Forward Deployed Engineer – Generative AI & Azure
for section headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We must include sections only when posting has real specific content. We have content: About the role (description). Responsibilities (list of duties). Qualifications (list). Benefits (list). Pay (range). Schedule not mentioned, so omit. Skills maybe not separate; we have qualifications covering skills; but we could have Skills section if there is explicit skills list; but we can combine into Qualifications. The instruction: Use headings for those section names if we have content. We'll include About the role, Responsibilities, Qualifications, Benefits, Pay. Possibly also Skills? The posting includes qualifications broken into Generative AI & AI Engineering, Data Engineering, Azure & Cloud Engineering. That's more like skills/requirements. Could treat as Qualifications. We'll not duplicate.
We need to use - for any list of duties/requirements/benefits. Use
for prose.
We must preserve all substantive information, not invent or summarize away detail. Remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, application instructions. Also remove Equal Employment Opportunity statement, accommodation contact, company description? The company description may be considered boilerplate? It's substantive about company but maybe not required? The instruction: Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The company description is not explicitly listed to remove, but it's more about the company; could be considered not part of job description? Might be okay to keep? However they said preserve ALL substantive information; company description is substantive about the employer. But typical job posting includes it; we could keep as a
under maybe "About the company"? But we don't have a heading for that. Since we only allowed headings from list, we could embed company description in About the role paragraph? Might be okay to include as a separate
without heading. The instruction: Output ONLY the HTML fragment. So we can include multiple
and
etc.
We need to not repeat the job title as a heading. The job title is "Forward Deployed Engineer – Generative AI & Azure". Not to be used as heading.
We must not put Job ID/requisition numbers as headings; none present.
We must not create Pay/Schedule/Benefits sections unless posting actually states them. Pay is stated: Pay Range: $155,000 - $195,000. Benefits are stated. Schedule not stated, so omit.
We must not emit a heading followed by filler/placeholder text like "commensurate with experience". Not present.
Now craft HTML.
We'll start with About the role
then containing the prose from "Job Description Publicis Sapient is looking for an experienced Forward Deployed Engineer – Generative AI & Azure to design and build enterprise-grade Generative AI and data solutions on Microsoft Azure. In this role, you will combine strong software and data engineering capabilities with cloud architecture and Generative AI expertise to solve complex business problems. You will work across Azure OpenAI, LLMs, RAG architectures, AI agents, data platforms, APIs, and enterprise systems to deliver scalable and production-ready solutions. You will collaborate closely with client stakeholders, product teams, architects, and engineering teams to translate business requirements into practical technical solutions."
Then maybe a second paragraph for "Your Impact" bullet list? Actually "Your Impact" heading is not in allowed list; but we can treat those as responsibilities. So we can put responsibilities list.
Thus after About the role, we add
Responsibilities
then - each bullet from "Your Impact". Need to preserve all bullet points.
List:
- Design and develop Generative AI solutions using Azure OpenAI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and enterprise AI platforms.
- Build scalable data solutions using Azure Data Factory, Databricks Unity Catalog, data pipelines, data models, and integration patterns.
- Architect and engineer cloud-native solutions on Microsoft Azure, incorporating security, networking, monitoring, reliability, and deployment automation.
- Design solutions leveraging vector databases, embeddings, semantic search, and enterprise knowledge-management platforms.
- Build data and knowledge architectures that provide reliable enterprise context and grounding for Generative AI applications.
- Develop and integrate APIs and services that connect AI applications with enterprise data and operational systems.
- Apply strong engineering practices to take AI solutions from experimentation and prototypes to scalable enterprise implementations.
- Work closely with stakeholders to understand complex business problems and translate them into effective technical solutions.
- Contribute to solution architecture decisions across AI, data, integration, and cloud engineering.
- Work with Azure AI Foundry to accelerate enterprise AI solution development.
