Agentic AI / Machine Learning Architect - Senior Principal
Slalom · New York, NY · 1 mo ago
Engineering$215k–$275k/yrFull-time
We need to output HTML fragment with sections: headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Use
for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Use - for any list of duties/requirements/benefits, and
for prose. Do NOT repeat job title as heading. Do NOT put Job ID/requisition numbers as headings. Preserve all substantive info, remove boilerplate (cookie/navigation/legal/EEO/equal-opportunity/scam-warning, duplicated lines, application instructions). Include a section only when posting has real specific content for it; never emit heading with filler/placeholder text. Prefer few substantive sections over many thin ones.
We need to parse the posting.
Content sections:
- "Who You’ll Work With": description of Slalom and role context. Could be "About the role" maybe.
- "What You’ll Do": responsibilities.
- "What You’ll Bring": qualifications/requirements/skills.
- "About Us": company description.
- "Compensation and Benefits": includes benefits and pay ranges.
- "EEO and Accommodations": boilerplate, should be removed.
We need to map to allowed headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule.
We can combine some.
We need to output only HTML fragment, no /, no markdown fences.
We need to include sections only if content present.
We need to use
for each section heading.
Inside sections, use
for prose,
- for lists.
We must preserve all substantive information.
We need to remove duplicated lines, boilerplate.
Let's extract substantive content.
From "Who You’ll Work With":
"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 prose.
From "What You’ll Do": list of bullet-like sentences each starting with a verb. Could be turned into
- .
Let's extract each bullet:
- Set technical direction for enterprise-scale AI systems spanning data products, retrieval pipelines, model orchestration, agentic workflows, evaluation, deployment, monitoring, optimization, and lifecycle management.
- Define secure, scalable, cloud-native and hybrid reference 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.
- Guide applied AI strategy and delivery across generative AI, agentic AI, multimodal AI, advanced RAG, knowledge assistants, prediction, optimization, computer vision, and decision-support use cases.
- Lead enterprise adoption of production GenAI and agentic AI, including advanced RAG, tool/function calling, structured outputs, workflow orchestration, model routing, prompt and context engineering, memory patterns, and human-in-the-loop controls.
- Establish AI evaluation, observability, and reliability standards, including offline test sets, automated evals, tracing, hallucination detection, quality scoring, latency/cost monitoring, feedback loops, and regression testing.
- Champion Responsible AI, AI security, and governance-by-design practices, including explainability, privacy, bias mitigation, guardrails, data protection, threat modeling, access controls, auditability, and compliance alignment.
- Evaluate emerging models, platforms, frameworks, standards, and deployment patterns, providing executive-ready 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 consistent quality across complex programs.
- Drive business development through proposals, executive client pitches, solution accelerators, reference architectures, technical points of view, and thought leadership.
- Develop senior practitioners and practice capability, fostering a culture of continuous learning, engineering discipline, responsible innovation, and practical AI adoption across the AI/ML practice.
- Help to hire, lead, mentor, and retain a high-performing, inclusive team of AI/ML engineers, architects, and data scientists. Set clear expectations, provide timely feedback, and create meaningful development and stretch opportunities.
That's many items; we can keep as list.
From "What You’ll Bring": list of requirements.
Extract each bullet:
- 9+ years of experience implementing ML/AI solutions in production, including classical ML, deep learning, generative AI, or agentic AI systems.
- 5+ years of experience in professional consulting or IT services, with proven ability to lead complex client-facing technical engagements.
- Proven ability to design and govern production AI systems that combine models, data, retrieval, orchestration, APIs, security controls, observability, operating model, and user experience into an end-to-end enterprise architecture.
- Deep expertise in 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 defining 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 setting standards for 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, governance requirements, and executive-level decisions.
- Experience managing senior delivery teams and shaping enterprise AI/ML roadmaps, reference architectures, implementation backlogs, governance models, 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 and to guide clients through tradeoffs pragmatically.
That's many.
From "About Us": prose.
"Slalom is a fiercely human business and technology consulting company that leads with outcomes to bring more value, in all ways, always. From strategy through delivery, our agile teams across 52 offices in 12 countries partner with clients to co-create powerful customer experiences, modern ways of working, and meaningful impact. What sets us apart? We believe work should be challenging and fulfilling, not perfect, but possible. That’s why we prioritize purpose, flexibility, connection, and recognition, so our people can thrive and love what they do, most days."
