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

Role Summary

We are seeking a Forward Deployed Engineering (FDE) Transformation Lead to help clients solve complex business challenges through technology. This leader will operate at the intersection of strategy, engineering, and consulting, working directly with business and technology executives to shape transformation agendas, build scalable solutions, and deliver measurable business outcomes.

The ideal candidate combines deep technology expertise with strong business acumen. They can translate strategic priorities into executable roadmaps, lead multidisciplinary teams through delivery, and identify opportunities to expand client value and grow the business.

Key Responsibilities

• Lead business and technology transformation initiatives from strategy through execution and value realization.
• Partner with executive stakeholders to define business priorities, transformation roadmaps, and investment strategies.
• Shape and oversee solution architecture across cloud, data, AI, digital, and platform engineering domains.
• Align business objectives, operating models, and technology solutions to drive measurable outcomes.
• Lead cross-functional teams spanning product, engineering, architecture, and business stakeholders.
• Establish governance, delivery frameworks, success metrics, and outcome tracking.
• Build trusted relationships with senior client executives and become a strategic advisor on technology-led transformation.
• Identify new opportunities, shape follow-on engagements, and contribute to account growth and market development.
• Mentor managers, architects, and engineers while building high-performing teams.

Forward Deployed Engineering Requirements

• Proven experience leading large, client-facing technology programs and working directly with executive, business, and technology stakeholders in ambiguous environments.
• Demonstrated ability to connect business strategy to architecture, engineering execution, operating-model change, user adoption, and measurable outcomes.
• Experience leading multiple workstreams and multidisciplinary teams, managing dependencies, and coordinating delivery across client and consulting organizations.
• Strong technical foundation in enterprise architecture, 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, with attention to evaluation, security, reliability, operational readiness, and business value.
• Working knowledge of AI application frameworks such as Google ADK, LangChain, or LangGraph, with the judgment to guide framework and architecture choices for a use case and client environment.
• Experience overseeing API, data-service, event-driven, and asynchronous integration patterns; familiarity with Apache Kafka or comparable messaging platforms.
• Ability to review engineering implementation in Python, Java, TypeScript, or comparable languages, including source control, code review, automated testing, and CI/CD practices.
• Experience guiding deployments and issue resolution in client cloud, on-premises, or hybrid environments, including security, integration, data readiness, and operational constraints.
• Strong executive communication, workshop facilitation, influencing, negotiation, and client relationship skills.
• Track record of contributing to account growth through opportunity identification, solution shaping, proposal development, or pursuit leadership.
• Experience developing managers and technical leaders and building teams that can deliver across multiple workstreams.
• Bachelor’s degree in Computer Science, Engineering, Business, or a related field, or equivalent practical experience.
• Recommended experience profile: 12+ years in technology, engineering, architecture, or consulting roles, including 5+ years leading teams, programs, or client delivery. Equivalent combinations of experience may be considered.

AI and Engineering Skills

GenAI and AI Transformation

• Ability to identify where AI and intelligent automation can address business needs; define value hypotheses, feasibility criteria, and appropriate human oversight.
• Experience shaping production LLM solutions and their end-to-end lifecycle, from data and knowledge sources through model output delivery (for example, Azure OpenAI, AWS Bedrock, or Google Vertex AI).
• Understanding of retrieval-augmented systems, including 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, workflow boundaries, and the conditions where simpler deterministic approaches are appropriate.

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 covering functional correctness, output quality, safety, and business-defined measures (for example, RAGAS, DeepEval, or Arize Phoenix).
• Understanding of production support for AI systems, including tracing, observability, degradation detection, behavioral drift, incident response, and human escalation.
• Ability to guide model and platform choices based on latency, throughput, scalability, security, and cost.

Software, Cloud, and Integration Engineering

• Strong working knowledge of software architecture and engineering practices for secure, modular, testable, 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 prepare and use structured and unstructured data for AI workloads, including data quality, readiness, and validation.
• Familiarity with AI-assisted engineering tools such as Claude Code, Codex, or GitHub Copilot, including the controls needed for responsible use.

Leadership and Additional Attributes for Success

• Takes accountability for client outcomes, program delivery, technical quality, and team effectiveness.
• Builds executive trust and explains technology choices, risks, and trade-offs in business terms.
• Sets direction in ambiguous, fast-moving environments and gives teams clarity on priorities and decisions.
• Balances strategic leadership with practical engineering judgment and remains engaged in critical implementation decisions.
• Leads through influence across business, product, architecture, engineering, data, risk, legal, compliance, and audit stakeholders.
• Coaches and develops leaders, creates opportunities for delegation, and builds teams that can scale delivery.
• Applies sound judgment in regulated environments, including awareness of security, privacy, responsible AI, human oversight, and operational controls.
• Demonstrates commercial judgment and contributes to sustainable client relationships and account growth.

Required Qualifications:

• Proven experience leading large-scale business and technology transformation programs.
• Strong understanding of enterprise architecture, cloud platforms, data ecosystems, AI/GenAI, and modern engineering practices.
• Demonstrated ability to deliver measurable business outcomes through technology investments.
• Experience leading complex programs involving multiple stakeholders, teams, and workstreams.
• Strong executive communication, stakeholder management, and influencing skills.
• Track record of developing trusted client relationships and expanding account opportunities.
• Commercial mindset with experience identifying, shaping, and growing new business opportunities.

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.

• Clients achieve measurable business outcomes through technology-enabled transformation.
• Complex business challenges are translated into scalable, sustainable solutions.
• Trusted executive relationships are established across client organizations.
• New opportunities are identified and converted into expanded client value and business growth.
• Teams are developed, coached, and positioned to operate with increasing scale and impact.

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 – $181,800 to $377,400
  • Bay Area California offices – $189,400 to $393,200
  • All other offices locations in the US, including Sacramento – $151,500 to $346,000
• 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.
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