Now Qualifications: we have sections: Generative AI & AI Engineering, Data Engineering, Azure & Cloud Engineering, Additional Information? Actually Additional Information includes benefits; we already have benefits.
We'll create
Qualifications
then maybe subheadings? Not allowed; only headings from list. Could we embed subheadings as inside ? Probably okay. But we need to use
- for any list of duties/requirements/benefits. So we can make a list of qualifications.
List qualifications bullet points:
Generative AI & AI Engineering:
- Hands-on experience designing and developing solutions using LLMs and Generative AI.
- Strong experience with Azure OpenAI or comparable enterprise LLM platforms.
- Experience implementing RAG architectures, AI agents, embeddings, vector search, semantic search, and knowledge-grounded AI solutions.
- Understanding of enterprise AI architecture and patterns for integrating AI solutions with business applications and data.
Data Engineering:
- Strong experience with Azure Data Factory and Databricks.
- Experience designing and building scalable data pipelines and integration workflows.
- Understanding of data modeling, governance, data quality, and enterprise data architecture.
- Experience with Databricks Unity Catalog is preferred.
Azure & Cloud Engineering:
- Strong hands-on experience with Microsoft Azure.
- Understanding of Azure architecture across security, networking, monitoring, and deployment automation.
- Experience designing scalable cloud-based enterprise applications and services.
We could flatten into a single list, but better to keep grouping maybe via
headings? Not allowed. Could use
Generative AI & AI Engineering:
then etc. That's okay: we can have for prose, and
- for list. So we can have a
with bold label then a
. That satisfies using - for list.
Similarly for Benefits: we have list:
- Wellness program and employee assistance.
- Generous paid leave and holidays.
- Comprehensive health benefits for you and your family.
- Flexibility to support work-life balance.
- Competitive compensation and benefits package.
- Access to ongoing learning and development opportunities.
- An inclusive workplace that promotes diversity and collaboration.
Pay: we have Pay Range: $155,000 - $195,000 plus note about range. We'll put in
.
We must not include boilerplate like Equal Employment Opportunity etc. Also remove Company Description? It's not in the list of headings; but we could keep as a
without heading? The instruction: Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. Company description is not those; but it's not part of the job description per se; but it's substantive about the company. I'd keep it maybe as a
after Pay? However the instruction says "Preserve ALL substantive information". So we should keep it. But we have no heading for it; we can just add a
with that description.
But we need to avoid duplication: The company description appears at end. We'll include it as a
.
Now ensure we don't include any markdown fences.
Let's craft HTML:
About the role
Publicis Sapient is looking for an experienced Forward Deployed Engineer – Generative AI & Azure to design and build enterprise-grade Generative AI and data solutions on Microsoft Azure. In this role, you will combine strong software and data engineering capabilities with cloud architecture and Generative AI expertise to solve complex business problems. You will work across Azure OpenAI, LLMs, RAG architectures, AI agents, data platforms, APIs, and enterprise systems to deliver scalable and production-ready solutions. You will collaborate closely with client stakeholders, product teams, architects, and engineering teams to translate business requirements into practical technical solutions.
Responsibilities
- Design and develop Generative AI solutions using Azure OpenAI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and enterprise AI platforms.
- Build scalable data solutions using Azure Data Factory, Databricks Unity Catalog, data pipelines, data models, and integration patterns.
- Architect and engineer cloud-native solutions on Microsoft Azure, incorporating security, networking, monitoring, reliability, and deployment automation.
- Design solutions leveraging vector databases, embeddings, semantic search, and enterprise knowledge-management platforms.
- Build data and knowledge architectures that provide reliable enterprise context and grounding for Generative AI applications.
- Develop and integrate APIs and services that connect AI applications with enterprise data and operational systems.
- Apply strong engineering practices to take AI solutions from experimentation and prototypes to scalable enterprise implementations.
- Work closely with stakeholders to understand complex business problems and translate them into effective technical solutions.
- Contribute to solution architecture decisions across AI, data, integration, and cloud engineering.
