Position Overview  We are seeking a highly skilled and compliance-minded AI Specialist to design, build, and deploy artificial intelligence and machine learning solutions that drive automation, operational efficiency, and intelligent decision-making across the organization. This role sits at the intersection of AI engineering, cloud infrastructure, and data security — requiring someone who can build powerful AI systems while operating within strict compliance frameworks including HIPAA and SOC 2.    The ideal candidate is a strong programmer with hands-on AI/ML development experience, deep familiarity with AWS cloud services, and a genuine understanding of what it means to build and deploy AI in regulated, security-sensitive environments. This is not a theoretical role — you will be building, integrating, and shipping.    Key Responsibilities  AI & Machine Learning Development  - Design, develop, and deploy AI and machine learning models to solve real business problems and automate workflows  - Build and maintain end-to-end ML pipelines from data ingestion and preprocessing through model training, evaluation, and production deployment  - Develop natural language processing (NLP), large language model (LLM) integrations, and generative AI solutions as applicable  - Fine-tune and optimize pre-trained models (including GPT, Claude, or open-source alternatives) for specific use cases  - Evaluate model performance, monitor for drift, and implement improvements based on real-world feedback  - Research and apply emerging AI techniques, frameworks, and tools to continuously improve solution quality    AI Integration & Automation  - Integrate AI models and APIs into existing applications, platforms, and workflows  - Build intelligent automation solutions that reduce manual effort and improve operational throughput  - Develop AI-powered features including chatbots, recommendation engines, document processing, and predictive analytics  - Design and implement RAG (Retrieval-Augmented Generation) architectures for knowledge-based AI applications  - Collaborate with product and operations teams to identify high-value AI use cases and deliver solutions    Programming & Software Engineering  - Write clean, well-documented, production-quality code primarily in Python, with additional languages as needed (JavaScript, SQL, Bash, etc.)  - Build APIs, microservices, and data pipelines that support AI workloads at scale  - Apply software engineering best practices including version control (Git), code review, testing, and CI/CD  - Maintain and refactor existing codebases for performance, reliability, and maintainability  - Document technical architectures, implementation decisions, and system behaviors clearly    AWS Cloud Infrastructure  - Architect, deploy, and manage AI and data workloads on AWS cloud infrastructure  - Utilize AWS services including SageMaker, Lambda, EC2, S3, RDS, Bedrock, Step Functions, and API Gateway  - Build scalable, cost-efficient cloud architectures that support model training, inference, and data processing  - Implement infrastructure-as-code using AWS CloudFormation, CDK, or Terraform  - Monitor cloud resource utilization and optimize for performance and cost  - Ensure all AWS environments are configured in alignment with security and compliance requirements    HIPAA Compliance  - Design and develop all AI systems and data pipelines in full compliance with HIPAA Privacy and Security Rules  - Ensure Protected Health Information (PHI) is handled, stored, transmitted, and processed with appropriate safeguards  - Implement technical controls including encryption at rest and in transit, access controls, and audit logging for all PHI-adjacent systems  - Participate in HIPAA risk assessments and support remediation of identified vulnerabilities  - Maintain documentation required for HIPAA compliance including data flow diagrams, system inventories, and access logs  - Stay current on HIPAA regulatory developments and ensure AI systems remain compliant as regulations evolve    SOC 2 Compliance  - Build and maintain AI systems and cloud infrastructure in accordance with SOC 2 Trust Service Criteria (Security, Availability, Confidentiality, Processing Integrity, and Privacy)  - Implement and maintain security controls required for SOC 2 Type I and Type II certification  - Support audit preparation by maintaining evidence, access logs, and system documentation  - Participate in vulnerability management, penetration testing, and incident response processes  - Collaborate with security and compliance teams to ensure all AI deployments meet SOC 2 standards  - Monitor systems continuously for security events and compliance gaps    Data Management & Security  - Design secure data architectures that protect sensitive information throughout the AI pipeline  - Implement role-based access controls, data masking, and anonymization techniques where appropriate  - Ensure data governance practices are followed for all datasets used in model training and inference  - Maintain data lineage documentation and audit trails for compliance and reproducibility    Collaboration & Documentation  - Work closely with engineering, product, operations, and compliance teams to align AI solutions with business needs and regulatory requirements  - Communicate complex technical concepts clearly to non-technical stakeholders  - Produce thorough technical documentation for all systems, models, and integrations  - Mentor junior team members on AI development practices and compliance standards    Required Qualifications  - 3 or more years of hands-on experience in AI, machine learning, or data science engineering roles  - Strong programming skills in Python — this is the primary development language for this role  - Demonstrated experience building and deploying ML models or AI-powered applications in production environments  - Proficiency with AWS cloud services — particularly those relevant to AI/ML workloads (SageMaker, Lambda, S3, EC2, Bedrock, or equivalent)  - Working knowledge of HIPAA requirements and experience building systems that handle PHI in compliance with applicable regulations  - Familiarity with SOC 2 compliance frameworks and the technical controls required to support certification  - Experience with LLMs, NLP, or generative AI frameworks such as LangChain, OpenAI API, Hugging Face, or similar  - Strong understanding of data security, encryption, access control, and audit logging best practices  - Excellent written and verbal communication skills, including the ability to document technical work clearly    Preferred Qualifications  - AWS certifications such as AWS Certified Machine Learning Specialty, AWS Solutions Architect, or AWS Security Specialty  - Experience with MLOps practices and tools including model versioning, monitoring, and automated retraining pipelines  - Familiarity with vector databases such as Pinecone, Weaviate, or pgvector for RAG implementations  - Experience in a HIPAA-covered entity or business associate environment  - Background in healthcare technology, health informatics, or digital health platforms  - Experience with additional programming languages such as JavaScript, TypeScript, Go, or Java  - Familiarity with containerization and orchestration tools including Docker and Kubernetes    Technical Stack  Core  - Python — primary language  - AWS — SageMaker, Lambda, S3, EC2, Bedrock, Step Functions, API Gateway, CloudFormation / CDK  - LLM frameworks — LangChain, OpenAI API, Anthropic API, Hugging Face    Data & Infrastructure  - SQL and NoSQL databases  - Vector databases for semantic search and RAG  - Git / GitHub — version control and CI/CD  - Docker / Kubernetes — containerization and orchestration    Compliance & Security  - HIPAA Privacy and Security Rule compliance  - SOC 2 Trust Service Criteria  - AWS security services — IAM, KMS, CloudTrail, GuardDuty, Security Hub

Position Overview  We are seeking a highly skilled and compliance-minded AI Specialist to design, build, and deploy artificial intelligence and machine learning solutions that drive automation, operational efficiency, and intelligent decision-making across the organization. This role sits at the intersection of AI engineering, cloud infrastructure, and data security — requiring someone who can build powerful AI systems while operating within strict compliance frameworks including HIPAA and SOC 2.    The ideal candidate is a strong programmer with hands-on AI/ML development experience, deep familiarity with AWS cloud services, and a genuine understanding of what it means to build and deploy AI in regulated, security-sensitive environments. This is not a theoretical role — you will be building, integrating, and shipping.    Key Responsibilities  AI & Machine Learning Development  - Design, develop, and deploy AI and machine learning models to solve real business problems and automate workflows  - Build and maintain end-to-end ML pipelines from data ingestion and preprocessing through model training, evaluation, and production deployment  - Develop natural language processing (NLP), large language model (LLM) integrations, and generative AI solutions as applicable  - Fine-tune and optimize pre-trained models (including GPT, Claude, or open-source alternatives) for specific use cases  - Evaluate model performance, monitor for drift, and implement improvements based on real-world feedback  - Research and apply emerging AI techniques, frameworks, and tools to continuously improve solution quality    AI Integration & Automation  - Integrate AI models and APIs into existing applications, platforms, and workflows  - Build intelligent automation solutions that reduce manual effort and improve operational throughput  - Develop AI-powered features including chatbots, recommendation engines, document processing, and predictive analytics  - Design and implement RAG (Retrieval-Augmented Generation) architectures for knowledge-based AI applications  - Collaborate with product and operations teams to identify high-value AI use cases and deliver solutions    Programming & Software Engineering  - Write clean, well-documented, production-quality code primarily in Python, with additional languages as needed (JavaScript, SQL, Bash, etc.)  - Build APIs, microservices, and data pipelines that support AI workloads at scale  - Apply software engineering best practices including version control (Git), code review, testing, and CI/CD  - Maintain and refactor existing codebases for performance, reliability, and maintainability  - Document technical architectures, implementation decisions, and system behaviors clearly    AWS Cloud Infrastructure  - Architect, deploy, and manage AI and data workloads on AWS cloud infrastructure  - Utilize AWS services including SageMaker, Lambda, EC2, S3, RDS, Bedrock, Step Functions, and API Gateway  - Build scalable, cost-efficient cloud architectures that support model training, inference, and data processing  - Implement infrastructure-as-code using AWS CloudFormation, CDK, or Terraform  - Monitor cloud resource utilization and optimize for performance and cost  - Ensure all AWS environments are configured in alignment with security and compliance requirements    HIPAA Compliance  - Design and develop all AI systems and data pipelines in full compliance with HIPAA Privacy and Security Rules  - Ensure Protected Health Information (PHI) is handled, stored, transmitted, and processed with appropriate safeguards  - Implement technical controls including encryption at rest and in transit, access controls, and audit logging for all PHI-adjacent systems  - Participate in HIPAA risk assessments and support remediation of identified vulnerabilities  - Maintain documentation required for HIPAA compliance including data flow diagrams, system inventories, and access logs  - Stay current on HIPAA regulatory developments and ensure AI systems remain compliant as regulations evolve    SOC 2 Compliance  - Build and maintain AI systems and cloud infrastructure in accordance with SOC 2 Trust Service Criteria (Security, Availability, Confidentiality, Processing Integrity, and Privacy)  - Implement and maintain security controls required for SOC 2 Type I and Type II certification  - Support audit preparation by maintaining evidence, access logs, and system documentation  - Participate in vulnerability management, penetration testing, and incident response processes  - Collaborate with security and compliance teams to ensure all AI deployments meet SOC 2 standards  - Monitor systems continuously for security events and compliance gaps    Data Management & Security  - Design secure data architectures that protect sensitive information throughout the AI pipeline  - Implement role-based access controls, data masking, and anonymization techniques where appropriate  - Ensure data governance practices are followed for all datasets used in model training and inference  - Maintain data lineage documentation and audit trails for compliance and reproducibility    Collaboration & Documentation  - Work closely with engineering, product, operations, and compliance teams to align AI solutions with business needs and regulatory requirements  - Communicate complex technical concepts clearly to non-technical stakeholders  - Produce thorough technical documentation for all systems, models, and integrations  - Mentor junior team members on AI development practices and compliance standards    Required Qualifications  - 3 or more years of hands-on experience in AI, machine learning, or data science engineering roles  - Strong programming skills in Python — this is the primary development language for this role  - Demonstrated experience building and deploying ML models or AI-powered applications in production environments  - Proficiency with AWS cloud services — particularly those relevant to AI/ML workloads (SageMaker, Lambda, S3, EC2, Bedrock, or equivalent)  - Working knowledge of HIPAA requirements and experience building systems that handle PHI in compliance with applicable regulations  - Familiarity with SOC 2 compliance frameworks and the technical controls required to support certification  - Experience with LLMs, NLP, or generative AI frameworks such as LangChain, OpenAI API, Hugging Face, or similar  - Strong understanding of data security, encryption, access control, and audit logging best practices  - Excellent written and verbal communication skills, including the ability to document technical work clearly    Preferred Qualifications  - AWS certifications such as AWS Certified Machine Learning Specialty, AWS Solutions Architect, or AWS Security Specialty  - Experience with MLOps practices and tools including model versioning, monitoring, and automated retraining pipelines  - Familiarity with vector databases such as Pinecone, Weaviate, or pgvector for RAG implementations  - Experience in a HIPAA-covered entity or business associate environment  - Background in healthcare technology, health informatics, or digital health platforms  - Experience with additional programming languages such as JavaScript, TypeScript, Go, or Java  - Familiarity with containerization and orchestration tools including Docker and Kubernetes    Technical Stack  Core  - Python — primary language  - AWS — SageMaker, Lambda, S3, EC2, Bedrock, Step Functions, API Gateway, CloudFormation / CDK  - LLM frameworks — LangChain, OpenAI API, Anthropic API, Hugging Face    Data & Infrastructure  - SQL and NoSQL databases  - Vector databases for semantic search and RAG  - Git / GitHub — version control and CI/CD  - Docker / Kubernetes — containerization and orchestration    Compliance & Security  - HIPAA Privacy and Security Rule compliance  - SOC 2 Trust Service Criteria  - AWS security services — IAM, KMS, CloudTrail, GuardDuty, Security Hub

Position Overview  We are seeking a highly skilled and compliance-minded AI Specialist to design, build, and deploy artificial intelligence and machine learning solutions that drive automation, operational efficiency, and intelligent decision-making across the organization. This role sits at the intersection of AI engineering, cloud infrastructure, and data security — requiring someone who can build powerful AI systems while operating within strict compliance frameworks including HIPAA and SOC 2.    The ideal candidate is a strong programmer with hands-on AI/ML development experience, deep familiarity with AWS cloud services, and a genuine understanding of what it means to build and deploy AI in regulated, security-sensitive environments. This is not a theoretical role — you will be building, integrating, and shipping.    Key Responsibilities  AI & Machine Learning Development  - Design, develop, and deploy AI and machine learning models to solve real business problems and automate workflows  - Build and maintain end-to-end ML pipelines from data ingestion and preprocessing through model training, evaluation, and production deployment  - Develop natural language processing (NLP), large language model (LLM) integrations, and generative AI solutions as applicable  - Fine-tune and optimize pre-trained models (including GPT, Claude, or open-source alternatives) for specific use cases  - Evaluate model performance, monitor for drift, and implement improvements based on real-world feedback  - Research and apply emerging AI techniques, frameworks, and tools to continuously improve solution quality    AI Integration & Automation  - Integrate AI models and APIs into existing applications, platforms, and workflows  - Build intelligent automation solutions that reduce manual effort and improve operational throughput  - Develop AI-powered features including chatbots, recommendation engines, document processing, and predictive analytics  - Design and implement RAG (Retrieval-Augmented Generation) architectures for knowledge-based AI applications  - Collaborate with product and operations teams to identify high-value AI use cases and deliver solutions    Programming & Software Engineering  - Write clean, well-documented, production-quality code primarily in Python, with additional languages as needed (JavaScript, SQL, Bash, etc.)  - Build APIs, microservices, and data pipelines that support AI workloads at scale  - Apply software engineering best practices including version control (Git), code review, testing, and CI/CD  - Maintain and refactor existing codebases for performance, reliability, and maintainability  - Document technical architectures, implementation decisions, and system behaviors clearly    AWS Cloud Infrastructure  - Architect, deploy, and manage AI and data workloads on AWS cloud infrastructure  - Utilize AWS services including SageMaker, Lambda, EC2, S3, RDS, Bedrock, Step Functions, and API Gateway  - Build scalable, cost-efficient cloud architectures that support model training, inference, and data processing  - Implement infrastructure-as-code using AWS CloudFormation, CDK, or Terraform  - Monitor cloud resource utilization and optimize for performance and cost  - Ensure all AWS environments are configured in alignment with security and compliance requirements    HIPAA Compliance  - Design and develop all AI systems and data pipelines in full compliance with HIPAA Privacy and Security Rules  - Ensure Protected Health Information (PHI) is handled, stored, transmitted, and processed with appropriate safeguards  - Implement technical controls including encryption at rest and in transit, access controls, and audit logging for all PHI-adjacent systems  - Participate in HIPAA risk assessments and support remediation of identified vulnerabilities  - Maintain documentation required for HIPAA compliance including data flow diagrams, system inventories, and access logs  - Stay current on HIPAA regulatory developments and ensure AI systems remain compliant as regulations evolve    SOC 2 Compliance  - Build and maintain AI systems and cloud infrastructure in accordance with SOC 2 Trust Service Criteria (Security, Availability, Confidentiality, Processing Integrity, and Privacy)  - Implement and maintain security controls required for SOC 2 Type I and Type II certification  - Support audit preparation by maintaining evidence, access logs, and system documentation  - Participate in vulnerability management, penetration testing, and incident response processes  - Collaborate with security and compliance teams to ensure all AI deployments meet SOC 2 standards  - Monitor systems continuously for security events and compliance gaps    Data Management & Security  - Design secure data architectures that protect sensitive information throughout the AI pipeline  - Implement role-based access controls, data masking, and anonymization techniques where appropriate  - Ensure data governance practices are followed for all datasets used in model training and inference  - Maintain data lineage documentation and audit trails for compliance and reproducibility    Collaboration & Documentation  - Work closely with engineering, product, operations, and compliance teams to align AI solutions with business needs and regulatory requirements  - Communicate complex technical concepts clearly to non-technical stakeholders  - Produce thorough technical documentation for all systems, models, and integrations  - Mentor junior team members on AI development practices and compliance standards    Required Qualifications  - 3 or more years of hands-on experience in AI, machine learning, or data science engineering roles  - Strong programming skills in Python — this is the primary development language for this role  - Demonstrated experience building and deploying ML models or AI-powered applications in production environments  - Proficiency with AWS cloud services — particularly those relevant to AI/ML workloads (SageMaker, Lambda, S3, EC2, Bedrock, or equivalent)  - Working knowledge of HIPAA requirements and experience building systems that handle PHI in compliance with applicable regulations  - Familiarity with SOC 2 compliance frameworks and the technical controls required to support certification  - Experience with LLMs, NLP, or generative AI frameworks such as LangChain, OpenAI API, Hugging Face, or similar  - Strong understanding of data security, encryption, access control, and audit logging best practices  - Excellent written and verbal communication skills, including the ability to document technical work clearly    Preferred Qualifications  - AWS certifications such as AWS Certified Machine Learning Specialty, AWS Solutions Architect, or AWS Security Specialty  - Experience with MLOps practices and tools including model versioning, monitoring, and automated retraining pipelines  - Familiarity with vector databases such as Pinecone, Weaviate, or pgvector for RAG implementations  - Experience in a HIPAA-covered entity or business associate environment  - Background in healthcare technology, health informatics, or digital health platforms  - Experience with additional programming languages such as JavaScript, TypeScript, Go, or Java  - Familiarity with containerization and orchestration tools including Docker and Kubernetes    Technical Stack  Core  - Python — primary language  - AWS — SageMaker, Lambda, S3, EC2, Bedrock, Step Functions, API Gateway, CloudFormation / CDK  - LLM frameworks — LangChain, OpenAI API, Anthropic API, Hugging Face    Data & Infrastructure  - SQL and NoSQL databases  - Vector databases for semantic search and RAG  - Git / GitHub — version control and CI/CD  - Docker / Kubernetes — containerization and orchestration    Compliance & Security  - HIPAA Privacy and Security Rule compliance  - SOC 2 Trust Service Criteria  - AWS security services — IAM, KMS, CloudTrail, GuardDuty, Security Hub

• Lead, mentor, and support a cross-functional team of engineers delivering growth-critical systems and features. • Collaborate daily with Product and Design to define scope, set priorities, and ensure seamless execution. • Keep projects moving forward — maintaining clear expectations, realistic estimates, and transparent communication with stakeholders. • Provide technical guidance and context to help engineers make sound architectural and implementation decisions. • Foster a culture of accountability, trust, and learning — helping the team grow their impact and autonomy. • Improve team processes, communication, and collaboration across time zones. • Partner with other engineering leaders to ensure consistency and best practices across the broader Growth organization. • Leverage AI tooling daily to boost personal and team productivity • Stay hands-on in the codebase, writing production-grade code and setting the bar for quality, reliability, and speed.