EY
EY5 open roles

Technology Consulting - TMT - Forward Deployed Engineer - Sr Manager

$171–445kDallas, TXHybridSeniorFull-time

Posted 3 days ago

The role in short

Lead the establishment of the FDE capability for the Technology sector by embedding with clients to build and deploy production AI systems, define engineering standards, and drive reusable solution patterns.

Location: Dallas, Houston, San Antonio, Bay Area and other locations

At EY, we’re all in to shape your future with confidence.

We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world.

The opportunity

#TechnologyConsulting #TMT

At EY Consulting, we help Technology companies transform through the power of people, technology, and innovation. Our AI Engineering and Solutions team builds Technology Sector Transformative solutions. These are production AI systems that change how software, cloud, semiconductor, cybersecurity, and digital-platform companies operate and grow.

As a Senior Manager and Forward Deployed Engineer (FDE), you will help establish and lead our FDE capability. An FDE combines four disciplines in one role: software engineering, deep AI and platform fluency, sector expertise, and customer leadership. You will work inside client environments, build production AI systems hands-on, and directly influence sales, delivery success, and reusable sector IP.

This role is not additional delivery capacity. It is a different kind of talent. Each engagement should create value that compounds: a project becomes a solution pattern, the pattern becomes a sector playbook, the playbook becomes a platform primitive, and the platform primitive becomes a competitive advantage for EY and our clients.

Your key responsibilities

  • Establish the FDE capability for the Technology sector. Define the role model, hiring bar, engineering standards, and ways of working with sales and delivery teams.
  • Embed with senior client stakeholders. Shape ambiguous problems into clear AI use cases and translate technical decisions into business outcomes.
  • Lead solution shaping, proposals, pricing, and technical demonstrations. Build working prototypes that win and reduce the risk of engagements.
  • Own end-to-end architecture for strategic generative AI, RAG, and agentic programs, including service boundaries, orchestration, data contracts, evaluation frameworks, and operational guardrails.
  • Stay hands-on. Write code, review designs, and resolve the most complex and escalated technical problems across teams.
  • Define reference architectures and API and integration strategies that decouple clients from internal AI service topology, so models, workflows, and data stores can evolve safely.
  • Set standards for evaluation, benchmarking, observability, resilience, security, and responsible AI. Build safety controls into every production system.
  • Turn delivery work into reusable solution patterns, sector playbooks, and platform primitives that improve sales and delivery across the Technology sector portfolio.
  • Build long-term, high-trust relationships with client executives. Grow accounts by connecting EY capabilities across AI and Data, Digital Engineering, Technology Strategy, and Risk.
  • Hire, coach, and develop FDEs and engineers. Use AI-assisted engineering tools to raise team speed and quality.

Skills and attributes for success

To qualify for the role, you must have:

  • 15+ years of applied engineering experience, including significant experience in senior AI/ML engineering, architecture, or client-facing technology delivery roles.
  • LLM depth, not just API usage. Expert knowledge of model behavior, context design, advanced prompt engineering, fine-tuning trade-offs, and failure modes.
  • Applied AI engineering with a safety mindset. Proven delivery of production-grade generative AI, RAG, and agentic systems with guardrails, human oversight, and responsible AI controls.
  • Workflow-centric AI design. Ability to redesign business processes around agents and multi-step AI workflows, including tool integration, context management, and failure handling.
  • Platform thinking. A track record of building reusable components, reference architectures, and shared AI platform capabilities across teams or clients.
  • Strong systems and performance orientation across distributed systems, API design (REST, GraphQL, or gRPC), latency, cost, reliability, and scale.
  • Full-stack and infrastructure skills. Advanced hands-on Python or Java, cloud-native services on Azure, AWS, and/or GCP, Kubernetes, Docker, and CI/CD.
  • Data and Lakehouse skills. Experience with modern data platforms such as Databricks, Snowflake, or Microsoft Fabric, and with AI-ready data foundations.
  • Structured reasoning and evaluation. Experience building evaluation frameworks, benchmarking, A/B testing, and production quality measurement.
  • Customer-facing ambiguity handling. Ability to work inside unclear client environments and reach a working solution quickly.
  • Architect-level customer guidance for senior technical and business stakeholders.
  • Field engineering and enablement. Experience preparing client and EY teams to adopt, run, and extend the systems you build.
  • High-trust, long-term client relationships, with proven contribution to sales and account growth.
  • Ability to work in person at an EY office and/or client location up to 50% of the time, based on client and project needs.
  • Candidates must be legally authorized to work in the U.S. without the need for employer sponsorship, now and in the future

Ideally, you will have:

  • Experience serving software, cloud, semiconductor, cybersecurity, digital-platform, fintech, media technology, or AI-native organizations.
  • Prior experience as a Forward Deployed Engineer, solutions engineer, or field CTO at a product or AI-native company.
  • Experience with GPU-accelerated AI workloads and managed cloud AI services, for example NVIDIA GPU platforms or managed AI services on major clouds.
  • Experience taking generative AI, RAG, or agentic systems from prototype to production
  • Experience with orchestration frameworks such as Teporal, Restate, LangGraph, LangChain, Semantic Kernel, or similar.
  • Established market relationships within the Bay Area technology ecosystem.
  • Microsoft, Google Cloud, or AWS certifications.
  • Bachelor’s degree in computer science, engineering, or a related field.

