Forward Deployed Engineer - Applied AI - Senior Manager - Financial Services - Consulting

We need to output HTML fragment with sections: About the role? Actually we need to use headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We need to parse the posting: It includes location list, then repeated intro, then job title line, then description about EY FSO, then "The Opportunity:" which seems like responsibilities? Then "Your Key Responsibilities:" list bullet points. Then "AI and Engineering Skills:" list. Then "Software Engineering:" list. Then "Soft Skills and additional attributes for success:" list. Then "Bachelor’s degree preferred..." etc. Then "Ideally, you’ll also have..." then "Prior consulting experience". Then "What We Offer You At EY, we’ll develop you..." truncated. We need to extract substantive content for sections: About the role (maybe the intro paragraphs about EY and the opportunity). Responsibilities (Your Key Responsibilities). Requirements (maybe the bullet list under AI and Engineering Skills, Software Engineering, Soft Skills). Qualifications (education, years experience). Skills (maybe combine AI and Engineering Skills, Software Engineering, Soft Skills). Benefits (What We Offer You). Pay and Schedule not present, so omit. We must not repeat job title as heading. Also not include Job ID etc. We need to preserve all substantive info, not invent. Remove boilerplate like cookie/navigation/legal/EEO etc. There's none. We need to output only HTML fragment, using

for section headings,

• for lists,

  for prose. We need to decide sections: About the role (intro paragraphs). Responsibilities (Your Key Responsibilities). Requirements (maybe combine education/experience). Qualifications (maybe same as Requirements? Could separate). But we should avoid thin sections. Perhaps combine Requirements and Qualifications into one "Requirements" section covering education, experience, plus plus. Skills could be separate "Skills" section covering AI and Engineering Skills, Software Engineering, Soft Skills. Benefits section from "What We Offer You". Let's do:

  About the role

  with paragraphs describing EY FSO, opportunity.

  Responsibilities

  with ul of Your Key Responsibilities.

  Skills

  with ul combining AI and Engineering Skills, Software Engineering, Soft Skills (maybe separate subheadings? But we cannot add extra headings beyond allowed list? We could still have nested
  but headings only for sections. Could combine all bullet points into one big list; but that may lose categorization but still preserve content. However we need to preserve all substantive info; merging is okay as long as we don't lose detail. Could also create subheadings inside Skills using

  ? Not allowed per instruction: only use

  for section headings. It didn't forbid other headings, but says use

  for section headings (e.g. About the role, Responsibilities...). It doesn't forbid other headings, but safer to stick to

  only for sections. Could use

  for intro before list. We could have Skills section with three

  paragraphs each introducing subcategory then

  . That's okay.

