AI/ML Architect - Principal (US - EAST)
Slalom · Miami, FL · 1 mo ago
Art & Creative$171k–$214k/yrFull-time
We need to output HTML fragment with sections: headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We must not repeat job title as heading. We must include sections only if posting has real specific content. We need to extract content.
Let's parse posting:
- Intro: "AI/ML Architect – Principal **** 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. **** Who You’ll Work WithAt 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 "Who You’ll Work With" maybe corresponds to "About the role"? Actually "Who You’ll Work With" is a section describing team and context. Could map to "About the role" or "Who You'll Work With". The instruction says use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we need to map content to those headings.
We have sections:
- 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? Could be omitted as not in list)
- Compensation and Benefits (Pay and Benefits)
- EEO and Accommodations (boilerplate to remove)
- Also note about remote work eligibility (maybe schedule? It's about onsite up to 3 days a week; could be Schedule? Actually schedule is about work schedule, remote eligibility. Could be placed under Schedule heading.
We must not include boilerplate like EEO, accommodations, legal, etc. Remove duplicated lines.
We must preserve all substantive information.
We need to output only HTML fragment, no /, no markdown fences.
We need to use
for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We must not repeat job title as heading. We must include sections only if posting has real specific content. We need to extract content.
Let's parse posting:
- Intro: "AI/ML Architect – Principal **** 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. **** Who You’ll Work WithAt 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 "Who You’ll Work With" maybe corresponds to "About the role"? Actually "Who You’ll Work With" is a section describing team and context. Could map to "About the role" or "Who You'll Work With". The instruction says use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we need to map content to those headings.
We have sections:
- 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? Could be omitted as not in list)
- Compensation and Benefits (Pay and Benefits)
- EEO and Accommodations (boilerplate to remove)
- Also note about remote work eligibility (maybe schedule? It's about onsite up to 3 days a week; could be Schedule? Actually schedule is about work schedule, remote eligibility. Could be placed under Schedule heading.
We must not include boilerplate like EEO, accommodations, legal, etc. Remove duplicated lines.
We must preserve all substantive information.
We need to 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 which sections to include: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule.
We have content for:
- About the role: Could be "Who You’ll Work With" paragraph describing Slalom and role context. That's substantive.
- Responsibilities: "What You’ll Do" bullet list (actually paragraphs with semicolons). We need to turn into list items.
- Requirements/Qualifications/Skills: "What You’ll Bring" includes many items: experience, years, etc. Could split into Requirements (experience), Qualifications (maybe education? none), Skills (technical). But we can combine into Requirements and Skills sections. The instruction: prefer a few substantive sections over many thin ones. So maybe we combine Requirements and Qualifications into one section? But we have headings list; we can include both if we have content. However we must not create sections with filler. We have plenty of content for Requirements (experience, years, etc.) and Skills (technical proficiencies). Could make two sections: Requirements and Skills.
- Benefits: "Compensation and Benefits" includes benefits description: meaningful time off, paid holidays, parental leave, 401(k) with match, health/dental/vision, adoption/fertility assistance, short/long-term disability, yearly $350 reimbursement, discounted home/auto/pet insurance. Also mention discretionary bonus. That's benefits.
- Pay: salary ranges: Boston, New Jersey, New York City, Washington DC, White Plains: $171,000 - $214,000; All other locations: $157,000 - $196,000. Also mention annual discretionary bonus. That's pay.
- Schedule: note about remote work eligibility: "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." That's schedule info.
We also have "About Us" but not in headings list; we could omit as not required. Could include under About the role? But we already have Who You’ll Work With. Might be okay to omit About Us.
We must not include EEO etc.
Now format.
We need to output HTML fragment only.
Structure:
About the role
... (the Who You’ll Work With paragraph)
Responsibilities
- ...
...
We need to break responsibilities into list items. The responsibilities paragraph includes many sentences separated by periods. We can split each sentence as a list item.
Responsibilities text:
"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."
We'll make each sentence a - .
Now Requirements: maybe combine experience and qualifications.
What You’ll Bring text:
"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, hands-on experience, ability to communicate, experience managing teams) and Skills (technical proficiencies). But we can also make one Requirements section with all items as list. However instruction: prefer a few substantive sections over many thin ones. Having one Requirements section with many items is fine. But we also have Skills heading; maybe we can combine Requirements and Qualifications into one, and Skills separate. But we have no separate Qualifications content (like degrees). So we could have Requirements (experience) and Skills (technical). Let's do that.
