AI & Cloud Engineering

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• Python — primary language

• AWS — SageMaker, Lambda, S3, EC2, Bedrock, Step Functions, API Gateway, CloudFormation / CDK

• LLM frameworks — LangChain, OpenAI API, Anthropic API, Hugging Face

• SQL and NoSQL databases

• Vector databases for semantic search and RAG

• Git / GitHub — version control and CI/CD

• Docker / Kubernetes — containerization and orchestration

• HIPAA Privacy and Security Rule compliance

• SOC 2 Trust Service Criteria

• AWS security services — IAM, KMS, CloudTrail, GuardDuty, Security Hub

Remote Raven