Location: New York, Atlanta, Boston, Charlotte, Chicago, Grandview Heights, Hoboken, and Philadelphia
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Role Summary
We are seeking a Forward Deployed Engineer (FDE) – Manager to help clients solve complex business challenges through technology. This role 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 ideal candidate combines technical depth with strong business acumen. They can navigate ambiguity, translate business priorities into scalable technology solutions, and lead delivery from concept through production while identifying opportunities to expand client value and grow the business.
Key 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.
Forward Deployed Engineering Requirements
- Demonstrated experience leading complex, client-facing technology delivery, 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 technical foundation in 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 the judgment to select an approach appropriate 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, and executive presentation skills.
- Commercial awareness, including experience contributing to proposals, account planning, solution shaping, or identifying follow-on work.
- Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
AI and Engineering Skills
GenAI and AI Solution Delivery
- Ability to assess business challenges and shape AI solutions with clear value hypotheses, feasibility criteria, and appropriate human oversight.
- Experience guiding the design and delivery of production LLM applications, including end-to-end flows from data ingestion through model output delivery (for example, 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 (for example, LangChain, LlamaIndex, Azure AI Search, pgvector, Pinecone, or Weaviate).
- Understanding of prompt design, structured outputs, and systematic evaluation and iteration (for example, DSPy or PromptFlow).
- Ability to assess agentic patterns, tool use, orchestration, memory, and workflow boundaries, and to guide teams toward reliable designs suited to the problem.
Agentic Systems, Quality, and Operations
- Experience guiding agentic systems and multi-step workflows, including orchestration, tool integration, state management, retries, fallbacks, and failure handling (for example, LangGraph, AutoGen, CrewAI, Semantic Kernel, or comparable frameworks).
- Ability to establish evaluation approaches that cover functional correctness, output quality, safety, and business-defined measures (for example, RAGAS, DeepEval, or Arize Phoenix).
- Understanding of production support needs for AI systems, including tracing, observability, degradation detection, behavioral drift, and incident response.
- Ability to guide decisions on model selection, inference performance, latency, throughput, and cost.
Software, Cloud, and Integration Engineering
- 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 (for example, Docker, Kubernetes, Azure Container Apps, or AWS ECS).
- Experience guiding teams that build with structured and unstructured data, including data quality, readiness, and validation for AI workloads.
- Familiarity with AI-assisted engineering tools such as Claude Code, Codex, GitHub Copilot, or equivalent, and the controls needed for their responsible use.
Demonstrated Experience in the Following Areas Will Be a Plus
- Experience delivering AI solutions in financial services or another regulated industry.
- Experience establishing model observability and evaluation practices (for example, LangSmith, Arize, Datadog, or Azure Monitor).
- Familiarity with fine-tuning approaches such as LoRA, QLoRA, or PEFT, and the 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.
Required Qualifications:
- 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.
- Strong understanding of modern engineering practices, cloud platforms, data ecosystems, AI/GenAI, and enterprise integration.
- Ability to connect technology execution with business objectives and measurable outcomes.
- Experience leading cross-functional teams and driving delivery across multiple workstreams.
- Strong stakeholder management, communication, and workshop facilitation skills.
- Ability to operate effectively in ambiguous, fast-paced environments.
- Commercial mindset with the ability to identify and develop growth opportunities within client accounts.
What Success Looks Like
A successful Forward Deployed Engineer is equally comfortable in a business stakeholder workshop, a solution design workshop, or an engineering review. They turn business challenges into scalable solutions, deliver tangible outcomes, and create opportunities for continued growth.
- Business challenges are translated into practical, scalable technology solutions.
- Solutions are delivered successfully and achieve intended business outcomes.
- Strong relationships are established with client stakeholders and delivery teams.
- New opportunities are identified and converted into expanded client value and engagement growth.
- Teams operate effectively across consulting, architecture, engineering, and delivery disciplines.
What we offer you At EY, we harness our collective strength to empower you to shape your future with confidence through professional growth, personal fulfillment and an inclusive culture. Learn more at ey.com/us/careers.
- The salary range for this job is:
- New York City, Boston, and Washington DC Metro Areas, Washington State, and Southern California offices – $166,900 to $278,100
- Bay Area California offices – $173,800 to $289,700
- All other offices locations in the US, including Sacramento – $139,100 to $254,900
- Individual salaries within these ranges are determined through a wide variety of factors including but not limited to education, experience, knowledge, skills and geography. In addition, our Total Rewards package includes medical and dental coverage, pension and 401(k) plans, and a wide range of paid time off options.
Are you ready to shape your future with confidence? Apply today.
- To make the most of your application experience, please limit yourself to two applications within a six-month period.
- EY accepts applications for this position on an on-going basis.
- For those living in California, please click here for additional information.
- At EY, our values set the foundation for how we work and the behaviors we expect of our people. Any misrepresentation or falsification of information or lack of integrity at any point in the recruiting process may result in withdrawal of your candidacy, revocation of an offer or immediate termination of employment.
EY | Building a better working world
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All in to shape the future with confidence.
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