Financial Services - Forward Deployed Engineer - Technology Consulting - Engineering- Manager

EY•Atlanta, GA

1d•$139,100 - $289,700

About The Position

EY is seeking a Forward Deployed Engineer (FDE) – Manager to help clients solve complex business challenges through technology. The position sits at the intersection of consulting, engineering, and product delivery, working directly with business and technology stakeholders to design, build, and deploy solutions that deliver measurable outcomes. The role calls for technical depth, business acumen, comfort with ambiguity, and the ability to lead delivery from concept through production while identifying opportunities to expand client value and grow the business. The successful candidate partners with clients to translate business priorities into scalable solutions across AI, cloud, data, digital, and platform engineering. They lead discovery, solution design, implementation, multidisciplinary teams, and end-to-end delivery, while tracking adoption and realized business value. They also build trusted client relationships and contribute to account growth through solution development, pursuit support, and follow-on initiatives. The role requires a strong foundation in software engineering, cloud platforms, data ecosystems, AI/GenAI, and enterprise integration. It includes guiding production AI and generative AI solutions, including LLM and retrieval-augmented applications, agentic workflows, evaluation, and operational readiness. The engineer must also guide teams in sound engineering practices, cloud deployment, integration, security, data readiness, and responsible use of AI-assisted engineering tools. The position is listed for locations in New York, Atlanta, Boston, Charlotte, Chicago, Grandview Heights, Hoboken, and Philadelphia. EY accepts applications on an ongoing basis. The stated salary range varies by office location: $166,900–$278,100 for New York City, Boston, Washington DC Metro, Washington State, and Southern California offices; $173,800–$289,700 for Bay Area California offices; and $139,100–$254,900 for other U.S. offices, including Sacramento. Individual salaries depend on factors including education, experience, knowledge, skills, and geography.

Requirements

Recommended experience profile: 8+ years in software engineering, architecture, technology consulting, or related roles, including 3+ years leading teams or complex delivery; equivalent combinations of experience may be considered.
Experience delivering complex technology solutions in client-facing environments, including direct work with business and technology stakeholders in ambiguous environments.
Ability to connect business objectives to architecture, engineering plans, delivery decisions, adoption, and measurable outcomes.
Experience leading multiple workstreams or multidisciplinary teams and coordinating dependencies across client and delivery organizations.
Strong understanding of modern software engineering, cloud platforms, data ecosystems, AI/GenAI, and enterprise integration.
Experience guiding the design and delivery of production AI/ML or generative AI solutions, including LLM applications, retrieval-augmented generation, agentic workflows, evaluation, and operational readiness.
Working knowledge of application frameworks such as Google ADK, LangChain, or LangGraph, and ability to select an approach suited to the use case and client environment.
Experience with APIs, data services, event-driven integration, and asynchronous workflows; familiarity with Apache Kafka or comparable messaging platforms.
Ability to guide engineering teams using Python, Java, TypeScript, or comparable languages, and practices such as source control, code review, automated testing, and CI/CD.
Experience supporting deployment and troubleshooting in cloud, on-premises, or hybrid client environments, including security, integration, and data-readiness considerations.
Strong communication, facilitation, negotiation, executive presentation, stakeholder management, and workshop facilitation skills.
Commercial awareness and mindset, including experience contributing to proposals, account planning, solution shaping, or identifying and developing follow-on work.
Ability to operate effectively in ambiguous, fast-paced environments.
Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
Ability to assess business challenges and shape AI solutions with clear value hypotheses, feasibility criteria, and appropriate human oversight.
Experience guiding production LLM applications from data ingestion through model output delivery; examples include Azure OpenAI, AWS Bedrock, or Google Vertex AI.
Understanding of retrieval-augmented systems, including knowledge-source integration, chunking, embeddings, retrieval, grounding, and quality trade-offs.
Understanding of prompt design, structured outputs, and systematic evaluation and iteration.
Ability to assess agentic patterns, tool use, orchestration, memory, and workflow boundaries, and guide reliable designs suited to the problem.
Experience guiding agentic systems and multi-step workflows, including orchestration, tool integration, state management, retries, fallbacks, and failure handling.
Ability to establish evaluation approaches covering functional correctness, output quality, safety, and business-defined measures.
Understanding of AI production support needs, including tracing, observability, degradation detection, behavioral drift, and incident response.
Strong working knowledge of software architecture and engineering practices for modular, testable, secure, and maintainable systems.
Ability to review designs involving APIs, service boundaries, event-driven patterns, data flows, and enterprise integrations.
Familiarity with containerization and cloud deployment patterns.
Experience guiding teams working with structured and unstructured data, including data quality, readiness, and validation for AI workloads.
Familiarity with AI-assisted engineering tools and controls for their responsible use.

Nice To Haves

Experience delivering AI solutions in financial services or another regulated industry.
Experience establishing model observability and evaluation practices.
Familiarity with fine-tuning approaches such as LoRA, QLoRA, or PEFT, and ability to assess when fine-tuning is appropriate.
Experience with AI security risks such as prompt injection, adversarial inputs, data leakage, and auditability.
Experience applying responsible AI practices, including fairness evaluation, explainability, human-in-the-loop controls, and governance.
Experience with data pipelines for AI workloads, including ingestion, transformation, and quality validation.
Experience with cloud AI platforms such as Azure ML, AWS SageMaker, or Google Vertex AI.
Experience using AI-assisted software engineering tools to improve development and review workflows.

Responsibilities

Partner with client stakeholders to understand business objectives, challenges, and opportunities.
Lead discovery, requirements definition, solution design, and implementation activities.
Design and deliver solutions across AI, cloud, data, digital, and platform engineering domains.
Translate business requirements into scalable architectures, delivery plans, and engineering workstreams.
Own solution delivery end-to-end, from problem definition through deployment and adoption.
Lead multidisciplinary teams across engineering, architecture, product, and business functions.
Measure and track business outcomes, adoption, and value realization.
Build trusted relationships with client stakeholders and delivery teams.
Identify adjacent business opportunities and shape follow-on initiatives that create additional client value.
Support account growth through solution development, pursuit support, and client engagement.
Guide design and delivery of production AI/ML and generative AI solutions, including LLM applications, retrieval-augmented generation, agentic workflows, evaluation, and operational readiness.
Guide engineering teams in software architecture and practices, API and enterprise integration design, cloud deployment, data quality and readiness, and production support for AI systems.
Establish or guide evaluation, observability, safety, and operational approaches for AI solutions.
Help assess model selection, inference performance, latency, throughput, and cost.
Apply responsible AI practices and guide controls for AI-assisted engineering tools where appropriate.