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 – $205,900 to $427,800
    • Bay Area California offices – $214,600 to $445,700
    • All other offices locations in the US, including Sacramento – $171,600 to $392,100
  • 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 herefor 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

EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets.

Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow.

EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.

All in to shape the future with confidence.

EY provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, genetic information, national origin, protected veteran status, disability status, or any other legally protected basis, including arrest and conviction records, in accordance with applicable law.

EY is committed to providing reasonable accommodation to qualified individuals with disabilities including veterans with disabilities. If you have a disability and either need assistance applying online or need to request an accommodation during any part of the application process, please call 1-800-EY-HELP3, select Option 2 for candidate related inquiries, then select Option 1 for candidate queries and finally select Option 2 for candidates with an inquiry which will route you to EY’s Talent Shared Services Team (TSS) or email the TSS at ssc.customersupport@ey.com.

Prep for this interview

5 questions likely to come up at EY, each matched to a line in this posting.

  1. 1Cloud, deployment and security
    They wrote: “Full-stack and infrastructure skills. Advanced hands-on Python or Java, cloud-native services on Azure, AWS, and/or GCP, Kubernetes, Docker, and CI/CD.”

    How would you debug a deployment that works in staging but fails at the customer's site?

    How to answer

    Why they ask: Customer environments differ in ways you can't see. This tests method under pressure.

    1. Compare versions, config and environment variables
    2. Check network rules, proxies and certificates (common in enterprise)
    3. Look at resource limits and permissions
    4. Add the missing check to your install steps afterwards

    Tip: List the usual suspects and how you'd check each without full access.

  2. 2LLMs, RAG and agents
    They wrote: “LLM depth, not just API usage. Expert knowledge of model behavior, context design, advanced prompt engineering, fine-tuning trade-offs, and failure modes.”

    A customer wants a chatbot over their internal documents. How do you build it?

    How to answer

    Why they ask: RAG is the most common first project. They want the whole path, from messy files to answers people trust.

    1. Collect and clean the documents; handle permissions per user
    2. Split them into chunks, embed, and store with source links
    3. Retrieve, then answer with citations; say “I don't know” when retrieval is weak
    4. Build a test set of real questions with expected answers before launch

    Tip: Spend more time on the data and on testing than on the model.

  3. 3Integrations and APIs
    They wrote: “Define reference architectures and API and integration strategies that decouple clients from internal AI service topology, so models, workflows, and data stores can evolve safely.”

    Walk me through connecting our product to a customer's internal system that has no documentation.

    How to answer

    Why they ask: This happens weekly in the job. They want a calm, step-by-step method.

    1. Find who owns the system and get read access to a test environment
    2. Watch real traffic or logs to learn the data shapes
    3. Build a thin adapter with logging around every call
    4. Agree on how failures get reported, and who fixes what

    Tip: Talk about finding the owner of the system first. The code is the easy part.

  4. 4Discovery and scoping
    They wrote: “Embed with senior client stakeholders. Shape ambiguous problems into clear AI use cases and translate technical decisions into business outcomes.”

    A customer says “we want AI in our claims process.” What do you ask in the first meeting?

    How to answer

    Why they ask: Most FDE work starts this vague. They want to hear you find the real problem before you pick a tool.

    1. Map the current process: who touches a claim, in what order, with which systems
    2. Find the slowest or most error-prone step and ask what it costs them
    3. Agree on one measurable goal (for example time per claim, or error rate)
    4. Ask what data you can access, and who signs off on security

    Tip: Ask about the work, not the technology. Who does it today, how long it takes, what a mistake costs, and how they'd know it got better.

  5. 5Coding
    They wrote: “We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world.”

    Build a small in-memory cache. Then add expiry. Then a size limit.

    How to answer

    Why they ask: Staged exercises show how your code holds up when requirements change mid-interview, like at a customer.

    1. Start with get and set on a dictionary, plus tests
    2. Store an expiry time with each value and check it on read
    3. Track use order and evict the least recently used entry when full
    4. Say what you'd change for many threads or many machines

    Tip: Keep the first version simple and well tested so the next stage is a small change.

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