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
Requirements
This is a 100% Remote Job
Full time
Rate is $10/hr
Originally posted on