Senior AI/ML Architect, Applied Field Engineering
for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real content.
We need to parse the job posting.
Content:
- Intro about Snowflake, AI-native thinkers, etc. Could be "About the role" or "About Snowflake"? The instruction: use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can decide appropriate headings.
We need to preserve all substantive information, not invent or summarize away detail. Remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. Also remove lines like "For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com". Also remove "To comply with pay transparency requirements..." etc.
We need to keep substantive info: description of role, responsibilities, expectations day one, qualifications, bonus points, benefits, pay range.
We need to format as semantic HTML: headings , lists - for any list of duties/requirements/benefits, and
for prose.
We must not repeat the job title as a heading (the page already shows it). So no heading for job title.
We must not put Job ID/requisition numbers as headings.
We need to include a section only when posting has real, specific content for it — never emit a heading followed by filler/placeholder text.
Thus we need to decide sections: maybe "About the role" (intro), "Responsibilities" (list of IN THIS ROLE YOU WILL GET TO...), "Requirements" (ON DAY ONE, WE WILL EXPECT YOU TO HAVE...), "Qualifications" maybe same as requirements? Could combine. But we need to avoid duplication. Could have "Qualifications" for degree etc. "Skills" maybe for technical skills. "Benefits" for benefits package. "Pay" for compensation range. "Schedule" maybe not present; we should not include if not present.
We need to preserve all substantive info; do not invent or summarize away detail. So we need to keep specifics like "5+ years of experience building and deploying machine learning and generative AI solutions in the cloud." etc.
We need to format as HTML fragment only.
Let's extract sections.
First, intro paragraph: "At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Our Solution Engineering organization is seeking an AI Specialist who can provide hands-on expertise and support while working with technical decision makers and data scientists to design and architect AI solutions built on the Snowflake AI Data Cloud. This is a strategic role that works closely with cross-functional teams, including product, engineering, and the broader field organization to ensure successful execution and customer adoption of Snowflake’s AI & ML solutions."
That could be "About the role".
Then responsibilities: bullet list under "IN THIS ROLE YOU WILL GET TO:":
- Be the technical expert in the room that positions Snowflake’s AI and ML features and value to technical stakeholders at Snowflake’s customers across the Americas.
- Partner with Snowflake account team teams and customer champions to scope and drive POCs to success and technical wins that prove the value of Snowflake’s capabilities, including executive readouts and business value cases.
- Collaborate with Snowflake’s product and engineering teams to influence Snowflake’s AI and ML roadmaps based on customer feedback.
- Publish content that helps the team and company scale beyond your individual efforts, like blog posts, presentations at conferences, or technical collateral like notebooks and demos.
- Influence, tailor and maintain Sales Engineering AI and ML selling assets, including customer presentations, demonstrations, and customer stories.
We need to put these in
- .
Then "ON DAY ONE, WE WILL EXPECT YOU TO HAVE:" list:
- 5+ years of experience building and deploying machine learning and generative AI solutions in the cloud.
- Familiarity and associated knowledge of generative AI techniques like RAG, few shot learning, prompt engineering, or fine-tuning that are used to operationalize enterprise AI use cases like interactive chat applications or text processing.
- Deep knowledge of Python and common ML packages (such as LangChain, pandas, sklearn, and PyTorch) as well as data engineering tools and technologies like dbt, Airflow, and Spark.
- Strong presentation skills to both technical and executive audiences, whether whiteboarding sessions or formal readouts and demos.
- Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent experience preferred.
We could separate qualifications (degree) and skills (experience, knowledge). But we can put all under "Requirements". Or we could have "Qualifications" for degree and "Skills" for technical skills. However we must not create thin sections; better to combine into "Requirements" covering experience, knowledge, skills, degree.
Then "BONUS POINTS FOR EXPERIENCE WITH THE FOLLOWING:" list:
- Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
- Experience and understanding of large-scale infrastructure-as-a-service platforms (e.g. AWS, Microsoft Azure, GCP, etc.)