From "Compensation and Benefits": includes benefits description and pay ranges.
We need to separate Benefits and Pay sections if content present.
Benefits: "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."
Pay: "Slalom is committed to fair and equitable compensation practices. For this role, we are hiring at the following levels and targeted base pay salary ranges: For Boston, New York, Washington D.C.: The targeted base salary pay range for a Sr. Principal is $215,000 to $275,000. For Atlanta, Charlotte, Philadelphia, Miami: The targeted base salary pay range for a Sr. Principal is $200,000 to $250,000. 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. Please note, this recipient is not able to support recruitment inquiries beyond this purpose."
We should keep the pay info but remove the contact line? The instruction: remove application instructions. The contact line is maybe considered application instruction? It's a contact for pay questions. Might be considered boilerplate? It's not exactly application instruction but could be considered contact info. Safer to remove it? The instruction: "Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions." The contact line is not exactly application instruction but could be considered contact for pay questions; but it's still a line that directs to email. Might be considered application instruction? I'd remove it to be safe. Also remove the sentence about "Please note, this recipient is not able to support recruitment inquiries beyond this purpose." That's definitely application instruction.
Thus keep pay description up to "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." Then stop before contact.
We also have "EEO and Accommodations" which is boilerplate; remove entirely.
Now we need to decide sections.
We have:
- About the role: could combine "Who You’ll Work With" and maybe "About Us"? But "About Us" is company description; could be separate section "About the role"? Actually "About the role" heading is for role description. "Who You’ll Work With" is about team/context. Could be part of About the role.
We could have sections:
About the role
with prose from Who You’ll Work With.
Responsibilities
with list from What You’ll Do.
Requirements
with list from What You’ll Bring (maybe combine qualifications and skills). Could split into Requirements and Skills but we can keep as Requirements.
Benefits
with list from Benefits.
Pay
with prose from Pay.
We could also have About the company
but not in allowed headings. Allowed headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we cannot add About the company. We could incorporate About Us into About the role or maybe omit? But we must preserve ALL substantive information. So we need to include About Us somewhere. Since we cannot create a heading not in list, we need to fit it into an allowed heading. Perhaps we can put About Us under About the role as additional prose. That seems okay.
Thus About the role section will contain both Who You’ll Work With and About Us.
Now we need to ensure we don't create sections with filler. All sections have content.
We must
for prose. Do NOT repeat job title as heading. Do NOT put Job ID/requisition numbers as headings. Preserve all substantive info, remove boilerplate (cookie/navigation/legal/EEO/equal-opportunity/scam-warning, duplicated lines, application instructions). Include a section only when posting has real specific content for it; never emit heading with filler/placeholder text. Prefer few substantive sections over many thin ones. We need to parse the posting. Content sections: - "Who You’ll Work With": description of Slalom and role context. Could be "About the role" maybe. - "What You’ll Do": responsibilities. - "What You’ll Bring": qualifications/requirements/skills. - "About Us": company description. - "Compensation and Benefits": includes benefits and pay ranges. - "EEO and Accommodations": boilerplate, should be removed. We need to map to allowed headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We can combine some. We need to output only HTML fragment, no /, no markdown fences. We need to include sections only if content present. We need to use
for each section heading. Inside sections, use
for prose,
- for lists.
We must preserve all substantive information.
We need to remove duplicated lines, boilerplate.
Let's extract substantive content.
From "Who You’ll Work With":
"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 prose.
From "What You’ll Do": list of bullet-like sentences each starting with a verb. Could be turned into
- .
Let's extract each bullet:
- Set technical direction for enterprise-scale AI systems spanning data products, retrieval pipelines, model orchestration, agentic workflows, evaluation, deployment, monitoring, optimization, and lifecycle management.
- Define secure, scalable, cloud-native and hybrid reference 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.
- Guide applied AI strategy and delivery across generative AI, agentic AI, multimodal AI, advanced RAG, knowledge assistants, prediction, optimization, computer vision, and decision-support use cases.
- Lead enterprise adoption of production GenAI and agentic AI, including advanced RAG, tool/function calling, structured outputs, workflow orchestration, model routing, prompt and context engineering, memory patterns, and human-in-the-loop controls.
- Establish AI evaluation, observability, and reliability standards, including offline test sets, automated evals, tracing, hallucination detection, quality scoring, latency/cost monitoring, feedback loops, and regression testing.