- Work with Azure AI Foundry to accelerate enterprise AI solution development.
Qualifications
Generative AI & AI Engineering:
- Hands-on experience designing and developing solutions using LLMs and Generative AI.
- Strong experience with Azure OpenAI or comparable enterprise LLM platforms.
- Experience implementing RAG architectures, AI agents, embeddings, vector search, semantic search, and knowledge-grounded AI solutions.
- Understanding of enterprise AI architecture and patterns for integrating AI solutions with business applications and data.
Data Engineering:
- Strong experience with Azure Data Factory and Databricks.
- Experience designing and building scalable data pipelines and integration workflows.
- Understanding of data modeling, governance, data quality, and enterprise data architecture.
- Experience with Databricks Unity Catalog is preferred.
Azure & Cloud Engineering:
- Strong hands-on experience with Microsoft Azure.
- Understanding of Azure architecture across security, networking, monitoring, and deployment automation.
- Experience designing scalable cloud-based enterprise applications and services.
Benefits
- Wellness program and employee assistance.
- Generous paid leave and holidays.
- Comprehensive health benefits for you and your family.
- Flexibility to support work-life balance.
- Competitive compensation and benefits package.
- Access to ongoing learning and development opportunities.
- An inclusive workplace that promotes diversity and collaboration.
Pay
Pay Range: $155,000 - $195,000. The range shown represents a grouping of relevant ranges currently in use at Publicis Sapient. Actual range for this position may differ, depending on location and specific skillset required for the work itself.
Publicis Sapient is a digital transformation partner helping established organizations get to their future, digitally-enabled state, both in the way they work
for prose. We must preserve all substantive information, not invent or summarize away detail. Remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, application instructions. Also remove Equal Employment Opportunity statement, accommodation contact, company description? The company description may be considered boilerplate? It's substantive about company but maybe not required? The instruction: Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The company description is not explicitly listed to remove, but it's more about the company; could be considered not part of job description? Might be okay to keep? However they said preserve ALL substantive information; company description is substantive about the employer. But typical job posting includes it; we could keep as a
under maybe "About the company"? But we don't have a heading for that. Since we only allowed headings from list, we could embed company description in About the role paragraph? Might be okay to include as a separate
without heading. The instruction: Output ONLY the HTML fragment. So we can include multiple
and
- etc.
We need to not repeat the job title as a heading. The job title is "Forward Deployed Engineer – Generative AI & Azure". Not to be used as heading.
We must not put Job ID/requisition numbers as headings; none present.
We must not create Pay/Schedule/Benefits sections unless posting actually states them. Pay is stated: Pay Range: $155,000 - $195,000. Benefits are stated. Schedule not stated, so omit.
We must not emit a heading followed by filler/placeholder text like "commensurate with experience". Not present.
Now craft HTML.
We'll start with
- each bullet from "Your Impact". Need to preserve all bullet points.
List:
- Design and develop Generative AI solutions using Azure OpenAI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and enterprise AI platforms.
- Build scalable data solutions using Azure Data Factory, Databricks Unity Catalog, data pipelines, data models, and integration patterns.
- Architect and engineer cloud-native solutions on Microsoft Azure, incorporating security, networking, monitoring, reliability, and deployment automation.
- Design solutions leveraging vector databases, embeddings, semantic search, and enterprise knowledge-management platforms.
- Build data and knowledge architectures that provide reliable enterprise context and grounding for Generative AI applications.
- Develop and integrate APIs and services that connect AI applications with enterprise data and operational systems.
- Apply strong engineering practices to take AI solutions from experimentation and prototypes to scalable enterprise implementations.
- Work closely with stakeholders to understand complex business problems and translate them into effective technical solutions.
- Contribute to solution architecture decisions across AI, data, integration, and cloud engineering.
- Work with Azure AI Foundry to accelerate enterprise AI solution development.
