AI Architect Lead Consultant

We need to output HTML fragment with sections: h3 headings for sections present. Must not repeat job title. Must not include Job ID etc. Must remove boilerplate. Must preserve substantive info. Must include sections only when posting has real specific content. We need to parse the posting: It includes: - Role Summary (maybe heading "Role Summary") - Key Responsibilities - Basic Qualifications - Preferred Qualifications - Core Capabilities - About NTT DATA (maybe not needed? It's company description; could be considered a section but maybe not needed? The instruction: use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). They gave examples but we can use other headings if appropriate. The posting includes "Role Summary", "Key Responsibilities", "Basic Qualifications", "Preferred Qualifications", "Core Capabilities", "About NTT DATA". Also maybe "Engagement models" etc but those are within responsibilities. We should not include sections like Benefits, Pay, Schedule because not mentioned. We must not include filler like "commensurate with experience". Not present. We must output only HTML fragment, no /, no markdown fences. We need to use

for section headings. Use

• for any list of duties/requirements/benefits, and

  for prose. We need to decide what content is list vs prose. The responsibilities are a list of bullet-like sentences each starting with a verb. Could be turned into

  • . Similarly qualifications could be list. We must preserve ALL substantive information; do not invent or summarize away detail. So we need to include all details from the posting, but we can format them. We need to remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. At the end there is EEO statement, etc. Must remove that. Also the part about NTT DATA recruiters will never ask for payment etc. That's boilerplate/scams warning; remove. Also the accessibility statement etc. Remove. Also the "About NTT DATA" paragraph is substantive? It's company description; maybe we keep it as a section? The instruction says we can include sections only when posting has real, specific content for it. The "About NTT DATA" is specific content about the company. Could be included as a section maybe "About NTT DATA". However the examples of headings didn't include that but we can still use h3 for any section heading present. The posting includes a heading "About NTT DATA". So we should include that as a section. We must not repeat the job title as a heading. The job title is "AI Architect Lead Consultant". Not used. We must not put Job ID/requisition numbers as headings; we can drop them. We need to output only HTML fragment. Let's extract content: - Role Summary: paragraph(s) after "Role Summary". It includes: "As an AI Architect at Lead Consultant level, you will lead AI projects from conception through deployment for clients across industries, translating business challenges into working AI solutions. You will design and build the major components of those solutions, and act as the technical lead for the workstreams you own within a client engagement. The role spans the AI lifecycle — from discovery and solution design through proof of concept, production implementation, and operational improvement. The scope includes machine learning, predictive analytics, natural language processing, computer vision, intelligent automation, and Generative AI. This is a hands-on architecture role. Expect roughly 70% of your time hands-on — building reference implementations, prototypes, production services, and integration work — and roughly 30% on design, technical leadership, and mentoring. Engagement models vary, and some client contexts require sustained hands-on delivery alongside the engineering team." We can keep as

    maybe multiple paragraphs but we can combine into one

    . - Key Responsibilities: list of bullet points each starting with a verb. We'll turn each into

  • . List: 1. Lead assigned AI projects from conception through deployment, owning design and delivery for the workstreams you are accountable for. 2. Translate business challenges into AI solution designs, and evaluate the business impact and expected return of the approaches you propose. 3. Design and implement major components of enterprise AI solutions across applications, integration, data, AI/ML, and infrastructure. 4. Design and build AI solution patterns across ML and GenAI — LLM applications, retrieval-augmented generation, agents, orchestration, embeddings, vector and graph-based retrieval, model integrations, and evaluation. 5. Conduct data preprocessing, feature engineering, and dataset preparation for AI workloads. 6. Build prototypes, proofs of concept, reference implementations, and production-ready AI services. 7. Optimize AI models and inference workloads for performance, scalability, and cost, including on distributed computing frameworks and AI-optimized hardware. 8. Design data storage and retrieval approaches for AI workloads across databases, data lakes, and vector or graph stores. 9. Apply secure-by-design practices — authentication, RBAC, secrets management, encryption, private networking, data protection, auditability, and authorization-aware data retrieval. 10. Implement responsible-AI controls, including content safety, PII protection, bias identification and mitigation, prompt-injection defenses, and human review. 11. Work within client data governance requirements and applicable data privacy regulation in everything you design and deliver. 12. Apply MLOps, LLMOps, and GenAIOps practices — CI/CD, version control, model and prompt versioning, infrastructure as code, testing, evaluation, monitoring, tracing, and lifecycle management. 13. Communicate AI concepts, design decisions, and trade-offs clearly to client technical, product, and business teams, and collaborate across delivery, data, infrastructure, and security functions to integrate AI solutions. 14. Mentor engineers and junior team members in AI engineering and architecture practices. 15. Contribute to reusable patterns, accelerators, and delivery playbooks; stay current with emerging AI technologies; and provide technical input to discovery workshops, solution shaping, and effort estimates. - Basic Qualifications: list of items each with a bold heading? Actually they are sentences with bold? The posting shows: "Bachelor’s degree in computer science, engineering, data science, information systems, or a related technical discipline. 8+ years in software engineering, cloud, data engineering, AI/ML, architecture, or enterprise technology delivery. 4+ years designing and delivering production AI or ML solutions. 1+ years delivering production Generative AI or LLM-based systems, including LLM APIs, RAG, embeddings, vector and graph-based retrieval, evaluation, guardrails, and agentic workflows. 5+ years in software engineering with professional-level proficiency in at least one mainstream language — Python, Java, C#, C++, TypeScript/JavaScript, Go, or equivalent — including designing and integrating APIs and backend services using REST, gRPC, event-driven, or asynchronous patterns. 3+ years delivering technical design and implementation of significant components in complex enterprise environments. 2+ years architecting AI solutions across more than one deployment model (public cloud plus private cloud, hybrid, or on-premises) and establishing operational practice for AI systems — model and prompt versioning, artifact and experiment tracking, automated evaluation, monitoring and tracing, incident response, rollback — using CI/CD, infrastructure as code, and DevOps tooling." We can turn each line into
  • . - Preferred Qualifications: list similarly: "Master’s degree in AI/ML, computer science, engineering, data science, or a related field. 2+ years in consulting or professional services delivering technical solutions to external clients, including discovery workshops, solution shaping, effort estimation, and proposal input. 2+ years architecting secure enterprise systems — identity and authentication, authorization and RBAC, secrets management, encryption, network isolation, audit logging — and implementing responsible-AI controls in production, including content safety, PII protection, prompt-injection defenses, human review, and model risk management. Experience spanning Generative AI and one or more additional AI domains, such as classical ML, predictive analytics, NLP, computer vision, or intelligent automation. Experience across two or more major cloud AI platforms, including their managed model, retrieval, and orchestration services. Experience with container orchestration and GPU or accelerator infrastructure. Experience with model-serving and inference-optimization stacks, open-source or vendor, for self-hosted and private deployments. Experience with modern deep learning and classical ML frameworks, and the wider open-source model ecosystem. Experience with knowledge graphs, ontologies, semantic modelling, and graph databases, including graph-based or hybrid retrieval approaches for AI systems. Experience with model fine-tuning, quantization, evaluation, model routing, inference optimization, or deployment of open-source and proprietary models. Experience in AI governance, data privacy, security architecture, responsible AI, compliance, model risk, observability, and production AI operations. Relevant architecture, cloud, or AI certifications are welcome but not required." We'll list each as
  • . - Core Capabilities: bullet list after colon? Actually it's: "Core Capabilities Beyond the qualifications above, success in this role depends on: Technical communication. Explaining design decisions and trade-offs clearly to delivery teams and client technical staff. Influence. Building consensus within a delivery team and bringing others with you on technical decisions. Developing others. Mentoring engineers and junior team members, and raising the standard of the work around you. Judgment under ambiguity. Recognising which decisions need to be escalated and framing them well when they are." We can treat each as
  • maybe with the bold part as part of text. - About NTT DATA: paragraph(s) after that heading. It includes multiple sentences. We'll keep as

    maybe multiple

    but we can combine. We must remove the EEO boilerplate etc. Also remove the part about NTT DATA recruiters will never ask for payment etc. Also remove the accessibility statement and contact us links. Also remove the EEO policy statement links etc. Thus final HTML:

    Role Summary

    As an AI Architect at Lead Consultant level, you will lead AI projects from conception through deployment for clients across industries, translating business challenges into working AI solutions. You will design and build the major components of those solutions, and act as the technical lead for the workstreams you own within a client engagement. The role spans the AI lifecycle — from discovery and solution design through proof of concept, production implementation, and operational improvement. The scope includes machine learning, predictive analytics, natural language processing, computer vision, intelligent automation, and Generative AI. This is a hands-on architecture role. Expect roughly 70% of your time hands-on — building reference implementations, prototypes, production services, and integration work — and roughly 30% on design, technical leadership, and mentoring. Engagement models vary, and some client contexts require sustained hands-on delivery alongside the engineering team.

    Key Responsibilities

    • Lead assigned AI projects from conception through deployment, owning design and delivery for the workstreams you are accountable for.
    • Translate business challenges into AI solution designs, and evaluate the business impact and expected return of the approaches you propose.
    • Design and implement major components of enterprise AI solutions across applications, integration, data, AI/ML, and infrastructure.
    • Design and build AI solution patterns across ML and GenAI — LLM applications, retrieval-augmented generation, agents, orchestration, embeddings, vector and graph-based retrieval, model integrations, and evaluation.
    • Conduct data preprocessing, feature engineering, and dataset preparation for AI workloads.
    • Build prototypes, proofs of concept, reference implementations, and production-ready AI services.
    • Optimize AI models and inference workloads for performance, scalability, and cost, including on distributed computing frameworks and AI-optimized hardware.
    • Design data storage and retrieval approaches for AI workloads across databases, data lakes, and vector or graph stores.
    • Apply secure-by-design practices — authentication, RBAC, secrets management, encryption, private networking, data protection, auditability, and authorization-aware data retrieval.