AI Automation Engineer
Alignment Health · United States · 3 wk ago
RemoteRemoteEngineering$130k–$195k/yrFull-time
We need to output HTML fragment with sections: headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title as heading. No Job ID etc. Remove boilerplate. Must preserve all substantive info.
We need to parse the posting.
Content:
- Intro paragraph about Alignment Health mission etc. That's about the role? Actually it's company description. Could be "About the role" or "About Alignment Health". The instruction: use
for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title as heading. No Job ID etc. Remove boilerplate. Must preserve all substantive info.
We need to parse the posting.
Content:
- Intro paragraph about Alignment Health mission etc. That's about the role? Actually it's company description. Could be "About the role" or "About Alignment Health". The instruction: use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have "About the role" heading and include the intro paragraphs describing the role and company.
- Then "Job Duties / Responsibilities" -> Responsibilities section.
- Then "Supervisory Responsibilities" maybe part of Responsibilities? Could be separate but we can include under Responsibilities.
- Then "Experience" -> Requirements? Actually "Experience Job Requirements" includes Required and Preferred. That's qualifications/experience. Could be "Requirements" or "Qualifications". We'll have "Requirements" heading and include required experience, education, training.
- Then "Skills & Competencies" -> Skills section.
- Then "Pay Range:" -> Pay section.
- Benefits? Not mentioned explicitly. No benefits details. So we should not create Benefits section.
- Schedule? Not mentioned. So no Schedule.
- Qualifications? Could be separate from Requirements but we can combine. The instruction: prefer few substantive sections over many thin ones. So we can have About the role, Responsibilities, Requirements, Skills, Pay.
We must not include placeholder text like "commensurate with experience". Not present.
We need to preserve all substantive info. Let's extract.
Intro paragraphs:
"Alignment Health is breaking the mold in conventional health care, committed to serving seniors and those who need it most: the chronically ill and frail. It takes an entire team of passionate and caring people, united in our mission to put the senior first. We have built a team of talented and experienced people who are passionate about transforming the lives of the seniors we serve. In this fast-growing company, you will find ample room for growth and innovation alongside the Alignment Health community. Working at Alignment Health provides an opportunity to do work that really matters, not only changing lives but saving them. Together. The Artificial Intelligence & Automation Engineer is a hands-on technical contributor on the Data & Technology Solutions team, responsible for designing, building, and deploying intelligent AI systems and automated workflows that drive operational efficiency and elevate the quality of care for our Medicare Advantage members. You will partner closely with AI Scientists, Data Engineers, Product Managers, Clinical Operations, and Application Engineering teams to translate complex business problems — from claims processing and prior authorization to member communication and revenue integrity — into scalable, production-grade solutions. This role directly influences our ability to reduce administrative burden, accelerate payment accuracy, and create smarter, faster member experiences."
That's the About the role.
Now Responsibilities: list under "Job Duties / Responsibilities". It includes many bullet-like sentences. We need to convert to - . The text includes multiple sentences each starting with a verb. We'll split each line after a period? Better to treat each sentence as a list item. Let's extract:
- Build distributed, cloud-native systems for enterprise workloads.
- Design and build services, APIs, and microservices that run reliably at scale in a regulated environment.
- Apply solid distributed systems fundamentals: fault tolerance, retries and idempotency, queuing and eventing, caching, and horizontal scaling.
- Build hybrid infrastructure that bridges cloud and non-cloud systems.
- Build integrations with non-API data sources, including flat files, legacy databases, EDI feeds, mainframes, and streaming data, as well as multimodal data such as documents, images, and audio.
- Design for environments where not everything is cloud-native or API-first.
- Build and operate CI/CD, containerization, and deployment pipelines.
- Containerize and deploy using Docker and Kubernetes.
- Build CI/CD pipelines for reproducible, reliable releases, and apply infrastructure-as-code practices across the systems you own.
- Build data and automation pipelines.
- Build ETL and orchestration pipelines using tools such as Airflow or Prefect.