  Requirements

  with education, years experience, plus plus list (the "Demonstrated experience in the following areas will be a huge plus:" list). Also maybe include "Ideally, you’ll also have Master’s degree..." and "Prior consulting experience". Those are also requirements/qualifications. We need to ensure we don't create sections with filler like "commensurate with experience". Not present. Now extract content. Let's copy relevant paragraphs. Intro: "Location: Charlotte, Dallas, New York, Tampa 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. 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. Forward Deployed Engineer – Applied AI - Senior Manager - Financial Services EY is the only professional services firm with a separate business unit (“FSO”) that is dedicated to the financial services marketplace. Our FSO teams have been at the forefront of every event that has reshaped and redefined the financial services industry. If you have a passion for rallying together to solve the most complex challenges in the financial services industry, come join our dynamic FSO team! The business problems our clients are facing today are not the same problems they have faced in the past. The rapid pace of development in Artificial Intelligence and the technology that enables it has created an urgent need to innovate and adapt to the new global business paradigm. Financial institutions are looking to build smarter and more efficient ways to operate their business, create new revenue streams, and better manage risk, through new opportunities uncovered by their data. We believe that to fully unlock the potential of Artificial Intelligence, we need to look not only at its application, but also at the strategy level for how best to transform the enterprise into one that is technology and data focused and ready for the new age. Our clients’ problems are becoming increasingly complex while at the same time the need to automate and streamline is rising. The Opportunity: Leads the definition and delivery of AI system design principles, reference architectures, and engineering standards for highly complex AI/ML initiatives across the organization. Brings deep technical authority, exceptional hands-on engineering experience, and the ability to shape long-term technology direction while also leading complex project and program outcomes. Takes accountability for the architecture, technical quality, integration approach, delivery execution, and operational excellence of AI-enabled solutions and platforms. Ensures business and user requirements are translated into scalable, secure, reusable, and high-impact technical designs that can be adopted across multiple teams and domains." That's about the role. Now Responsibilities list: after "Your Key Responsibilities:" we have bullet points each ending with a period. Let's capture them: - Define and govern system design principles, reference architectures, and engineering patterns for AI/ML, generative AI, RAG, and agentic systems. - Lead the most complex and escalated technical challenges across multiple teams, providing hands-on guidance in architecture, coding, troubleshooting, and design remediation. - Own end-to-end architecture for strategic AI initiatives, including service boundaries, orchestration models, data contracts, evaluation frameworks, and operational guardrails. - Drive consistency in engineering standards, design reviews, architecture governance, observability, resilience, security, and responsible AI practices. - Shape the enterprise integration model for AI/ML components within broader product, platform, infrastructure, and client delivery ecosystems. - Define and evolve API and integration strategies for AI platforms and applications, including contract design, versioning, security, idempotency, and reliability patterns at enterprise scale. - Ensure API layers and application integration patterns decouple clients from internal AI service topology, enabling safe evolution of models, workflows, and data stores without breaking consumers. - Lead large, complex project or program delivery outcomes by aligning architecture decisions, engineering execution, stakeholder governance, risks, dependencies, and delivery quality. - Influence platform strategy, technical roadmaps, and investment decisions through deep engineering judgment and practical delivery insight. - Partner with senior leaders across Engineering, Architecture, Product, Data, Security, Operations, and engagement leadership to align strategy with execution. - Establish scalable approaches for model evaluation, benchmarking, experimentation, rollout controls, and production quality measurement. - Mentor senior engineers and technical leads, raising the organization’s bar for system design, technical depth, delivery rigor, and architectural decision-making. - Use modern AI-assisted software engineering tools such as Claude Code, Codex, or equivalent agentic coding platforms to accelerate engineering design, implementation, and review practices. - Identify opportunities to reduce duplication, accelerate delivery, and create reusable AI platform capabilities across the enterprise. Now AI and Engineering Skills list after "AI and Engineering Skills:" includes many bullet points. Let's capture them: - Gen AI Foundational: Ability to translate complex enterprise business challenges into strategic AI architecture decisions, balancing immediate delivery needs with long-term platform scalability and firmwide adoption - Deep knowledge of foundation model landscape including open-source and commercial models, with the ability to evaluate, select, and advise on model suitability, capability trade-offs, and total cost of ownership across diverse enterprise use cases (e.g. GPT-4o, Claude, Llama, Gemini, Mistral etc.) - Demonstrated experience architecting and overseeing enterprise-scale knowledge AI systems spanning foundation model management, agentic design, AI application integration, and NLP and multimodal systems (e.g. Azure OpenAI, AWS Bedrock, Google Vertex AI, Hugging Face etc.) - Advanced hands-on engineering credibility in Python, with the ability to guide architecture and implementation decisions across senior engineering teams - Experience serving as a subject matter authority on knowledge AI systems and their application to both client-facing opportunities and internal horizontal platform development - Agentic and LLM Ops: Expertise in designing and governing enterprise agentic AI frameworks including multi-agent orchestration, tool use patterns, and memory architecture across complex, large-scale deployments (e.g. LangGraph, AutoGen, Semantic Kernel, NVIDIA NIM, CrewAI etc.) - Ability to define and enforce LLM Ops standards across the enterprise including model lifecycle governance, deployment pipelines, versioning strategies, and continuous improvement frameworks (e.g. MLflow, Azure ML, Kubeflow, GitHub Actions, LangChain, LlamaIndex etc.) - Experience integrating external vendor tooling for model monitoring, observability, safety, and compliance into enterprise AI platforms (e.g. LangSmith, Arize, Datadog, Azure Monitor etc.) - Ability to advise clients and internal stakeholders on multi-year AI architecture strategy, including platform investment decisions, build-vs-operate trade-offs, and sequencing of AI capability development across the enterprise - Ability to identify opportunities to reduce duplication, accelerate delivery, and create reusable AI platform capabilities and reference architectures that can be adopted across multiple teams and client engagements - Experience defining enterprise-wide evaluation and observability standards covering output quality, behavioral drift, safety, and auditability across agentic and LLM systems (e.g. RAGAS, DeepEval, Arize, Weights & Biases etc.) Now Software Engineering list after "Software Engineering:": - Ability to define and govern API strategy, containerization, and integration standards for enterprise AI platforms including contract design, versioning, security, idempotency, and reliability patterns — ensuring AI service consumers are decoupled from internal model and workflow topology (e.g. Docker, Kubernetes, REST, gRPC, Azure API Management, AWS API Gateway etc.) - Proven track record of building and delivering large-scale enterprise AI platforms, balancing hands-on technical contribution with cross-functional coordination and stakeholder alignment - Experience governing data security, privacy, and compliance practices as they apply to enterprise LLM and agentic system development and deployment (e.g. Azure Purview, AWS Macie, Microsoft Presidio etc.) - Strong ability to communicate complex AI architecture concepts to executive, technical, and non-technical audiences and translate strategic direction into actionable engineering roadmaps Now Soft Skills and additional attributes for success list after that heading: - Clear communicator able to explain complex AI system behavior and trade‑offs to technical and non‑technical stakeholders, including risk and compliance. - Strong ownership and accountability, taking responsibility for AI systems from design through production and issue resolution. - Comfort with ambiguity, able to operate effectively as requirements, regulations, and technologies evolve. - Collaborative and cross‑functional, working closely with engineering, product, risk, legal, and audit teams. - Sound judgment in regulated environments, with awareness of risk, controls, and when human oversight is required. - Bachelor’s degree preferred. - 10+ years of applied engineering experience, including extensive experience in senior AI/ML engineering, architecture, or complex technology delivery roles. - Demonstrated experience in the following areas will be a huge plus: (list) Let's capture that list: - Demonstrated experience advising clients on enterprise AI platform strategy including build-vs-operate trade-offs, vendor evaluation, and