Senior AI / Data Engineer: Data Platforms, Machine Learning & Applied AI
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we need to decide which sections have real content.
We have:
- Intro paragraph about company and promise: could be "About the role" or maybe "About the company". Since they said "About the role" as example, we can use that for overview.
- Key Role description: "Work in a dynamic, fast-paced environment as a Senior AI / Data Engineer..." That's also role description. Could be part of About the role.
- Key Responsibilities: list.
- Basic Qualifications: list.
- Additional Qualifications: list.
- Compensation, Bonuses, And Other Incentives: includes salary range and bonus info. That's Pay section.
- No explicit Benefits or Schedule sections. There's mention: "This is a remote position with occasional travel." That's schedule/location info. Could be Schedule section? They said include Schedule only if posting actually states them. It does state remote with occasional travel, so we can include Schedule.
- Benefits: Not mentioned. No benefits details. So we should not include Benefits.
- Pay: we have salary range and bonus info. So include Pay.
- Skills: Not explicitly a separate section; qualifications cover skills. Could combine into Requirements/Qualifications. But we can have Requirements (Basic Qualifications) and Qualifications (Additional Qualifications). Or we could merge. The instruction: prefer a few substantive sections over many thin ones. We have many sections already: About the role, Responsibilities, Requirements (Basic), Qualifications (Additional), Pay, Schedule. That's six sections. Could be okay.
We must not repeat job title as heading. No Job ID.
We must remove boilerplate: EEO statement, legal, etc. Also remove "Read more here: www.intertwineassociates.com". Also remove "Intertwine Associates LLC – Equal Employment Opportunity Statement" and following paragraphs. Also remove "Compensation, Bonuses, And Other Incentives" heading? We'll keep Pay section but rephrase.
We need to preserve all substantive information. So we need to keep the salary range, bonus info, remote occasional travel, etc.
We need to use - for any list of duties/requirements/benefits. So Responsibilities list, Basic Qualifications list, Additional Qualifications list.
We need to use
for prose.
We must not invent or summarize away detail. Keep all details.
Let's extract content.
Intro: "Explore What Happens When Ideas Intertwine At Intertwine Associates, we live by a simple promise: We show up ready to work. We partner with organizations across government, management, strategy, and technology to deliver operational efficiency and measurable impact. Our focus is on practical solutions, tailored frameworks, and innovation that drives meaningful results. Join a team where expertise meets execution, and where your work truly matters."
That's about the company/role. We'll put under
About the role
with a containing that.
Key Role paragraph: "Work in a dynamic, fast-paced environment as a Senior AI / Data Engineer supporting federal agencies and other clients. Lead the design and delivery of the data platforms, machine learning systems, and AI applications that turn client data into decisions, and set the engineering standards other engineers follow. Strong client or customer engagement is required in this role. You will work directly with client program leadership, architects, data scientists, and governance teams, advise on technical direction, and represent Intertwine Associates and the client with professionalism, responsiveness, and sound judgment at every touchpoint. This is a remote position with occasional travel. Assignments are matched to client needs and to each candidate's expertise and eligibility."
We could incorporate this into About the role or separate. Since we already have About the role, we could add another paragraph. But maybe better to have About the role cover company overview, and then a separate section like "Position Overview". However instruction only lists certain headings; we can still use other headings? 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 as examples. Safer to stick to those. We could put the Key Role content under About the role as additional paragraph.
Thus About the role will have two paragraphs: first about company, second about role details.
Now Responsibilities: list items from "Key Responsibilities". We'll extract each bullet.
List:
- Architect and lead the delivery of enterprise data platforms and pipelines on Azure (Data Lake Storage, Azure SQL, Data Factory, Microsoft Fabric / Foundry) or equivalent cloud services.
- Set standards for ETL design, schema enforcement, data validation, quality checks, and lineage, and review other engineers' work against them.
- Design, build, and deploy machine learning and AI solutions, including applications built on large language models such as retrieval-augmented generation, document understanding, and agentic workflows, with evaluation and guardrails.
- Build secure API endpoints and microservices that make data and models available to analytics and ML platforms.
- Establish MLOps practices: experiment tracking, model registries, CI/CD, containers, monitoring, and cost and performance tuning.
- Apply data and AI governance for compliance, reproducibility, auditability, and responsible AI, including federal frameworks such as the NIST AI Risk Management Framework.
