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

EY•Charlotte, NC

33d•$125,500 - $261,600•Hybrid

About The Position

EY is hiring a Forward Deployed Engineer – Applied AI at the Manager level to lead delivery of AI/ML solution and infrastructure development for complex financial services initiatives. The role is part of EY’s Financial Services Organization, serving banking, capital markets, insurance, and asset management clients. It focuses on helping financial institutions use AI and data to improve operations, create revenue opportunities, and manage risk. The Manager is accountable for the design, development, delivery, integration, and maintenance of production-grade AI-enabled solutions. The role translates business and user needs into technical designs and implementation patterns, and leads workstreams while coordinating delivery, addressing issues, and communicating with stakeholders. The engineer also guides engineering quality, system reliability, scalability, security, user impact, and cost efficiency. The position involves hands-on software engineering and technical leadership across generative AI, LLM applications, retrieval-augmented generation, agentic systems, intelligent automation, and enterprise integrations. The role partners with engineering, product, data, architecture, and engagement teams, and uses AI-assisted software engineering tools as part of delivery and engineering execution. EY describes its work model as hybrid, with client-serving employees generally expected to work together in person 40–60% of the time over the course of an engagement, project, or year. The role may be based in Charlotte, Dallas, New York, or Tampa.

Requirements

7 or more years of applied engineering experience, including significant experience in AI/ML engineering roles.
Advanced hands-on software engineering proficiency in Python.
Experience leading and managing multidisciplinary teams through the full AI product lifecycle.
Experience managing and mentoring teams of AI engineers and data scientists.
Demonstrated experience architecting and delivering production-grade LLM applications, including RAG, agentic orchestration, and structured output pipelines at enterprise scale.
Strong knowledge of embedding models, vector search, semantic retrieval, and NLP similarity systems used in enterprise RAG and knowledge AI architectures.
Deep expertise in LLM Ops, including model lifecycle management, versioning, CI/CD for AI systems, deployment governance, and production improvement loops.
Expertise in agentic system architecture, including multi-agent orchestration, tool use, memory design, and human-in-the-loop workflows.
Experience governing production agent behavior, including audit trails, cost and latency controls, and reliability management.
Experience defining and governing LLM evaluation frameworks for output quality, safety, and alignment with business requirements.
Knowledge of MLOps practices for AI systems in cloud environments, including containerization and orchestration for scalable, secure LLM deployment.
Experience governing API design standards, including contract design, versioning, error handling, retry semantics, and decoupling consumers from internal model and workflow topology.
Strong system design capability across service boundaries, asynchronous workflows, data contracts, cloud-native patterns, and secure deployment models.
Proficiency in containerization and orchestration for production LLM applications.
Ability to collaborate with data engineers, ML engineers, and business stakeholders to align solutions with enterprise data and technology constraints.
Clear communication skills, including the ability to explain AI system behavior and trade-offs to technical and non-technical stakeholders, including risk and compliance.
Strong ownership and accountability for AI systems from design through production and issue resolution.
Ability to work effectively amid ambiguity as requirements, regulations, and technologies evolve.
Collaborative approach working with engineering, product, risk, legal, and audit teams.
Sound judgment in regulated environments, with awareness of risk, controls, and when human oversight is required.

Nice To Haves

Bachelor’s degree preferred.
Experience advising clients on AI platform and infrastructure strategy, model access layer selection, build-versus-buy decisions, and integration with existing data and technology infrastructure.
Ability to quantify business improvement from LLM solutions using evaluation metrics, performance benchmarks, and client-facing reporting.
Ability to design and govern model observability and monitoring strategies covering output quality, behavioral drift, and agentic workflow tracing.
Understanding of LLM fine-tuning methods and advising clients on their application, including data preparation, training, and post-training evaluation.
Experience leading controlled model rollouts, including shadow deployment, A/B testing, canary releases, stakeholder sign-off, and rollback criteria.
Familiarity with LLM-specific AI security risks, including prompt injection, data poisoning, and model extraction, and mitigation and audit trail requirements.
Familiarity with bias, fairness, and explainability approaches for financial services AI systems.
Familiarity with scalability, fault tolerance, and distributed architecture principles for production AI workloads.
Familiarity with enterprise AI data pipeline architecture, including ingestion, transformation, and governance.
Understanding of data security and privacy practices in cloud environments for LLM application development and deployment.
Familiarity with AI-assisted software engineering tools such as Claude Code, GitHub Copilot, or Codex.
Familiarity with GPU-accelerated AI workloads and cloud AI services for model inference and deployment at scale.
Familiarity with agile and modern engineering delivery methodologies for AI/ML initiatives.
Master’s degree in Business Administration (MBA) or Science (MS) preferred.
Prior consulting experience.

Responsibilities

Design, develop, test, deploy, and support production-grade AI/ML, generative AI, and intelligent automation solutions.
Solve 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 covering service boundaries, orchestration layers, data flows, security controls, and external integrations.
Lead workstreams or project delivery through planning, coordination, execution oversight, issue management, and stakeholder communication.
Drive engineering quality through coding standards, CI/CD practices, automated testing, observability, and documentation.
Partner with Development, Engineering, Product, Data, Architecture, and engagement leadership teams to deliver AI capabilities.
Improve the performance, resilience, maintainability, and cost efficiency of deployed AI systems.
Participate in architecture and design reviews, providing trade-off analysis and implementation guidance.
Use modern AI-assisted software engineering tools such as Claude Code, Codex, or equivalent agentic coding platforms in delivery leadership and engineering execution.
Lead and manage multidisciplinary teams through the AI product lifecycle, from requirements and architecture through build, evaluation, and production handoff.
Manage and mentor AI engineers and data scientists executing business use cases, maintaining technical quality and delivery consistency.
Architect and deliver production-grade LLM applications, including retrieval-augmented systems, agentic orchestration layers, and structured output pipelines.
Apply LLM Ops practices, govern agent behavior, define evaluation frameworks, and improve deployed LLM and agentic systems.
Collaborate with data engineers, ML engineers, and business stakeholders to align solution design with enterprise data and technology constraints.

Benefits

Medical coverage
Dental coverage
Pension plans
401(k) plans
Paid time off options
Flexible vacation policy
EY Paid Holidays
Winter and Summer breaks
Personal and Family Care time off
Other leaves of absence to support physical, financial, and emotional well-being