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

EY•Atlanta, GA

1d•$151,500 - $393,200

About The Position

EY is seeking a Forward Deployed Engineering Transformation Lead to help clients solve complex business challenges through technology. The role operates at the intersection of strategy, engineering, and consulting, working 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 business acumen to translate strategic priorities into executable roadmaps, lead multidisciplinary teams, and identify opportunities to expand client value and grow the business. The role leads business and technology transformation initiatives from strategy through execution and value realization. It partners with executive stakeholders to define priorities, roadmaps, and investment strategies, and shapes solution architecture across cloud, data, AI, digital, and platform engineering. The position aligns business objectives, operating models, and technology solutions; establishes governance and delivery frameworks; and builds trusted executive relationships as a strategic advisor. It also contributes to account growth and market development while mentoring managers, architects, and engineers. A successful Forward Deployed Engineer is comfortable in business stakeholder and solution design workshops as well as engineering reviews. They translate business challenges into scalable, sustainable solutions, deliver tangible outcomes, build trusted executive relationships, develop teams, and identify opportunities for continued client value and business growth. EY describes its culture as inclusive and focused on professional growth and personal fulfillment. The listed salary ranges vary by office location, and compensation is determined by factors including education, experience, knowledge, skills, and geography.

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, and 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.
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.
Understanding of retrieval-augmented systems, including 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, workflow boundaries, and when simpler deterministic approaches are appropriate.
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 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.
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.
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, including the controls needed for responsible use.
Accountability for client outcomes, program delivery, technical quality, and team effectiveness; ability to explain technology choices, risks, and trade-offs in business terms.
Ability to set direction in ambiguous, fast-moving environments and give teams clarity on priorities and decisions.
Ability to balance strategic leadership with practical engineering judgment and remain engaged in critical implementation decisions.
Ability to lead through influence across business, product, architecture, engineering, data, risk, legal, compliance, and audit stakeholders.
Sound judgment in regulated environments, including awareness of security, privacy, responsible AI, human oversight, and operational controls.
Commercial judgment and contribution to sustainable client relationships and account growth.
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.

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 serve as 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.
Guide the design and delivery of production AI/ML and generative AI solutions, including evaluation, security, reliability, operational readiness, and business value.
Guide architecture and engineering choices for AI applications, integrations, cloud deployments, and production operations.
Develop and coach technical leaders and teams delivering across multiple workstreams.
Contribute to opportunity identification, solution shaping, proposal development, or pursuit leadership.