- Translate mission and business needs into technical roadmaps, estimates, and briefings for client leadership.
- Mentor engineers and data scientists and lead technical work across multidisciplinary teams.
Now Basic Qualifications list:
- 7+ years of experience building data pipelines, machine learning systems, or AI applications in production, including 2+ years leading technical work.
- Expert Python (including Pandas and NumPy) and advanced SQL, with experience integrating relational and unstructured sources.
- Deep hands-on experience with Azure data services, or equivalent depth in AWS or Google Cloud.
- Production experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn.
- Experience with data validation, schema enforcement, quality assurance, and governance in regulated environments.
- Experience designing APIs and microservices for analytics and ML platforms.
- Excellent written and verbal communication, including briefing senior, non-technical audiences.
- U.S. citizenship or lawful permanent residency may be required per contract.
- Ability to obtain a federal government Public Trust or security clearance where the assignment requires one.
Additional Qualifications list:
- Bachelor's or advanced degree in Computer Science, Data Science, Statistics, Engineering, or a related field, or equivalent professional experience.
- Experience building applications on large language models: retrieval-augmented generation, embeddings and vector databases, evaluation, and guardrails.
- Microsoft certifications such as Azure Data Engineer, Azure AI Engineer, Fabric Data Engineer, or Azure Solutions Architect, or equivalent AWS or Google Cloud certifications.
- Experience with Databricks, Snowflake, Microsoft Fabric, or similar modern data platforms at enterprise scale.
- Familiarity with FedRAMP, FISMA, and federal data security requirements.
- Experience supporting a federal agency, or health, scientific, or research data.
- An active federal Public Trust or security clearance.
Now Pay section: from "Compensation, Bonuses, And Other Incentives". We need to capture:
- Anticipated base salary range: $130,000-$175,000 per year.
- This range is a good-faith estimate at the time of posting and reflects the base salary the company reasonably expects to offer for this role.
- Actual base salary will be determined based on lawful, job-related factors, including education, experience, specialized expertise, credentials, geographic location, scope of responsibilities, internal equity, client and contract requirements, and funding availability.
- This position may be eligible for discretionary bonuses, signing or retention incentives, or other variable compensation, as stated in the applicable written offer or plan documents.
- Any bonus or incentive is not guaranteed and is subject to applicable plan terms, individual and company performance, client or contract requirements, funding availability, and applicable law.
- Bonuses and other incentives are separate from base salary and may be modified or discontinued.
- The salary range does not include potential bonuses, other incentive compensation, or benefits.
- Nothing in this posting creates an employment contract or guarantees any specific salary, bonus, incentive, benefit, or term of employment.
We'll put that in a
or multiple
under Pay heading.
Schedule: from "This is a remote position with occasional travel." Also maybe "Assignments are matched to client needs and to each candidate's expertise and eligibility." That's more about assignment matching. We'll include schedule: remote, occasional travel.
We'll create
Schedule
with a containing that sentence.
We must not include Benefits section.
Now ensure we don't repeat any boilerplate like EEO statement. We removed that.
Now output HTML fragment only.
Let's craft.
Structure:
About the role
Explore What Happens When Ideas Intertwine At Intertwine Associates, we live by a simple promise: We show up ready to work. We partner with organizations across government, management, strategy, and technology to deliver operational efficiency and measurable impact. Our focus is on practical solutions, tailored frameworks, and innovation that drives meaningful results. Join a team where expertise meets execution, and where your work truly matters.
Work in a dynamic, fast-paced environment as a Senior AI / Data Engineer supporting federal agencies and other clients. Lead the design and delivery of the data platforms, machine learning systems, and AI applications that turn client data into decisions, and set the engineering standards other engineers follow. Strong client or customer engagement is required in this role. You will work directly with client program leadership, architects, data scientists, and governance teams, advise on technical direction, and represent Intertwine Associates and the client with professionalism, responsiveness, and sound judgment at every touchpoint. This is a remote position with occasional travel. Assignments are matched to client needs and to each candidate's expertise and eligibility.
Responsibilities
- Architect and lead the delivery of enterprise data platforms and pipelines on Azure (Data Lake Storage, Azure SQL, Data Factory, Microsoft Fabric / Foundry) or equivalent cloud services.
- Set standards for ETL design, schema enforcement, data validation, quality checks, and lineage, and review other engineers' work against them.