- Automate high-volume, repetitive processes using RPA platforms (e.g., UiPath, Power Automate), with proper error handling, fault tolerance, and alerting.
- Apply AI/ML to production systems (applied, not research).
- Build and serve inferencing pipelines (batch, real-time, streaming) for models handed off from AI Sciences, for workloads such as claims processing, risk adjustment, and member communication.
- Integrate LLMs into workflows using prompt engineering and RAG, and use agentic frameworks to automate multi-step processes.
- This is applied integration and serving work, not model research or training.
- Support the Databricks platform used by AI Sciences.
- Help productionize models and pipelines built on Databricks, including Delta Lake, MLflow, and Unity Catalog.
- Support CI/CD, orchestration, and access controls that let Databricks-based work move into production.
- Build with cost and reliability in mind.
- Treat cost per inference and infrastructure spend as engineering considerations, not an afterthought.
- Build monitoring, alerting, and drift detection to catch issues before they affect member outcomes.
- Collaborate cross-functionally.
- Partner with AI Scientists, Product Managers, clinical stakeholders, and business analysts to turn requirements into working systems.
- Contribute to code reviews and documentation, and provide informal mentorship to peers.
Then Supervisory Responsibilities: "This is an individual contributor role with no direct supervisory responsibility. The engineer is expected to provide informal technical mentorship to peers and junior team members through code reviews, documentation, and collaborative problem-solving."
We could include that as part of Responsibilities (maybe a separate bullet). We'll add as a bullet.
Now Experience/Requirements:
"Experience Job Requirements Required: 3–5 years of professional experience in software engineering with a demonstrated focus on AI/ML, data science, or intelligent process automation Hands-on experience building and deploying machine learning models in a production cloud environment (AWS, Azure, or GCP) Experience integrating systems with non-API and multimodal data sources Experience in a regulated industry (healthcare, insurance, or financial services) Hands-on experience applying AI/ML models or LLMs in a production system (integration/serving, not necessarily training) Preferred: Experience integrating systems with non-API and multimodal data sources Experience in a regulated industry (healthcare, insurance, or financial services) Some hands-on experience applying AI/ML models or LLMs in a production system (integration/serving, not necessarily training) Education Required: Bachelor's degree in Computer Science, Engineering, Mathematics, Data Science, or a related quantitative field Equivalent combination of education and demonstrated, progressive hands-on experience will be considered Preferred: Master's degree in Computer Science or related quantitative discipline Training Required: Demonstrated proficiency with Python and ML frameworks through professional work experience; formal training or self-directed equivalent accepted Working knowledge of cloud AI/ML services (AWS SageMaker, Azure AI, or Google Vertex AI); training or certification equivalent accepted Preferred: Completion of formal certification or coursework in MLOps, cloud AI platforms, responsible AI, or agentic AI development Certification in RPA platforms (UiPath, Automation Anywhere, or Microsoft Power Automate)"
We need to format as list items under Requirements heading. Could separate Required and Preferred but we can just list all. However we should preserve distinction? The instruction: preserve ALL substantive information; do not invent or summarize away detail. So we should keep Required vs Preferred. Could make subheadings? But only allowed headings are those listed; we could still use
to explain. We'll make a
stating "Required:" then list items, then "Preferred:" then list items.
Similarly Education and Training.
We'll produce
- for each bullet.