- 1+ years of practical Snowflake experience.
- Knowledge of and experience with large-scale database technology (e.g. Snowflake, Netezza, Exadata, Teradata, Greenplum, etc.)
These could be under "Preferred Qualifications" or "Additional Skills". We could have a section "Preferred Qualifications". But the instruction only gave examples of headings; we can use others if needed? It says use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings, but suggests those. Probably better to stick to those. We could map bonus points to "Skills" or "Qualifications". But we already used Requirements for mandatory. Could have "Preferred Qualifications" as a heading but not in list; maybe okay but risk. Safer to put under "Skills" (since they are skills/experience). Or under "Qualifications". Let's decide: Use "Skills" for both mandatory and bonus? But we already have Requirements for mandatory experience. Could have "Skills" for technical skills (Python, ML packages, presentation). Bonus points could be under "Preferred Qualifications". However the instruction didn't list Preferred Qualifications as example but we could still use it; but better to follow examples. Could incorporate bonus points into "Skills" as additional skills.
Thus sections:
- About the role (intro paragraph)
- Responsibilities (list)
- Requirements (list of mandatory expectations)
- Skills (list of technical skills: deep knowledge of Python and ML packages, presentation skills, plus bonus points)
- Benefits (list of benefits)
- Pay (compensation range)
We need to ensure we don't create sections with filler. All have content.
Now we need to extract benefits paragraph: "This role is also eligible for a competitive benefits package that includes: medical, dental, vision, life, and disability insurance; 401(k) retirement plan; flexible spending & health savings account; at least 12 paid holidays; paid time off; parental leave; employee assistance program; and other company benefits."
We need to put as - each item.
Pay: "This role is eligible to participate in Snowflake's commission plan and it is common for employees in this role to receive total on-target earnings of $220,000 - $288,750. The estimated base salary for this role is $165,000 - $216,562.Additionally, this role is eligible to participate in Snowflake’s equity plan."
We need to preserve that info. Could put as
with that text.
We should not include the line about "To comply with pay transparency requirements..." etc. Remove that.
Also remove the line about "For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com". Remove.
Also remove any duplicated lines.
Now produce HTML.
Structure:
About the role
...
Responsibilities
- ...
...
Requirements
- ...
...
Skills
- Deep knowledge of Python and common ML packages (such as LangChain, pandas, sklearn, and PyTorch) as well as data engineering tools and technologies like dbt, Airflow, and Spark.
- Strong presentation skills to both technical and executive audiences, whether whiteboarding sessions or formal readouts and demos.
- Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
- Experience and understanding of large-scale infrastructure-as-a-service platforms (e.g. AWS, Microsoft Azure, GCP, etc.)
- 1+ years of practical Snowflake experience.
- Knowledge of and experience with large-scale database technology (e.g. Snowflake, Netezza, Exadata, Teradata, Greenplum, etc.)
But note: we already included deep knowledge of Python etc. in Requirements? Actually we put that under Requirements earlier. Let's decide: Requirements list includes the 5+ years experience, familiarity with generative AI techniques, deep knowledge of Python..., strong presentation skills, Bachelor's Degree required, Masters preferred. That covers a lot. Then Skills could be bonus points only. But we might duplicate deep knowledge etc. Let's split: Requirements: mandatory experience, generative AI familiarity, degree. Skills: technical skills (Python/ML packages, presentation) plus bonus points. That avoids duplication.
Thus:
Requirements ():
- 5+ years of experience building and deploying machine learning and generative AI solutions in the cloud.
- Familiarity and associated knowledge of generative AI techniques like RAG, few shot learning, prompt engineering, or fine-tuning that are used to operationalize enterprise AI use cases like interactive chat applications or text processing.
- Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent experience preferred.
Skills ():
- Deep knowledge of Python and common ML packages (such as LangChain, pandas, sklearn, and PyTorch) as well as data engineering tools and technologies like dbt, Airflow, and Spark.
- Strong presentation skills to both technical and executive audiences, whether whiteboarding sessions or formal readouts and demos.
- Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
- Experience and understanding of large-scale infrastructure-as-a-service platforms (e.g. AWS, Microsoft Azure, GCP, etc.)
- 1+ years of practical Snowflake experience.
- Knowledge of and experience with large-scale database technology (e.g. Snowflake, Netezza, Exadata, Teradata, Greenplum, etc.)
Benefits ():
- medical, dental, vision, life, and disability insurance
- 401(k) retirement plan
- flexible spending & health savings account
-
, lists - for any list of duties/requirements/benefits, and
for prose.
We must not repeat the job title as a heading (the page already shows it). So no heading for job title.
We must not put Job ID/requisition numbers as headings.
We need to include a section only when posting has real, specific content for it — never emit a heading followed by filler/placeholder text.
Thus we need to decide sections: maybe "About the role" (intro), "Responsibilities" (list of IN THIS ROLE YOU WILL GET TO...), "Requirements" (ON DAY ONE, WE WILL EXPECT YOU TO HAVE...), "Qualifications" maybe same as requirements? Could combine. But we need to avoid duplication. Could have "Qualifications" for degree etc. "Skills" maybe for technical skills. "Benefits" for benefits package. "Pay" for compensation range. "Schedule" maybe not present; we should not include if not present.
We need to preserve all substantive info; do not invent or summarize away detail. So we need to keep specifics like "5+ years of experience building and deploying machine learning and generative AI solutions in the cloud." etc.
We need to format as HTML fragment only.
Let's extract sections.
First, intro paragraph: "At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Our Solution Engineering organization is seeking an AI Specialist who can provide hands-on expertise and support while working with technical decision makers and data scientists to design and architect AI solutions built on the Snowflake AI Data Cloud. This is a strategic role that works closely with cross-functional teams, including product, engineering, and the broader field organization to ensure successful execution and customer adoption of Snowflake’s AI & ML solutions."
That could be "About the role".
Then responsibilities: bullet list under "IN THIS ROLE YOU WILL GET TO:":
- Be the technical expert in the room that positions Snowflake’s AI and ML features and value to technical stakeholders at Snowflake’s customers across the Americas.
- Partner with Snowflake account team teams and customer champions to scope and drive POCs to success and technical wins that prove the value of Snowflake’s capabilities, including executive readouts and business value cases.
- Collaborate with Snowflake’s product and engineering teams to influence Snowflake’s AI and ML roadmaps based on customer feedback.
- Publish content that helps the team and company scale beyond your individual efforts, like blog posts, presentations at conferences, or technical collateral like notebooks and demos.
- Influence, tailor and maintain Sales Engineering AI and ML selling assets, including customer presentations, demonstrations, and customer stories.
We need to put these in
- .
Then "ON DAY ONE, WE WILL EXPECT YOU TO HAVE:" list:
- 5+ years of experience building and deploying machine learning and generative AI solutions in the cloud.
- Familiarity and associated knowledge of generative AI techniques like RAG, few shot learning, prompt engineering, or fine-tuning that are used to operationalize enterprise AI use cases like interactive chat applications or text processing.
- Deep knowledge of Python and common ML packages (such as LangChain, pandas, sklearn, and PyTorch) as well as data engineering tools and technologies like dbt, Airflow, and Spark.
- Strong presentation skills to both technical and executive audiences, whether whiteboarding sessions or formal readouts and demos.
- Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent experience preferred.
We could separate qualifications (degree) and skills (experience, knowledge). But we can put all under "Requirements". Or we could have "Qualifications" for degree and "Skills" for technical skills. However we must not create thin sections; better to combine into "Requirements" covering experience, knowledge, skills, degree.
Then "BONUS POINTS FOR EXPERIENCE WITH THE FOLLOWING:" list:
- Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
- Experience and understanding of large-scale infrastructure-as-a-service platforms (e.g. AWS, Microsoft Azure, GCP, etc.)
- 1+ years of practical Snowflake experience.