- Champion Responsible AI, AI security, and governance-by-design practices, including explainability, privacy, bias mitigation, guardrails, data protection, threat modeling, access controls, auditability, and compliance alignment.
- Evaluate emerging models, platforms, frameworks, standards, and deployment patterns, providing executive-ready 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 consistent quality across complex programs.
- Drive business development through proposals, executive client pitches, solution accelerators, reference architectures, technical points of view, and thought leadership.
- Develop senior practitioners and practice capability, fostering a culture of continuous learning, engineering discipline, responsible innovation, and practical AI adoption across the AI/ML practice.
- Help to hire, lead, mentor, and retain a high-performing, inclusive team of AI/ML engineers, architects, and data scientists. Set clear expectations, provide timely feedback, and create meaningful development and stretch opportunities.
That's many items; we can keep as list.
From "What You’ll Bring": list of requirements.
Extract each bullet:
- 9+ years of experience implementing ML/AI solutions in production, including classical ML, deep learning, generative AI, or agentic AI systems.
- 5+ years of experience in professional consulting or IT services, with proven ability to lead complex client-facing technical engagements.
- Proven ability to design and govern production AI systems that combine models, data, retrieval, orchestration, APIs, security controls, observability, operating model, and user experience into an end-to-end enterprise architecture.
- Deep expertise in 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 defining 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 setting standards for 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, governance requirements, and executive-level decisions.
- Experience managing senior delivery teams and shaping enterprise AI/ML roadmaps, reference architectures, implementation backlogs, governance models, 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 and to guide clients through tradeoffs pragmatically.
That's many.
From "About Us": prose.
"Slalom is a fiercely human business and technology consulting company that leads with outcomes to bring more value, in all ways, always. From strategy through delivery, our agile teams across 52 offices in 12 countries partner with clients to co-create powerful customer experiences, modern ways of working, and meaningful impact. What sets us apart? We believe work should be challenging and fulfilling, not perfect, but possible. That’s why we prioritize purpose, flexibility, connection, and recognition, so our people can thrive and love what they do, most days."
From "Compensation and Benefits": includes benefits description and pay ranges.
We need to separate Benefits and Pay sections if content present.
Benefits: "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."
Pay: "Slalom is committed to fair and equitable compensation practices. For this role, we are hiring at the following levels and targeted base pay salary ranges: For Boston, New York, Washington D.C.: The targeted base salary pay range for a Sr. Principal is $215,000 to $275,000. For Atlanta, Charlotte, Philadelphia, Miami: The targeted base salary pay range for a Sr. Principal is $200,000 to $250,000. 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. Please note, this recipient is not able to support recruitment inquiries beyond this purpose."
We should keep the pay info but remove the contact line? The instruction: remove application instructions. The contact line is maybe considered application instruction? It's a contact for pay questions. Might be considered boilerplate? It's not exactly application instruction but could be considered contact info. Safer to remove it? The instruction: "Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions." The contact line is not exactly application instruction but could be considered contact for pay questions; but it's still a line that directs to email. Might be considered application instruction? I'd remove it to be safe. Also remove the sentence about "Please note, this recipient is not able to support recruitment inquiries beyond this purpose." That's definitely application instruction.
Thus keep pay description up to "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." Then stop before contact.
We also have "EEO and Accommodations" which is boilerplate; remove entirely.
Now we need to decide sections.
We have:
- About the role: could combine "Who You’ll Work With" and maybe "About Us"? But "About Us" is company description; could be separate section "About the role"? Actually "About the role" heading is for role description. "Who You’ll Work With" is about team/context. Could be part of About the role.
We could have sections:
About the role
with prose from Who You’ll Work With.Responsibilities
with list from What You’ll Do.Requirements
with list from What You’ll Bring (maybe combine qualifications and skills). Could split into Requirements and Skills but we can keep as Requirements.Benefits
with list from Benefits.Pay
with prose from Pay. We could also haveAbout the company
but not in allowed headings. Allowed headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we cannot add About the company. We could incorporate About Us into About the role or maybe omit? But we must preserve ALL substantive information. So we need to include About Us somewhere. Since we cannot create a heading not in list, we need to fit it into an allowed heading. Perhaps we can put About Us under About the role as additional prose. That seems okay. Thus About the role section will contain both Who You’ll Work With and About Us. Now we need to ensure we don't create sections with filler. All sections have content. We must
- .