Now Qualifications: we have sections: Generative AI & AI Engineering, Data Engineering, Azure & Cloud Engineering, Additional Information? Actually Additional Information includes benefits; we already have benefits.
We'll create
Qualifications
then maybe subheadings? Not allowed; only headings from list. Could we embed subheadings as inside? Probably okay. But we need to use
- for any list of duties/requirements/benefits. So we can make a list of qualifications.
List qualifications bullet points:
Generative AI & AI Engineering:
- Hands-on experience designing and developing solutions using LLMs and Generative AI.
- Strong experience with Azure OpenAI or comparable enterprise LLM platforms.
- Experience implementing RAG architectures, AI agents, embeddings, vector search, semantic search, and knowledge-grounded AI solutions.
- Understanding of enterprise AI architecture and patterns for integrating AI solutions with business applications and data.
Data Engineering:
- Strong experience with Azure Data Factory and Databricks.
- Experience designing and building scalable data pipelines and integration workflows.
- Understanding of data modeling, governance, data quality, and enterprise data architecture.
- Experience with Databricks Unity Catalog is preferred.
Azure & Cloud Engineering:
- Strong hands-on experience with Microsoft Azure.
- Understanding of Azure architecture across security, networking, monitoring, and deployment automation.
- Experience designing scalable cloud-based enterprise applications and services.
We could flatten into a single list, but better to keep grouping maybe via
headings? Not allowed. Could use
Generative AI & AI Engineering:
then- etc. That's okay: we can have
- for list. So we can have a
with bold label then a
- . That satisfies using
- for list.
Similarly for Benefits: we have list:
- Wellness program and employee assistance.
- Generous paid leave and holidays.
- Comprehensive health benefits for you and your family.
- Flexibility to support work-life balance.
- Competitive compensation and benefits package.
- Access to ongoing learning and development opportunities.
- An inclusive workplace that promotes diversity and collaboration.
Pay: we have Pay Range: $155,000 - $195,000 plus note about range. We'll put in
. We must not include boilerplate like Equal Employment Opportunity etc. Also remove Company Description? It's not in the list of headings; but we could keep as a
without heading? The instruction: Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. Company description is not those; but it's not part of the job description per se; but it's substantive about the company. I'd keep it maybe as a
after Pay? However the instruction says "Preserve ALL substantive information". So we should keep it. But we have no heading for it; we can just add a
with that description. But we need to avoid duplication: The company description appears at end. We'll include it as a
. Now ensure we don't include any markdown fences. Let's craft HTML:
About the role
Publicis Sapient is looking for an experienced Forward Deployed Engineer – Generative AI & Azure to design and build enterprise-grade Generative AI and data solutions on Microsoft Azure. In this role, you will combine strong software and data engineering capabilities with cloud architecture and Generative AI expertise to solve complex business problems. You will work across Azure OpenAI, LLMs, RAG architectures, AI agents, data platforms, APIs, and enterprise systems to deliver scalable and production-ready solutions. You will collaborate closely with client stakeholders, product teams, architects, and engineering teams to translate business requirements into practical technical solutions.
Responsibilities
- Design and develop Generative AI solutions using Azure OpenAI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and enterprise AI platforms.
- Build scalable data solutions using Azure Data Factory, Databricks Unity Catalog, data pipelines, data models, and integration patterns.
- Architect and engineer cloud-native solutions on Microsoft Azure, incorporating security, networking, monitoring, reliability, and deployment automation.
- Design solutions leveraging vector databases, embeddings, semantic search, and enterprise knowledge-management platforms.
- Build data and knowledge architectures that provide reliable enterprise context and grounding for Generative AI applications.
- Develop and integrate APIs and services that connect AI applications with enterprise data and operational systems.
- Apply strong engineering practices to take AI solutions from experimentation and prototypes to scalable enterprise implementations.
- Work closely with stakeholders to understand complex business problems and translate them into effective technical solutions.
- Contribute to solution architecture decisions across AI, data, integration, and cloud engineering.