- Knowledge of and experience with large-scale database technology (e.g. Snowflake, Netezza, Exadata, Teradata, Greenplum, etc.)
These could be under "Preferred Qualifications" or "Additional Skills". We could have a section "Preferred Qualifications". But the instruction only gave examples of headings; we can use others if needed? It says use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings, but suggests those. Probably better to stick to those. We could map bonus points to "Skills" or "Qualifications". But we already used Requirements for mandatory. Could have "Preferred Qualifications" as a heading but not in list; maybe okay but risk. Safer to put under "Skills" (since they are skills/experience). Or under "Qualifications". Let's decide: Use "Skills" for both mandatory and bonus? But we already have Requirements for mandatory experience. Could have "Skills" for technical skills (Python, ML packages, presentation). Bonus points could be under "Preferred Qualifications". However the instruction didn't list Preferred Qualifications as example but we could still use it; but better to follow examples. Could incorporate bonus points into "Skills" as additional skills.
Thus sections:
- About the role (intro paragraph)
- Responsibilities (list)
- Requirements (list of mandatory expectations)
- Skills (list of technical skills: deep knowledge of Python and ML packages, presentation skills, plus bonus points)
- Benefits (list of benefits)
- Pay (compensation range)
We need to ensure we don't create sections with filler. All have content.
Now we need to extract benefits paragraph: "This role is also eligible for a competitive benefits package that includes: medical, dental, vision, life, and disability insurance; 401(k) retirement plan; flexible spending & health savings account; at least 12 paid holidays; paid time off; parental leave; employee assistance program; and other company benefits."
We need to put as - each item.
Pay: "This role is eligible to participate in Snowflake's commission plan and it is common for employees in this role to receive total on-target earnings of $220,000 - $288,750. The estimated base salary for this role is $165,000 - $216,562.Additionally, this role is eligible to participate in Snowflake’s equity plan."
We need to preserve that info. Could put as
with that text.
We should not include the line about "To comply with pay transparency requirements..." etc. Remove that.
Also remove the line about "For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com". Remove.
Also remove any duplicated lines.
Now produce HTML.
Structure:
About the role
...
Responsibilities
- ...
...
Requirements
- ...
...
Skills
- Deep knowledge of Python and common ML packages (such as LangChain, pandas, sklearn, and PyTorch) as well as data engineering tools and technologies like dbt, Airflow, and Spark.
- Strong presentation skills to both technical and executive audiences, whether whiteboarding sessions or formal readouts and demos.
- Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
- Experience and understanding of large-scale infrastructure-as-a-service platforms (e.g. AWS, Microsoft Azure, GCP, etc.)
- 1+ years of practical Snowflake experience.
- Knowledge of and experience with large-scale database technology (e.g. Snowflake, Netezza, Exadata, Teradata, Greenplum, etc.)
But note: we already included deep knowledge of Python etc. in Requirements? Actually we put that under Requirements earlier. Let's decide: Requirements list includes the 5+ years experience, familiarity with generative AI techniques, deep knowledge of Python..., strong presentation skills, Bachelor's Degree required, Masters preferred. That covers a lot. Then Skills could be bonus points only. But we might duplicate deep knowledge etc. Let's split: Requirements: mandatory experience, generative AI familiarity, degree. Skills: technical skills (Python/ML packages, presentation) plus bonus points. That avoids duplication.
Thus:
Requirements ():
- 5+ years of experience building and deploying machine learning and generative AI solutions in the cloud.
- Familiarity and associated knowledge of generative AI techniques like RAG, few shot learning, prompt engineering, or fine-tuning that are used to operationalize enterprise AI use cases like interactive chat applications or text processing.
- Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent experience preferred.
Skills ():
- Deep knowledge of Python and common ML packages (such as LangChain, pandas, sklearn, and PyTorch) as well as data engineering tools and technologies like dbt, Airflow, and Spark.
- Strong presentation skills to both technical and executive audiences, whether whiteboarding sessions or formal readouts and demos.
- Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
- Experience and understanding of large-scale infrastructure-as-a-service platforms (e.g. AWS, Microsoft Azure, GCP, etc.)
- 1+ years of practical Snowflake experience.
- Knowledge of and experience with large-scale database technology (e.g. Snowflake, Netezza, Exadata, Teradata, Greenplum, etc.)
Benefits ():
- medical, dental, vision, life, and disability insurance
- 401(k) retirement plan
- flexible spending & health savings account
-
for prose. We must not repeat the job title as a heading (the page already shows it). So no heading for job title. We must not put Job ID/requisition numbers as headings. We need to include a section only when posting has real, specific content for it — never emit a heading followed by filler/placeholder text. Thus we need to decide sections: maybe "About the role" (intro), "Responsibilities" (list of IN THIS ROLE YOU WILL GET TO...), "Requirements" (ON DAY ONE, WE WILL EXPECT YOU TO HAVE...), "Qualifications" maybe same as requirements? Could combine. But we need to avoid duplication. Could have "Qualifications" for degree etc. "Skills" maybe for technical skills. "Benefits" for benefits package. "Pay" for compensation range. "Schedule" maybe not present; we should not include if not present. We need to preserve all substantive info; do not invent or summarize away detail. So we need to keep specifics like "5+ years of experience building and deploying machine learning and generative AI solutions in the cloud." etc. We need to format as HTML fragment only. Let's extract sections. First, intro paragraph: "At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Our Solution Engineering organization is seeking an AI Specialist who can provide hands-on expertise and support while working with technical decision makers and data scientists to design and architect AI solutions built on the Snowflake AI Data Cloud. This is a strategic role that works closely with cross-functional teams, including product, engineering, and the broader field organization to ensure successful execution and customer adoption of Snowflake’s AI & ML solutions." That could be "About the role". Then responsibilities: bullet list under "IN THIS ROLE YOU WILL GET TO:": - Be the technical expert in the room that positions Snowflake’s AI and ML features and value to technical stakeholders at Snowflake’s customers across the Americas. - Partner with Snowflake account team teams and customer champions to scope and drive POCs to success and technical wins that prove the value of Snowflake’s capabilities, including executive readouts and business value cases. - Collaborate with Snowflake’s product and engineering teams to influence Snowflake’s AI and ML roadmaps based on customer feedback. - Publish content that helps the team and company scale beyond your individual efforts, like blog posts, presentations at conferences, or technical collateral like notebooks and demos. - Influence, tailor and maintain Sales Engineering AI and ML selling assets, including customer presentations, demonstrations, and customer stories. We need to put these in
- .
Then "ON DAY ONE, WE WILL EXPECT YOU TO HAVE:" list:
- 5+ years of experience building and deploying machine learning and generative AI solutions in the cloud.
- Familiarity and associated knowledge of generative AI techniques like RAG, few shot learning, prompt engineering, or fine-tuning that are used to operationalize enterprise AI use cases like interactive chat applications or text processing.
- Deep knowledge of Python and common ML packages (such as LangChain, pandas, sklearn, and PyTorch) as well as data engineering tools and technologies like dbt, Airflow, and Spark.
- Strong presentation skills to both technical and executive audiences, whether whiteboarding sessions or formal readouts and demos.
- Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent experience preferred.
We could separate qualifications (degree) and skills (experience, knowledge). But we can put all under "Requirements". Or we could have "Qualifications" for degree and "Skills" for technical skills. However we must not create thin sections; better to combine into "Requirements" covering experience, knowledge, skills, degree.
Then "BONUS POINTS FOR EXPERIENCE WITH THE FOLLOWING:" list:
- Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
- Experience and understanding of large-scale infrastructure-as-a-service platforms (e.g. AWS, Microsoft Azure, GCP, etc.)
- 1+ years of practical Snowflake experience.
- Knowledge of and experience with large-scale database technology (e.g. Snowflake, Netezza, Exadata, Teradata, Greenplum, etc.)
