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AI & Cloud Engineering at Remote Raven. 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 . This is a 100% Remote Job. Full time. Rate is $10/hr. Company Location: Kenya.