- Work with Azure AI Foundry to accelerate enterprise AI solution development.
Qualifications
Generative AI & AI Engineering:
- Hands-on experience designing and developing solutions using LLMs and Generative AI.
- Strong experience with Azure OpenAI or comparable enterprise LLM platforms.
- Experience implementing RAG architectures, AI agents, embeddings, vector search, semantic search, and knowledge-grounded AI solutions.
- Understanding of enterprise AI architecture and patterns for integrating AI solutions with business applications and data.
Data Engineering:
- Strong experience with Azure Data Factory and Databricks.
- Experience designing and building scalable data pipelines and integration workflows.
- Understanding of data modeling, governance, data quality, and enterprise data architecture.
- Experience with Databricks Unity Catalog is preferred.
Azure & Cloud Engineering:
- Strong hands-on experience with Microsoft Azure.
- Understanding of Azure architecture across security, networking, monitoring, and deployment automation.
- Experience designing scalable cloud-based enterprise applications and services.
Benefits
- Wellness program and employee assistance.
- Generous paid leave and holidays.
- Comprehensive health benefits for you and your family.
- Flexibility to support work-life balance.
- Competitive compensation and benefits package.
- Access to ongoing learning and development opportunities.
- An inclusive workplace that promotes diversity and collaboration.
Pay
Pay Range: $155,000 - $195,000. The range shown represents a grouping of relevant ranges currently in use at Publicis Sapient. Actual range for this position may differ, depending on location and specific skillset required for the work itself.
Publicis Sapient is a digital transformation partner helping established organizations get to their future, digitally-enabled state, both in the way they work
- for list.
Similarly for Benefits: we have list:
- Wellness program and employee assistance.
- Generous paid leave and holidays.
- Comprehensive health benefits for you and your family.
- Flexibility to support work-life balance.
- Competitive compensation and benefits package.
- Access to ongoing learning and development opportunities.
- An inclusive workplace that promotes diversity and collaboration.
Pay: we have Pay Range: $155,000 - $195,000 plus note about range. We'll put in
for prose, and
- for list. So we can have a
- for any list of duties/requirements/benefits. So we can make a list of qualifications.
List qualifications bullet points:
Generative AI & AI Engineering:
- Hands-on experience designing and developing solutions using LLMs and Generative AI.
- Strong experience with Azure OpenAI or comparable enterprise LLM platforms.
- Experience implementing RAG architectures, AI agents, embeddings, vector search, semantic search, and knowledge-grounded AI solutions.
- Understanding of enterprise AI architecture and patterns for integrating AI solutions with business applications and data.
Data Engineering:
- Strong experience with Azure Data Factory and Databricks.
- Experience designing and building scalable data pipelines and integration workflows.
- Understanding of data modeling, governance, data quality, and enterprise data architecture.
- Experience with Databricks Unity Catalog is preferred.
Azure & Cloud Engineering:
- Strong hands-on experience with Microsoft Azure.
- Understanding of Azure architecture across security, networking, monitoring, and deployment automation.
- Experience designing scalable cloud-based enterprise applications and services.
We could flatten into a single list, but better to keep grouping maybe via
About the role
thencontaining the prose from "Job Description Publicis Sapient is looking for an experienced Forward Deployed Engineer – Generative AI & Azure to design and build enterprise-grade Generative AI and data solutions on Microsoft Azure. In this role, you will combine strong software and data engineering capabilities with cloud architecture and Generative AI expertise to solve complex business problems. You will work across Azure OpenAI, LLMs, RAG architectures, AI agents, data platforms, APIs, and enterprise systems to deliver scalable and production-ready solutions. You will collaborate closely with client stakeholders, product teams, architects, and engineering teams to translate business requirements into practical technical solutions." Then maybe a second paragraph for "Your Impact" bullet list? Actually "Your Impact" heading is not in allowed list; but we can treat those as responsibilities. So we can put responsibilities list. Thus after About the role, we add