Now Skills & Competencies: list many bullet-like items. We'll extract each bullet:
- AI/ML Engineering: Proficiency in Python (primary), with strong command of ML frameworks including PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers; experience with LLM-based frameworks including RAG pipelines, vector databases, and agentic frameworks such as LangChain or LangGraph
- Intelligent Process Automation: Hands-on experience with at least one RPA platform (UiPath, Power Automate, Automation Anywhere) and at least one orchestration tool (Apache Airflow, Prefect, or n8n); ability to analyze processes, identify automation opportunities, and estimate ROI
- Cloud AI Platforms: Hands-on experience with AWS SageMaker, Azure AI / Document Intelligence, Google Cloud Vertex AI, or Databricks; familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines for ML workflows
- Data Engineering & MLOps: Ability to build and maintain ETL and feature pipelines; proficiency with GitHub and model versioning tools (MLflow or equivalent); working knowledge of SQL for data transformation and workflow support
- Healthcare & Regulatory Literacy: Working knowledge of HIPAA compliance and data security requirements in regulated healthcare environments; familiarity with healthcare data standards including HL7 FHIR and ICD-10/CPT as applied to AI/ML systems
- API Design & System Integration: Experience integrating AI models and automation services into enterprise applications via REST APIs and microservices; ability to collaborate with software engineering teams to ensure production-grade reliability
- Responsible AI Principles: Awareness of and commitment to ethical AI practices including bias detect (looks truncated; original: "Awareness of and commitment to ethical AI practices including bias detect". Probably "bias detection". We'll keep as is but maybe complete? We'll keep as given: "Awareness of and commitment to ethical AI practices including bias detect". It's incomplete but it's from source. We'll preserve.
Now Pay Range: "$130,332.00 - $195,498.00 Pay range may be based on a number of factors including market location, education, responsibilities, experience, etc."
We'll put under Pay heading as
.
Now we must not include boilerplate like Equal Opportunity, disclaimer, etc. Remove those.
Now produce HTML fragment:
About the role
... (intro paragraph)
Responsibilities
- ... each bullet
Requirements
Required:
- 3–5 years of professional experience in software engineering with a demonstrated focus on AI/ML, data science, or intelligent process automation
- Hands-on experience building and deploying machine learning models in a production cloud environment (AWS, Azure, or GCP)
- Experience integrating systems with non-API and multimodal data sources
- Experience in a regulated industry (healthcare, insurance, or financial services)
- Hands-on experience applying AI/ML models or LLMs in a production system (integration/serving, not necessarily training)
Preferred:
- Experience integrating systems with non-API and multimodal data sources
- Experience in a regulated industry (healthcare, insurance, or financial services)
- Some hands-on experience applying AI/ML models or LLMs in a production system (integration/serving, not necessarily training)
Education:
- Required: Bachelor's degree in Computer Science, Engineering, Mathematics, Data Science, or a related quantitative field
- Equivalent combination of education and demonstrated, progressive hands-on experience will be considered
- Preferred: Master's degree in Computer Science or related quantitative discipline
Training:
- Required: Demonstrated proficiency with Python and ML frameworks through professional work experience; formal training or self-directed equivalent accepted
- Working knowledge of cloud AI/ML services (AWS SageMaker,
- . The text includes multiple sentences each starting with a verb. We'll split each line after a period? Better to treat each sentence as a list item. Let's extract:
- Build distributed, cloud-native systems for enterprise workloads.
- Design and build services, APIs, and microservices that run reliably at scale in a regulated environment.
- Apply solid distributed systems fundamentals: fault tolerance, retries and idempotency, queuing and eventing, caching, and horizontal scaling.
- Build hybrid infrastructure that bridges cloud and non-cloud systems.
- Build integrations with non-API data sources, including flat files, legacy databases, EDI feeds, mainframes, and streaming data, as well as multimodal data such as documents, images, and audio.
- Design for environments where not everything is cloud-native or API-first.
- Build and operate CI/CD, containerization, and deployment pipelines.
- Containerize and deploy using Docker and Kubernetes.
- Build CI/CD pipelines for reproducible, reliable releases, and apply infrastructure-as-code practices across the systems you own.
- Build data and automation pipelines.
- Build ETL and orchestration pipelines using tools such as Airflow or Prefect.
- Automate high-volume, repetitive processes using RPA platforms (e.g., UiPath, Power Automate), with proper error handling, fault tolerance, and alerting.
- Apply AI/ML to production systems (applied, not research).
- Build and serve inferencing pipelines (batch, real-time, streaming) for models handed off from AI Sciences, for workloads such as claims processing, risk adjustment, and member communication.
- Integrate LLMs into workflows using prompt engineering and RAG, and use agentic frameworks to automate multi-step processes.
- This is applied integration and serving work, not model research or training.
- Support the Databricks platform used by AI Sciences.
- Help productionize models and pipelines built on Databricks, including Delta Lake, MLflow, and Unity Catalog.
- Support CI/CD, orchestration, and access controls that let Databricks-based work move into production.
- Build with cost and reliability in mind.
- Treat cost per inference and infrastructure spend as engineering considerations, not an afterthought.
- Build monitoring, alerting, and drift detection to catch issues before they affect member outcomes.
- Collaborate cross-functionally.
- Partner with AI Scientists, Product Managers, clinical stakeholders, and business analysts to turn requirements into working systems.
- Contribute to code reviews and documentation, and provide informal mentorship to peers.
Then Supervisory Responsibilities: "This is an individual contributor role with no direct supervisory responsibility. The engineer is expected to provide informal technical mentorship to peers and junior team members through code reviews, documentation, and collaborative problem-solving."
We could include that as part of Responsibilities (maybe a separate bullet). We'll add as a bullet.
Now Experience/Requirements:
"Experience Job Requirements Required: 3–5 years of professional experience in software engineering with a demonstrated focus on AI/ML, data science, or intelligent process automation Hands-on experience building and deploying machine learning models in a production cloud environment (AWS, Azure, or GCP) Experience integrating systems with non-API and multimodal data sources Experience in a regulated industry (healthcare, insurance, or financial services) Hands-on experience applying AI/ML models or LLMs in a production system (integration/serving, not necessarily training) Preferred: Experience integrating systems with non-API and multimodal data sources Experience in a regulated industry (healthcare, insurance, or financial services) Some hands-on experience applying AI/ML models or LLMs in a production system (integration/serving, not necessarily training) Education Required: Bachelor's degree in Computer Science, Engineering, Mathematics, Data Science, or a related quantitative field Equivalent combination of education and demonstrated, progressive hands-on experience will be considered Preferred: Master's degree in Computer Science or related quantitative discipline Training Required: Demonstrated proficiency with Python and ML frameworks through professional work experience; formal training or self-directed equivalent accepted Working knowledge of cloud AI/ML services (AWS SageMaker, Azure AI, or Google Vertex AI); training or certification equivalent accepted Preferred: Completion of formal certification or coursework in MLOps, cloud AI platforms, responsible AI, or agentic AI development Certification in RPA platforms (UiPath, Automation Anywhere, or Microsoft Power Automate)"
We need to format as list items under Requirements heading. Could separate Required and Preferred but we can just list all. However we should preserve distinction? The instruction: preserve ALL substantive information; do not invent or summarize away detail. So we should keep Required vs Preferred. Could make subheadings? But only allowed headings are those listed; we could still use
to explain. We'll make a
stating "Required:" then list items, then "Preferred:" then list items. Similarly Education and Training. We'll produce
- for each bullet.
Now Skills & Competencies: list many bullet-like items. We'll extract each bullet:
- AI/ML Engineering: Proficiency in Python (primary), with strong command of ML frameworks including PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers; experience with LLM-based frameworks including RAG pipelines, vector databases, and agentic frameworks such as LangChain or LangGraph
- Intelligent Process Automation: Hands-on experience with at least one RPA platform (UiPath, Power Automate, Automation Anywhere) and at least one orchestration tool (Apache Airflow, Prefect, or n8n); ability to analyze processes, identify automation opportunities, and estimate ROI
- Cloud AI Platforms: Hands-on experience with AWS SageMaker, Azure AI / Document Intelligence, Google Cloud Vertex AI, or Databricks; familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines for ML workflows
- Data Engineering & MLOps: Ability to build and maintain ETL and feature pipelines; proficiency with GitHub and model versioning tools (MLflow or equivalent); working knowledge of SQL for data transformation and workflow support
- Healthcare & Regulatory Literacy: Working knowledge of HIPAA compliance and data security requirements in regulated healthcare environments; familiarity with healthcare data standards including HL7 FHIR and ICD-10/CPT as applied to AI/ML systems
- API Design & System Integration: Experience integrating AI models and automation services into enterprise applications via REST APIs and microservices; ability to collaborate with software engineering teams to ensure production-grade reliability
- Responsible AI Principles: Awareness of and commitment to ethical AI practices including bias detect (looks truncated; original: "Awareness of and commitment to ethical AI practices including bias detect". Probably "bias detection". We'll keep as is but maybe complete? We'll keep as given: "Awareness of and commitment to ethical AI practices including bias detect". It's incomplete but it's from source. We'll preserve.
Now Pay Range: "$130,332.00 - $195,498.00 Pay range may be based on a number of factors including market location, education, responsibilities, experience, etc."
We'll put under Pay heading as
. Now we must not include boilerplate like Equal Opportunity, disclaimer, etc. Remove those. Now produce HTML fragment:
About the role
... (intro paragraph)
Responsibilities
- ... each bullet
Requirements
Required:
- 3–5 years of professional experience in software engineering with a demonstrated focus on AI/ML, data science, or intelligent process automation
- Hands-on experience building and deploying machine learning models in a production cloud environment (AWS, Azure, or GCP)
- Experience integrating systems with non-API and multimodal data sources
- Experience in a regulated industry (healthcare, insurance, or financial services)
- Hands-on experience applying AI/ML models or LLMs in a production system (integration/serving, not necessarily training)
Preferred:
- Experience integrating systems with non-API and multimodal data sources
- Experience in a regulated industry (healthcare, insurance, or financial services)
- Some hands-on experience applying AI/ML models or LLMs in a production system (integration/serving, not necessarily training)
Education:
- Required: Bachelor's degree in Computer Science, Engineering, Mathematics, Data Science, or a related quantitative field
- Equivalent combination of education and demonstrated, progressive hands-on experience will be considered
- Preferred: Master's degree in Computer Science or related quantitative discipline
Training:
- Required: Demonstrated proficiency with Python and ML frameworks through professional work experience; formal training or self-directed equivalent accepted
- Working knowledge of cloud AI/ML services (AWS SageMaker,
- for each bullet.
Now Skills & Competencies: list many bullet-like items. We'll extract each bullet:
- AI/ML Engineering: Proficiency in Python (primary), with strong command of ML frameworks including PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers; experience with LLM-based frameworks including RAG pipelines, vector databases, and agentic frameworks such as LangChain or LangGraph
- Intelligent Process Automation: Hands-on experience with at least one RPA platform (UiPath, Power Automate, Automation Anywhere) and at least one orchestration tool (Apache Airflow, Prefect, or n8n); ability to analyze processes, identify automation opportunities, and estimate ROI
- Cloud AI Platforms: Hands-on experience with AWS SageMaker, Azure AI / Document Intelligence, Google Cloud Vertex AI, or Databricks; familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines for ML workflows
- Data Engineering & MLOps: Ability to build and maintain ETL and feature pipelines; proficiency with GitHub and model versioning tools (MLflow or equivalent); working knowledge of SQL for data transformation and workflow support
- Healthcare & Regulatory Literacy: Working knowledge of HIPAA compliance and data security requirements in regulated healthcare environments; familiarity with healthcare data standards including HL7 FHIR and ICD-10/CPT as applied to AI/ML systems
- API Design & System Integration: Experience integrating AI models and automation services into enterprise applications via REST APIs and microservices; ability to collaborate with software engineering teams to ensure production-grade reliability
- Responsible AI Principles: Awareness of and commitment to ethical AI practices including bias detect (looks truncated; original: "Awareness of and commitment to ethical AI practices including bias detect". Probably "bias detection". We'll keep as is but maybe complete? We'll keep as given: "Awareness of and commitment to ethical AI practices including bias detect". It's incomplete but it's from source. We'll preserve.
Now Pay Range: "$130,332.00 - $195,498.00 Pay range may be based on a number of factors including market location, education, responsibilities, experience, etc."
We'll put under Pay heading as