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

We need to output HTML fragment with sections for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections if posting has real specific content. We must not repeat job title as heading. Must preserve all substantive info, remove boilerplate, duplicate lines, application instructions, cookie/navigation/legal/EEO etc. We need to parse the posting. Let's extract content: Location: Charlotte, Dallas, New York, Tampa (maybe part of metadata; we can fold into short p). Then repeated intro: "At EY, we’re all in to shape your future with confidence..." repeated twice. We'll keep one paragraph summarizing? Must preserve all substantive info; we can combine but not lose info. The repeated text is duplicate; we should remove duplicated lines. So keep one instance. Then job title line: "Forward Deployed Engineer – Applied AI - Senior Manager - Financial Services". Not a heading. Then description about EY FSO etc. We'll keep as prose under About the role maybe. Then "The Opportunity:" then bullet-like sentences. Those are responsibilities. Then "Your Key Responsibilities:" then a list of items each ending with period. Those are responsibilities. Then "AI and Engineering Skills:" then list of skill items. Then "Software Engineering:" then list. Then "Soft Skills and additional attributes for success:" then list. Then "Bachelor’s degree preferred.10+ years of applied engineering experience, ..." That's qualifications. Then list of "Demonstrated experience in the following areas will be a huge plus:" etc. Those are additional qualifications/skills. Then "Ideally, you’ll also have Master’s degree in Business Administration (MBA) or Science (MS) preferredPrior consulting experience". That's qualifications. Then "What We Offer You At EY, we’ll develop you with future-focused skills and equip you with world-class experiences. We’ll empower you in a flexible environment, and fuel you and your extra" truncated; seems incomplete. Probably benefits but truncated. We cannot include incomplete placeholder. Since it's incomplete and ends with "and fuel you and your extra". That's not substantive; we should not create Benefits section unless posting actually states them. The text is cut off; we cannot assume. So we should not include Benefits, Pay, Schedule sections because not provided. Thus sections: About the role (intro + EY FSO description), Responsibilities (combined from The Opportunity and Your Key Responsibilities), Requirements (maybe qualifications: Bachelor's degree preferred, 10+ years experience, etc.), Skills (AI and Engineering Skills, Software Engineering, Soft Skills), Qualifications (maybe combine with Requirements? Actually we have both Requirements and Qualifications sections; we need to decide which to use. The instruction: use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have both Requirements and Qualifications if content distinct. Requirements could be the mandatory qualifications (Bachelor's degree preferred, 10+ years experience). Qualifications could be the "huge plus" items and Master's degree preferred, consulting experience. Skills could be the skill categories. We must not invent placeholder text. Ensure each heading has substantive content. Let's extract each piece. About the role: combine the intro paragraphs (unique) and the description about EY FSO and the opportunity? Actually "The Opportunity:" is more like responsibilities. So About the role: the introductory marketing copy about EY and the FSO team description. Let's craft:

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.

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.

That seems good. Responsibilities: combine The Opportunity paragraph and the list under Your Key Responsibilities. The Opportunity paragraph: "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." Then list items (each sentence). We'll put them as

• . Let's extract list items: - 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. We'll include those. Requirements: mandatory qualifications: Bachelor’s degree preferred. 10+ years of applied engineering experience, including extensive experience in senior AI/ML engineering, architecture, or complex technology delivery roles. We'll put as

  Bachelor’s degree preferred.

  10+ years of applied engineering experience, including extensive experience in senior AI/ML engineering, architecture, or complex technology delivery roles.

  Qualifications: the "huge plus" items and Master's degree preferred, prior consulting experience. Let's extract the huge plus list: - Demonstrated experience advising clients on enterprise AI platform strategy including build-vs-operate trade-offs, vendor evaluation, and integration of foundational model and agentic tooling into existing technology ecosystems (e.g. Azure OpenAI, AWS Bedrock, Google Vertex AI, NVIDIAAI Enterprise, Hugging Face etc.) - Demonstrated ability to translate governance and compliance requirements into scalable technical architectures that enable responsible AI adoption at scale - Ability to maintain current knowledge of emerging AI techniques, model architectures, and agentic patterns and assess their readiness and applicability for enterprise adoption - Ability to collaborate across business, technology, and product domains to align AI initiatives with enterprise architecture standards and strategic objectives - Familiarity with safety and alignment techniques for large language models including output filtering, guardrail design, and responsible AI governance frameworks (e.g. NeMo Guardrails, Guardrails AI, Azure Content Safety, LLM Guard etc.) - Familiarity with multimodal and vision-language model architectures and their applicability to enterprise knowledge AI use cases (e.g. GPT-4o, Gemini, LLaVA etc.) - Familiarity with small language model design patterns and their role in cost-effective, latency-sensitive enterprise deployments (e.g. Phi, Mistral, Llama 3, Gemma etc.) - Familiarity with AI regulatory and risk management frameworks relevant to financial services including model risk governance and explainability obligations (e.g. SR 11-7, FINRA, REG BI) - Familiarity with enterprise data architecture patterns that underpin large-scale AI systems including data mesh, datalake house, and real-time streaming pipelines (e.g. Databricks, Azure Synapse, Apache Kafka, Delta Lake etc.) - Familiarity with AI-assisted software engineering tools for accelerating engineering design, implementation, and review practices at enterprise scale (e.g. Claude Code, GitHub Copilot, Codex etc.) - Experience with GPU-accelerated AI workloads and cloud AI services, and the ability to advise on infrastructure strategy for model training and inference at scale (e.g. NVIDIA GPU platforms, NVIDIA AI Enterprise, Azure ML, AWS SageMaker etc.) - Experience establishing enterprise engineering standards, architecture governance practices, and AI platform modernization initiatives Then also: "Ideally, you’ll also have Master’s degree in Business Administration (MBA) or Science (MS) preferredPrior consulting experience". We'll separate: Master’s degree preferred. Prior consulting experience. We'll put as

  Master’s degree in Business Administration (MBA) or Science (MS) preferred.

  Prior consulting experience preferred.

  Skills: we have AI and Engineering Skills, Software Engineering, Soft Skills and additional attributes for success. We could combine into one Skills section with subheadings? But only allowed headings are the listed ones; we cannot add custom headings. So we need to put all skill content under Skills heading, maybe as paragraphs or lists. We'll extract each bullet. AI and Engineering Skills list: - 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