Requirements list items: items that are about years of experience, consulting experience, hands-on experience designing production AI systems, ability to communicate, experience managing delivery teams, problem-solving, etc.
Skills list items: technical proficiencies: deep understanding of modern GenAI patterns, experience with agentic AI architecture patterns, proficiency with frameworks, strong programming skills in Python, proficiency in cloud AI/ML platforms, experience with enterprise data ecosystems, experience with MLOps/LLMOps, strong understanding of AI architecture tradeoffs.
We need to ensure we don't lose any content.
Let's extract:
From What You’ll Bring:
1. 6+ years of experience implementing ML/AI solutions in production, including classical ML, deep learning, generative AI, or agentic AI systems. -> Requirement (experience)
2. 3+ years of experience in professional consulting or IT services, with proven ability to lead client-facing technical engagements. -> Requirement (experience)
3. 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. -> Requirement (hands-on experience)
4. 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. -> Skill
5. 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. -> Skill (or requirement? It's experience)
6. 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. -> Skill
7. 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. -> Skill
8. Proficiency in cloud AI
- for any list of duties/requirements/benefits, and
for prose. We need to decide which sections to include: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We have content for: - About the role: Could be "Who You’ll Work With" paragraph describing Slalom and role context. That's substantive. - Responsibilities: "What You’ll Do" bullet list (actually paragraphs with semicolons). We need to turn into list items. - Requirements/Qualifications/Skills: "What You’ll Bring" includes many items: experience, years, etc. Could split into Requirements (experience), Qualifications (maybe education? none), Skills (technical). But we can combine into Requirements and Skills sections. The instruction: prefer a few substantive sections over many thin ones. So maybe we combine Requirements and Qualifications into one section? But we have headings list; we can include both if we have content. However we must not create sections with filler. We have plenty of content for Requirements (experience, years, etc.) and Skills (technical proficiencies). Could make two sections: Requirements and Skills. - Benefits: "Compensation and Benefits" includes benefits description: meaningful time off, paid holidays, parental leave, 401(k) with match, health/dental/vision, adoption/fertility assistance, short/long-term disability, yearly $350 reimbursement, discounted home/auto/pet insurance. Also mention discretionary bonus. That's benefits. - Pay: salary ranges: Boston, New Jersey, New York City, Washington DC, White Plains: $171,000 - $214,000; All other locations: $157,000 - $196,000. Also mention annual discretionary bonus. That's pay. - Schedule: note about remote work eligibility: "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." That's schedule info. We also have "About Us" but not in headings list; we could omit as not required. Could include under About the role? But we already have Who You’ll Work With. Might be okay to omit About Us. We must not include EEO etc. Now format. We need to output HTML fragment only. Structure:
About the role
... (the Who You’ll Work With paragraph)
Responsibilities
- ... ...
- . Now Requirements: maybe combine experience and qualifications. What You’ll Bring text: "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, hands-on experience, ability to communicate, experience managing teams) and Skills (technical proficiencies). But we can also make one Requirements section with all items as list. However instruction: prefer a few substantive sections over many thin ones. Having one Requirements section with many items is fine. But we also have Skills heading; maybe we can combine Requirements and Qualifications into one, and Skills separate. But we have no separate Qualifications content (like degrees). So we could have Requirements (experience) and Skills (technical). Let's do that. Requirements list items: items that are about years of experience, consulting experience, hands-on experience designing production AI systems, ability to communicate, experience managing delivery teams, problem-solving, etc. Skills list items: technical proficiencies: deep understanding of modern GenAI patterns, experience with agentic AI architecture patterns, proficiency with frameworks, strong programming skills in Python, proficiency in cloud AI/ML platforms, experience with enterprise data ecosystems, experience with MLOps/LLMOps, strong understanding of AI architecture tradeoffs. We need to ensure we don't lose any content. Let's extract: From What You’ll Bring: 1. 6+ years of experience implementing ML/AI solutions in production, including classical ML, deep learning, generative AI, or agentic AI systems. -> Requirement (experience) 2. 3+ years of experience in professional consulting or IT services, with proven ability to lead client-facing technical engagements. -> Requirement (experience) 3. 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. -> Requirement (hands-on experience) 4. 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. -> Skill 5. 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. -> Skill (or requirement? It's experience) 6. 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. -> Skill 7. 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. -> Skill 8. Proficiency in cloud AI