These could be under "Preferred Qualifications" or "Additional Skills". We could have a section "Preferred Qualifications". But the instruction only gave examples of headings; we can use others if needed? It says use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings, but suggests those. Probably better to stick to those. We could map bonus points to "Skills" or "Qualifications". But we already used Requirements for mandatory. Could have "Preferred Qualifications" as a heading but not in list; maybe okay but risk. Safer to put under "Skills" (since they are skills/experience). Or under "Qualifications". Let's decide: Use "Skills" for both mandatory and bonus? But we already have Requirements for mandatory experience. Could have "Skills" for technical skills (Python, ML packages, presentation). Bonus points could be under "Preferred Qualifications". However the instruction didn't list Preferred Qualifications as example but we could still use it; but better to follow examples. Could incorporate bonus points into "Skills" as additional skills. Thus sections: - About the role (intro paragraph) - Responsibilities (list) - Requirements (list of mandatory expectations) - Skills (list of technical skills: deep knowledge of Python and ML packages, presentation skills, plus bonus points) - Benefits (list of benefits) - Pay (compensation range) We need to ensure we don't create sections with filler. All have content. Now we need to extract benefits paragraph: "This role is also eligible for a competitive benefits package that includes: medical, dental, vision, life, and disability insurance; 401(k) retirement plan; flexible spending & health savings account; at least 12 paid holidays; paid time off; parental leave; employee assistance program; and other company benefits." We need to put as
- each item.
Pay: "This role is eligible to participate in Snowflake's commission plan and it is common for employees in this role to receive total on-target earnings of $220,000 - $288,750. The estimated base salary for this role is $165,000 - $216,562.Additionally, this role is eligible to participate in Snowflake’s equity plan."
We need to preserve that info. Could put as
with that text. We should not include the line about "To comply with pay transparency requirements..." etc. Remove that. Also remove the line about "For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com". Remove. Also remove any duplicated lines. Now produce HTML. Structure:
About the role
...
Responsibilities
- ... ...
Requirements
- ... ...
Skills
- Deep knowledge of Python and common ML packages (such as LangChain, pandas, sklearn, and PyTorch) as well as data engineering tools and technologies like dbt, Airflow, and Spark.
- Strong presentation skills to both technical and executive audiences, whether whiteboarding sessions or formal readouts and demos.
- Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
- Experience and understanding of large-scale infrastructure-as-a-service platforms (e.g. AWS, Microsoft Azure, GCP, etc.)
- 1+ years of practical Snowflake experience.
- Knowledge of and experience with large-scale database technology (e.g. Snowflake, Netezza, Exadata, Teradata, Greenplum, etc.)
- ):
- 5+ years of experience building and deploying machine learning and generative AI solutions in the cloud.
- Familiarity and associated knowledge of generative AI techniques like RAG, few shot learning, prompt engineering, or fine-tuning that are used to operationalize enterprise AI use cases like interactive chat applications or text processing.
- Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent experience preferred.
Skills (
- ):
- Deep knowledge of Python and common ML packages (such as LangChain, pandas, sklearn, and PyTorch) as well as data engineering tools and technologies like dbt, Airflow, and Spark.
- Strong presentation skills to both technical and executive audiences, whether whiteboarding sessions or formal readouts and demos.
- Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
- Experience and understanding of large-scale infrastructure-as-a-service platforms (e.g. AWS, Microsoft Azure, GCP, etc.)
- 1+ years of practical Snowflake experience.
- Knowledge of and experience with large-scale database technology (e.g. Snowflake, Netezza, Exadata, Teradata, Greenplum, etc.)
Benefits (
- ):
- medical, dental, vision, life, and disability insurance
- 401(k) retirement plan
- flexible spending & health savings account
-
- each item.
Pay: "This role is eligible to participate in Snowflake's commission plan and it is common for employees in this role to receive total on-target earnings of $220,000 - $288,750. The estimated base salary for this role is $165,000 - $216,562.Additionally, this role is eligible to participate in Snowflake’s equity plan."
We need to preserve that info. Could put as