- Design, build, and deploy machine learning and AI solutions, including applications built on large language models such as retrieval-augmented generation, document understanding, and agentic workflows, with evaluation and guardrails.
- Build secure API endpoints and microservices that make data and models available to analytics and ML platforms.
- Establish MLOps practices: experiment tracking, model registries, CI/CD, containers, monitoring, and cost and performance tuning.
- Apply data and AI governance for compliance, reproducibility, auditability, and responsible AI, including federal frameworks such as the NIST AI Risk Management Framework.
- Translate mission and business needs into technical roadmaps, estimates, and briefings for client leadership.
- Mentor engineers and data scientists and lead technical work across multidisciplinary teams.
Requirements
- 7+ years of experience building data pipelines, machine learning systems, or AI applications in production, including 2+ years leading technical work.
- Expert Python (including Pandas and NumPy) and advanced SQL, with experience integrating relational and unstructured sources.
- Deep hands-on experience with Azure data services, or equivalent depth in AWS or Google Cloud.
- Production experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn.
for prose. We must not invent or summarize away detail. Keep all details. Let's extract content. Intro: "Explore What Happens When Ideas Intertwine At Intertwine Associates, we live by a simple promise: We show up ready to work. We partner with organizations across government, management, strategy, and technology to deliver operational efficiency and measurable impact. Our focus is on practical solutions, tailored frameworks, and innovation that drives meaningful results. Join a team where expertise meets execution, and where your work truly matters." That's about the company/role. We'll put under
About the role
with acontaining that. Key Role paragraph: "Work in a dynamic, fast-paced environment as a Senior AI / Data Engineer supporting federal agencies and other clients. Lead the design and delivery of the data platforms, machine learning systems, and AI applications that turn client data into decisions, and set the engineering standards other engineers follow. Strong client or customer engagement is required in this role. You will work directly with client program leadership, architects, data scientists, and governance teams, advise on technical direction, and represent Intertwine Associates and the client with professionalism, responsiveness, and sound judgment at every touchpoint. This is a remote position with occasional travel. Assignments are matched to client needs and to each candidate's expertise and eligibility." We could incorporate this into About the role or separate. Since we already have About the role, we could add another paragraph. But maybe better to have About the role cover company overview, and then a separate section like "Position Overview". However instruction only lists certain headings; we can still use other headings? 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 as examples. Safer to stick to those. We could put the Key Role content under About the role as additional paragraph. Thus About the role will have two paragraphs: first about company, second about role details. Now Responsibilities: list items from "Key Responsibilities". We'll extract each bullet. List: - Architect and lead the delivery of enterprise data platforms and pipelines on Azure (Data Lake Storage, Azure SQL, Data Factory, Microsoft Fabric / Foundry) or equivalent cloud services. - Set standards for ETL design, schema enforcement, data validation, quality checks, and lineage, and review other engineers' work against them. - Design, build, and deploy machine learning and AI solutions, including applications built on large language models such as retrieval-augmented generation, document understanding, and agentic workflows, with evaluation and guardrails. - Build secure API endpoints and microservices that make data and models available to analytics and ML platforms. - Establish MLOps practices: experiment tracking, model registries, CI/CD, containers, monitoring, and cost and performance tuning. - Apply data and AI governance for compliance, reproducibility, auditability, and responsible AI, including federal frameworks such as the NIST AI Risk Management Framework. - Translate mission and business needs into technical roadmaps, estimates, and briefings for client leadership. - Mentor engineers and data scientists and lead technical work across multidisciplinary teams. Now Basic Qualifications list: - 7+ years of experience building data pipelines, machine learning systems, or AI applications in production, including 2+ years leading technical work. - Expert Python (including Pandas and NumPy) and advanced SQL, with experience integrating relational and unstructured sources. - Deep hands-on experience with Azure data services, or equivalent depth in AWS or Google Cloud. - Production experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn. - Experience with data validation, schema enforcement, quality assurance, and governance in regulated environments. - Experience designing APIs and microservices for analytics and ML platforms. - Excellent written and verbal communication, including briefing senior, non-technical audiences. - U.S. citizenship or lawful permanent residency may be required per contract. - Ability to obtain a federal government Public Trust or security clearance where the assignment