AI/ML Architect - Principal (US - EAST)

We need to output only HTML fragment, no html/body tags, no markdown fences. Use

for section headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Skills, Benefits, Pay, Schedule. We must include sections only if posting has real specific content. The posting includes: Who You’ll Work With (maybe "About the role"? Actually "Who You’ll Work With" could be considered "About the role"? The instruction says use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we need to map content accordingly. We have sections. We have content: "Who You’ll Work With" describing Slalom and role context. Could map to "About the role". Then "What You’ll Do" -> Responsibilities. Then "What You’ll Bring" -> Requirements/Qualifications/Skills. Could split into Requirements (experience years), Qualifications (education? none), Skills (technical). But we can combine maybe. The instruction: Prefer a few substantive sections over many thin ones. So we could have sections: About the role, Responsibilities, Requirements, Skills, Benefits, Pay. No Schedule because not mentioned. No Qualifications separate if not present. We must not repeat job title as heading. Also not include Job ID etc. Remove boilerplate: EEO, accommodations, etc. Also remove "Please note: This role is not eligible for 100% remote work..." That's relevant? It's about remote work eligibility; maybe could be part of About the role? It's a note about location requirement. Should we keep? It's substantive info about remote work eligibility. It's not boilerplate. Should we include? Probably yes, as part of About the role or maybe a separate note. But we can include in About the role paragraph. We must preserve all substantive information; do not invent or summarize away detail. So we need to include all details from sections: Who You’ll Work With paragraph, What You’ll Do bullet-like sentences (but they are sentences separated by periods). We need to format as

• for any list of duties/requirements/benefits. The posting does not have explicit bullet list but we can treat each sentence as a list item? The instruction: Use
  • for any list of duties/requirements/benefits. If the content is prose, we can keep as

    . But we could convert each duty into list items for clarity. However we must not invent or summarize away detail; converting sentences to list items is okay as it's still preserving detail. Probably better to use

    • for duties and requirements. Thus: maybe not needed; just output headings and content. Structure:

      About the role

      ...

      Responsibilities

      • Architect and deliver enterprise-scale AI systems spanning data products, retrieval pipelines, model orchestration, agentic workflows, evaluation, deployment, monitoring, optimization, and lifecycle management.
      • Design secure, scalable, cloud-native and hybrid architectures across AWS, Azure, and Google Cloud, including modern AI platform services such as Amazon Bedrock, Azure AI Foundry, Google Vertex AI, and enterprise data platforms.
      ... etc for each sentence. Similarly for Requirements: maybe combine experience, consulting, hands-on, etc. We need to decide sections: About the role, Responsibilities, Requirements (maybe combine What You’ll Bring into Requirements and Skills). Could split: Requirements (experience years, consulting, hands-on), Skills (technical proficiencies). But we can also have one Requirements section with all. Given instruction to prefer few substantive sections, we could have About the role, Responsibilities, Requirements, Benefits, Pay. We need Benefits section: from "Compensation and Benefits" paragraph includes benefits list. So we need to extract benefits: meaningful time off and paid holidays, parental leave, 401(k) with a match, a range of choices for highly subsidized health, dental, & vision coverage, adoption and fertility assistance, short/long-term disability, yearly $350 reimbursement account for any well-being-related expenses, discounted home, auto, and pet insurance. Also mention compensation details in Pay section. Pay section: base pay salary range for specific locations and all other locations, plus annual discretionary bonus. We must not include filler like "commensurate with experience". Not present. We must not include EEO etc. Now produce HTML. We need to ensure no markdown fences. Just raw HTML. Let's craft. First, About the role: combine Who You’ll Work With and the note about remote work. Paragraph: "At Slalom, we co-create modern technology and software products with clients who are accelerating their digital transformation journeys. We blend design, product engineering, analytics, automation, and AI-native delivery to build intelligent products and platforms that can operate safely at enterprise scale. As an AI/ML Architect, you’ll design and deliver production-grade AI systems that combine machine learning, generative AI, agentic workflows, modern data platforms, and cloud-native engineering across AWS, Azure, and Google Cloud. You’ll partner with clients to shape strategy, define secure and governed architectures, and move AI solutions from experimentation into reliable business operations." Then add note: "Please note: This role is not eligible for 100% remote work. Employees must live within a commutable distance of a Slalom Office and must be willing to be onsite at the client and/or Slalom office when needed up to 3 days a week." We can combine into one

      or two

      . Probably one

      with both. Now Responsibilities: list each sentence from "What You’ll Do". Let's extract sentences: - Architect and deliver enterprise-scale AI systems spanning data products, retrieval pipelines, model orchestration, agentic workflows, evaluation, deployment, monitoring, optimization, and lifecycle management. - Design secure, scalable, cloud-native and hybrid architectures across AWS, Azure, and Google Cloud, including modern AI platform services such as Amazon Bedrock, Azure AI Foundry, Google Vertex AI, and enterprise data platforms. - Lead applied AI solution design across generative AI, agentic AI, multimodal AI, advanced RAG, knowledge assistants, prediction, optimization, computer vision, and decision-support use cases. - Enable production GenAI and agentic AI adoption, including advanced RAG, tool/function calling, structured outputs, workflow orchestration, model routing, prompt and context engineering, memory patterns, and human-in-the-loop controls. - Define AI evaluation, observability, and reliability patterns, including offline test sets, automated evals, tracing, hallucination detection, quality scoring, latency/cost monitoring, feedback loops, and regression testing. - Champion Responsible AI and AI security practices, including governance, explainability, privacy, bias mitigation, guardrails, data protection, threat modeling, access controls, auditability, and compliance-by-design. - Evaluate emerging models, platforms, frameworks, standards, and deployment patterns, providing practical recommendations based on use case fit, enterprise readiness, cost, risk, and operational complexity. - Lead and mentor cross-functional delivery teams of data engineers, AI engineers, ML engineers, software engineers, architects, and consultants, ensuring on-time, high-quality outcomes. - Support business development through proposals, client pitches, solution accelerators, reference architectures, technical points of view, and thought leadership. - Coach and mentor junior consultants, fostering a culture of continuous learning, engineering discipline, responsible innovation, and practical AI adoption across the AI/ML practice. Now Requirements/Qualifications/Skills: from "What You’ll Bring". Let's break into experience requirements and skills. Sentences: - 6+ years of experience implementing ML/AI solutions in production, including classical ML, deep learning, generative AI, or agentic AI systems. - 3+ years of experience in professional consulting or IT services, with proven ability to lead client-facing technical engagements. - Hands-on experience designing production AI systems that combine models, data, retrieval, orchestration, APIs, security controls, observability, and user experience into an end-to-end architecture. - Deep understanding of modern GenAI patterns, including advanced RAG, embeddings, vector and hybrid search, re-ranking, knowledge graphs, tool/function calling, structured outputs, context engineering, and multimodal inputs. - Experience with agentic AI architecture patterns, including single-agent and multi-agent workflows, supervisor/worker patterns, state and memory management, workflow orchestration, human approval gates, and safe action execution. - Proficiency with modern AI engineering frameworks and tools such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, Haystack, CrewAI, Hugging Face, or comparable open-source and cloud-native frameworks. - Strong programming skills in Python and modern software engineering practices, with familiarity in APIs, event-driven patterns, test automation, infrastructure as code, and scalable service design. - Proficiency in cloud AI/ML platforms and services such as Amazon Bedrock, AWS SageMaker, Azure AI Foundry, Azure Machine Learning, Google Vertex AI, and related model hosting, retrieval, agent, and evaluation capabilities. - Experience with enterprise data and AI ecosystems such as Databricks, Snowflake, Spark, Kafka, dbt, vector databases, lakehouse architectures, and modern data governance patterns. - Experience with MLOps, LLMOps, CI/CD, model and prompt versioning, automated evaluation, observability, containerization, Kubernetes, serverless deployment, and cost/performance optimization. - Strong understanding of AI architecture tradeoffs, including model selection, retrieval strategy, latency, accuracy, security, privacy, scalability, cost, vendor lock-in, and operating model implications. - Ability to communicate complex AI concepts to technical and non-technical stakeholders, translating architecture choices into business value, delivery risk, and executive-level decisions. - Experience managing delivery teams and shaping AI/ML roadmaps, reference architectures, implementation backlogs, and adoption plans for enterprise clients. - Strong problem-solving, critical thinking, and business acumen, with the judgment to distinguish viable production solutions from prototype-only patterns. We could split into Requirements (experience years, consulting, hands-on) and Skills (technical proficiencies). But to keep few sections, maybe just one Requirements section covering all. However the instruction says use headings like Requirements, Qualifications, Skills. We could have Requirements (experience) and Skills (technical). But we risk thin sections. However we have plenty of content for each. Let's do:

      Requirements

      • 6+ years of experience implementing ML/AI solutions in production, including classical ML, deep learning, generative AI, or agentic AI systems.
      • 3+ years of experience in professional consulting or IT services, with proven ability to lead client-facing technical engagements.
      • Hands-on experience designing production AI systems that combine models, data, retrieval, orchestration, APIs, security controls, observability, and user experience into an end-to-end architecture.

      Skills

      • Deep understanding of modern GenAI patterns, including advanced RAG, embeddings, vector and hybrid search, re-ranking, knowledge graphs, tool/function calling, structured outputs, context engineering, and multimodal inputs.
      • Experience with agentic AI architecture patterns, including single-agent and multi-agent workflows, supervisor/worker patterns, state and memory management, workflow orchestration, human approval gates, and safe action execution.
      • Proficiency with modern AI engineering frameworks and tools such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, Haystack, CrewAI, Hugging Face, or comparable open-source and cloud-native frameworks.
      • Strong programming skills in Python and modern software engineering practices, with familiarity in APIs, event-driven patterns, test automation, infrastructure as code, and scalable service design.
      • Proficiency in cloud AI/ML platforms and services such as Amazon Bedrock, AWS SageMaker, Azure AI Foundry, Azure Machine Learning, Google Vertex AI, and related model hosting, retrieval, agent, and evaluation capabilities.
      • Experience with enterprise data and AI ecosystems such as Databricks, Snowflake, Spark, Kafka, dbt, vector databases, lakehouse architectures, and modern data governance patterns.