards, CI/CD practices, automated testing, observability, and documentation.

+ Partner with Development, Engineering, Product, Data, Architecture, and engagement 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 guidance.

+ Use modern AI-assisted software engineering tools such as Claude Code, Codex, or equivalent agentic coding platforms as part of delivery leadership and engineering execution.

** AI and Engineering

Skills:

*
* ** Gen AI Foundational:*
* + Ability to understand complex business challenges across banking, capital markets, insurance, and asset management and translate them into LLM-powered solutions that deliver measurable business value

+ Practical experience leading and managing multi-disciplinary teams through the full AI product lifecycle - requirements, architecture, build, evaluation, and production handoff

+ Demonstrated experience managing and mentoring teams of AI engineers and data scientists through the execution of specific business use cases, ensuring technical quality and delivery consistency across engagements

+ Advanced hands-on software engineering proficiency in Python, with the credibility to guide implementation decisions as well as architecture across delivery teams

+ Demonstrated experience architecting and delivering production-grade LLM applications including retrieval-augmented systems, agentic orchestration layers, and structured output pipelines at enterprise scale (e.g. Llama Index, Lang Chain,  Azure OpenAI, AWS Bedrock)

+ Strong knowledge of embedding models, vector search, semantic…