Let's extract each bullet:
- Set technical direction for enterprise-scale AI systems spanning data products, retrieval pipelines, model orchestration, agentic workflows, evaluation, deployment, monitoring, optimization, and lifecycle management.
- Define secure, scalable, cloud-native and hybrid reference 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.
- Guide applied AI strategy and delivery across generative AI, agentic AI, multimodal AI, advanced RAG, knowledge assistants, prediction, optimization, computer vision, and decision-support use cases.
- Lead enterprise adoption of production GenAI and agentic AI, including advanced RAG, tool/function calling, structured outputs, workflow orchestration, model routing, prompt and context engineering, memory patterns, and human-in-the-loop controls.
- Establish AI evaluation, observability, and reliability standards, including offline test sets, automated evals, tracing, hallucination detection, quality scoring, latency/cost monitoring, feedback loops, and regression testing.
- Champion Responsible AI, AI security, and governance-by-design practices, including explainability, privacy, bias mitigation, guardrails, data protection, threat modeling, access controls, auditability, and compliance alignment.
- Evaluate emerging models, platforms, frameworks, standards, and deployment patterns, providing executive-ready 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 consistent quality across complex programs.
- Drive business development through proposals, executive client pitches, solution accelerators, reference architectures, technical points of view, and thought leadership.
- Develop senior practitioners and practice capability, fostering a culture of continuous learning, engineering discipline, responsible innovation, and practical AI adoption across the AI/ML practice.
- Help to hire, lead, mentor, and retain a high-performing, inclusive team of AI/ML engineers, architects, and data scientists. Set clear expectations, provide timely feedback, and create meaningful development and stretch opportunities.
That's many items; we can keep as list.
From "What You’ll Bring": list of requirements.
Extract each bullet:
- 9+ years of experience implementing ML/AI solutions in production, including classical ML, deep learning, generative AI, or agentic AI systems.
- 5+ years of experience in professional consulting or IT services, with proven ability to lead complex client-facing technical engagements.
- Proven ability to design and govern production AI systems that combine models, data, retrieval, orchestration, APIs, security controls, observability, operating model, and user experience into an end-to-end enterprise architecture.
- Deep expertise in 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 defining 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 setting standards for 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, governance requirements, and executive-level decisions.
- Experience managing senior delivery teams and shaping enterprise AI/ML roadmaps, reference architectures, implementation backlogs, governance models, 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 and to guide clients through tradeoffs pragmatically.
That's many.
From "About Us": prose.
"Slalom is a fiercely human business and technology consulting company that leads with outcomes to bring more value, in all ways, always. From strategy through delivery, our agile teams across 52 offices in 12 countries partner with clients to co-create powerful customer experiences, modern ways of working, and meaningful impact. What sets us apart? We believe work should be challenging and fulfilling, not perfect, but possible. That’s why we prioritize purpose, flexibility, connection, and recognition, so our people can thrive and love what they do, most days."
From "Compensation and Benefits": includes benefits description and pay ranges.
We need to separate Benefits and Pay sections if content present.
Benefits: "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."
Pay: "Slalom is committed to fair and equitable compensation practices. For this role, we are hiring at the following levels and targeted base pay salary ranges: For Boston, New York, Washington D.C.: The targeted base salary pay range for a Sr. Principal is $215,000 to $275,000. For Atlanta, Charlotte, Philadelphia, Miami: The targeted base salary pay range for a Sr. Principal is $200,000 to $250,000. 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. Please note, this recipient is not able to support recruitment inquiries beyond this purpose."
We should keep the pay info but remove the contact line? The instruction: remove application instructions. The contact line is maybe considered application instruction? It's a contact for pay questions. Might be considered boilerplate? It's not exactly application instruction but could be considered contact info. Safer to remove it? The instruction: "Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions." The contact line is not exactly application instruction but could be considered contact for pay questions; but it's still a line that directs to email. Might be considered application instruction? I'd remove it to be safe. Also remove the sentence about "Please note, this recipient is not able to support recruitment inquiries beyond this purpose." That's definitely application instruction.
Thus keep pay description up to "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." Then stop before contact.
We also have "EEO and Accommodations" which is boilerplate; remove entirely.
Now we need to decide sections.
We have:
- About the role: could combine "Who You’ll Work With" and maybe "About Us"? But "About Us" is company description; could be separate section "About the role"? Actually "About the role" heading is for role description. "Who You’ll Work With" is about team/context. Could be part of About the role.
We could have sections: