AI/ML Architect - Principal (US - EAST)

We need to output HTML fragment with sections: headings using

for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections with real specific content. The posting includes: "Who You’ll Work With" (maybe About the role?), "What You’ll Do" (Responsibilities), "What You’ll Bring" (Requirements/Qualifications/Skills), "About Us" (maybe not needed), "Compensation and Benefits" (Benefits and Pay). Also note about remote work eligibility etc. Should we include a section for "About the role"? The posting includes "Who You’ll Work With" and description of role context. Could treat that as About the role. Also there is note about remote work eligibility (must live within commutable distance, onsite up to 3 days a week). That could be Schedule? Actually schedule is about remote/hybrid. Could include Schedule section with that info. Benefits section: they list benefits. Pay section: they list salary ranges. Also note about bonus. We must not repeat job title as heading. No Job ID. Remove boilerplate: EEO, accommodations, etc. Also remove application instructions (none). Remove duplicated lines. We need to preserve all substantive information. So include details about responsibilities, requirements, etc. We must not create sections with filler like "commensurate with experience". Not present. We need to decide which sections to include: About the role (maybe from "Who You’ll Work With" and initial paragraph). Responsibilities (What You’ll Do). Requirements (What You’ll Bring). Skills maybe part of Requirements; but we could combine Requirements and Skills into one? The instruction: use headings for those categories if content exists. Could split: Requirements (experience years), Qualifications (maybe education? not present), Skills (technical skills). But we have a lot of detail; we could make a single "Requirements" heading covering all. However they said prefer a few substantive sections over many thin ones. So maybe we combine into "Requirements" covering experience, skills, qualifications. But we also have Benefits and Pay sections. Schedule: we have info about remote work eligibility and onsite up to 3 days a week. That's schedule. Thus sections: About the role, Responsibilities, Requirements, Benefits, Pay, Schedule. Check if any content for Qualifications separate? Not needed. Now craft HTML. We need to wrap each section:

About the role

then

... maybe multiple paragraphs. For lists use

• . We must not include markdown fences. Let's extract content. About the role: From "Who You’ll Work With" 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." Also there is initial note about remote work eligibility? That's schedule. Thus About the role paragraph as above. Responsibilities: "What You’ll Do" bullet list (actually sentences). We'll convert each sentence to
• . Let's list: - 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. That's responsibilities. Requirements: "What You’ll Bring" list. Let's extract each bullet: - 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 also include "About Us" but not needed. Benefits: From "Compensation and Benefits" paragraph: "Slalom prides itself on helping team members thrive in their work and life. As a result, Slalom is proud to invest in benefits that include 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, and short/long-term disability. We also offer yearly $350 reimbursement account for any well-being-related expenses, as well as discounted home, auto, and pet insurance." We should keep that as benefits list. Pay: Salary ranges: "For this role, we are hiring at the Principal level and targeted base pay salary range as follows: Boston, New Jersey, New York City, Washington DC, White Plains: $171,000 - $214,000 All other locations: $157,000 - $196,000 In addition, individuals may be eligible for an annual discretionary bonus. Actual compensation will depend upon an individual’s skills, experience, qualifications, location, and other relevant factors. The salary pay range is subject to change and may be modified at any time. We are committed to pay transparency and compliance with applicable laws. If you have questions or concerns about the pay range or other compensation information in this posting, please contact us at: peopleone@slalom.com." We should keep that as pay info. Note: we should not include contact email? It's part of compensation info; maybe we can keep but it's not boilerplate? It's contact for pay questions; could be considered part of pay section. Probably okay to keep. Schedule: The note at top: "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 put that in Schedule section. Now produce HTML. We need to ensure no extra whitespace? It's fine. Let's craft:

  About the role

  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.

  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.
  • 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