requires one. Additional Qualifications list: - Bachelor's or advanced degree in Computer Science, Data Science, Statistics, Engineering, or a related field, or equivalent professional experience. - Experience building applications on large language models: retrieval-augmented generation, embeddings and vector databases, evaluation, and guardrails. - Microsoft certifications such as Azure Data Engineer, Azure AI Engineer, Fabric Data Engineer, or Azure Solutions Architect, or equivalent AWS or Google Cloud certifications. - Experience with Databricks, Snowflake, Microsoft Fabric, or similar modern data platforms at enterprise scale. - Familiarity with FedRAMP, FISMA, and federal data security requirements. - Experience supporting a federal agency, or health, scientific, or research data. - An active federal Public Trust or security clearance. Now Pay section: from "Compensation, Bonuses, And Other Incentives". We need to capture: - Anticipated base salary range: $130,000-$175,000 per year. - This range is a good-faith estimate at the time of posting and reflects the base salary the company reasonably expects to offer for this role. - Actual base salary will be determined based on lawful, job-related factors, including education, experience, specialized expertise, credentials, geographic location, scope of responsibilities, internal equity, client and contract requirements, and funding availability. - This position may be eligible for discretionary bonuses, signing or retention incentives, or other variable compensation, as stated in the applicable written offer or plan documents. - Any bonus or incentive is not guaranteed and is subject to applicable plan terms, individual and company performance, client or contract requirements, funding availability, and applicable law. - Bonuses and other incentives are separate from base salary and may be modified or discontinued. - The salary range does not include potential bonuses, other incentive compensation, or benefits. - Nothing in this posting creates an employment contract or guarantees any specific salary, bonus, incentive, benefit, or term of employment. We'll put that in a
or multiple
under Pay heading. Schedule: from "This is a remote position with occasional travel." Also maybe "Assignments are matched to client needs and to each candidate's expertise and eligibility." That's more about assignment matching. We'll include schedule: remote, occasional travel. We'll create
Schedule
with acontaining that sentence. We must not include Benefits section. Now ensure we don't repeat any boilerplate like EEO statement. We removed that. Now output HTML fragment only. Let's craft. Structure:
About the role
Explore What Happens When Ideas Intertwine At Intertwine Associates, we live by a simple promise: We show up ready to work. We partner with organizations across government, management, strategy, and technology to deliver operational efficiency and measurable impact. Our focus is on practical solutions, tailored frameworks, and innovation that drives meaningful results. Join a team where expertise meets execution, and where your work truly matters.
Work in a dynamic, fast-paced environment as a Senior AI / Data Engineer supporting federal agencies and other clients. Lead the design and delivery of the data platforms, machine learning systems, and AI applications that turn client data into decisions, and set the engineering standards other engineers follow. Strong client or customer engagement is required in this role. You will work directly with client program leadership, architects, data scientists, and governance teams, advise on technical direction, and represent Intertwine Associates and the client with professionalism, responsiveness, and sound judgment at every touchpoint. This is a remote position with occasional travel. Assignments are matched to client needs and to each candidate's expertise and eligibility.
Responsibilities
- Architect and lead the delivery of enterprise data platforms and pipelines on Azure (Data Lake Storage, Azure SQL, Data Factory, Microsoft Fabric / Foundry) or equivalent cloud services.
- Set standards for ETL design, schema enforcement, data validation, quality checks, and lineage, and review other engineers' work against them.
- Design, build, and deploy machine learning and AI solutions, including applications built on large language models such as retrieval-augmented generation, document understanding, and agentic workflows, with evaluation and guardrails.
- Build secure API endpoints and microservices that make data and models available to analytics and ML platforms.
- Establish MLOps practices: experiment tracking, model registries, CI/CD, containers, monitoring, and cost and performance tuning.
- Apply data and AI governance for compliance, reproducibility, auditability, and responsible AI, including federal frameworks such as the NIST AI Risk Management Framework.
- Translate mission and business needs into technical roadmaps, estimates, and briefings for client leadership.
- Mentor engineers and data scientists and lead technical work across multidisciplinary teams.
Requirements
- 7+ years of experience building data pipelines, machine learning systems, or AI applications in production, including 2+ years leading technical work.
- Expert Python (including Pandas and NumPy) and advanced SQL, with experience integrating relational and unstructured sources.
- Deep hands-on experience with Azure data services, or equivalent depth in AWS or Google Cloud.
- Production experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn.