AI/ML Architect - Principal (US - EAST)

We need to output only HTML fragment, with headings

for sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title as heading. Must not include Job ID etc. Remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning, duplicated lines, application instructions. Also remove "Please note: This role is not eligible for 100% remote work..."? That is about remote work eligibility; it's not a section heading but could be part of About the role? It's a note about location requirement. Should we keep it? It's substantive info about location requirement. It's not a heading; we can put it in a

under About the role maybe. The instruction: "Preserve ALL substantive information; do NOT invent or summarize away detail." So we need to keep that note. We need to parse the posting. Sections present: - 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? It's about company; could be omitted? The instruction: include a section ONLY when the posting has real, specific content for it. About Us is present but is it considered a section? The allowed headings list includes About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. About Us is not in list, so we should not create a heading for it. We could incorporate its content into About the role? But About the role is separate. The posting has "About Us" describing Slalom. Could we treat that as part of About the role? Probably not; but we cannot create a heading not in list. So we should either drop that content or incorporate into About the role? The instruction: "Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short

or drop it." It doesn't forbid other headings not in list, but says "Use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." The e.g. suggests those are examples, but likely they want only those headings. Safer to only use those headings. So we need to map content to those sections. We have: - About the role: could include "Who You’ll Work With" and maybe the note about remote work eligibility and location requirement. Also maybe "About Us" could be considered part of About the role? But it's about company, not role. Might be okay to include as part of About the role? The instruction: "Prefer a few substantive sections over many thin ones." So we could combine About the role and About Us into About the role? Might be okay. - Responsibilities: "What You’ll Do" bullet list. - Requirements: maybe "What You’ll Bring" includes experience, years, etc. Could split into Requirements (experience years) and Qualifications (specific skills) and Skills (tools). But we need to avoid thin sections. Could combine Requirements and Qualifications into one? But we have separate headings allowed. We need to decide based on content. The posting includes: What You’ll Bring: - 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. That's a lot. Could be split into Requirements (years of experience) and Qualifications (specific experience) and Skills (tools/frameworks). But we need to avoid thin sections. However each of those would be substantive. We also have Compensation and Benefits section: includes benefits description and pay range. So we need Benefits and Pay sections. Schedule: The posting mentions remote work note: "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." That's schedule info (onsite up to 3 days a week). So we can create a Schedule section with that. Thus we will have sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to ensure we don't create empty sections. All have content. Now we need to strip boilerplate: EEO and Accommodations, etc. Also remove "Please note: This role is not eligible for 100% remote work..."? Actually that's part of schedule note; we keep it. Also remove "Compensation and Benefits" heading? We'll use Benefits and Pay headings. We need to preserve all substantive information: So we need to include the benefits description: "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." Also pay range: "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 but maybe trim the contact line? It's substantive? It's contact info; maybe we can keep as part of Pay section. Now About the role: Should include "Who You’ll Work With" paragraph and maybe the note about remote work eligibility? Actually that note is schedule. We'll put schedule separate. Thus 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." That's from the "Who You’ll Work With" paragraph. Now Responsibilities: "What You’ll Do" bullet list. We need to convert each sentence into

items. The paragraph is a long sentence with many items separated by periods. We can split into separate responsibilities. Let's extract: - 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: Could be the years of experience items: "6+ years of experience implementing ML/AI solutions in production, including classical ML, deep learning, generative AI, or agentic AI systems." and "3+ years of experience in professional consulting or IT services, with proven ability to lead client-facing technical engagements." Also maybe "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." That could be Qualifications. But we need to decide. Let's define: Requirements: minimum years of experience. Qualifications: specific experience and understanding. Skills: technical skills, frameworks, tools, programming, cloud platforms, etc. Thus: Requirements (

• ): - 6+ years of experience implementing ML/AI solutions in production, including classical ML,